{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T16:54:23Z","timestamp":1744217663065,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-13-MONU-0001"],"award-info":[{"award-number":["ANR-13-MONU-0001"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Adv. Model. and Simul. in Eng. Sci."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Urban air quality simulation is an important tool to understand the impacts of air pollution. However, the simulations are often computationally expensive, and require extensive data on pollutant sources. Data on road traffic pollution, often the predominant source, can be obtained through sparse measurements, or through simulation of traffic and emissions. Modeling chains combine the simulations of multiple models to provide the most accurate representation possible, however the need to solve multiple models for each simulation increases computational costs even more. In this paper we construct a meta-modeling chain for urban atmospheric pollution, from dynamic traffic modeling to air pollution modeling. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a non-intrusive reduced order scheme, and study its application to a full chain of operational models. Reduced basis are constructed using principal component analysis (PCA), and the concentration fields are approximated as projections onto this reduced space. We use statistical emulation to approximate projection coefficients in a non-intrusive manner. We apply a multi-level meta-modeling technique to a chain using the dynamic traffic assignment model LADTA, the emissions database COPERT IV, and the urban dispersion-reaction air quality model SIRANE to a case study on the city of Clermont-Ferrand with over 45,\u00a0000 daily traffic observations, a 47,\u00a0000-link road network, a simulation domain covering <jats:inline-formula><jats:alternatives><jats:tex-math>$$180\\,\\text {km}^2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>180<\/mml:mn>\n                    <mml:mspace\/>\n                    <mml:msup>\n                      <mml:mtext>km<\/mml:mtext>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. We assess the results using hourly NO<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> concentration observations measured at stations in the agglomeration. Computational times are reduced from nearly 3 h per simulation to under 0.1 s, while maintaining accuracy comparable to the original models. The low cost of the meta-model chain and its non-intrusive character demonstrate the versatility of the method, and the utility for long-term or many-query air quality studies such as epidemiological inquiry or uncertainty quantification.<\/jats:p>","DOI":"10.1186\/s40323-020-00173-2","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T11:12:17Z","timestamp":1599736337000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Meta-modeling of a simulation chain for urban air quality"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2113-306X","authenticated-orcid":false,"given":"J. K.","family":"Hammond","sequence":"first","affiliation":[]},{"given":"R.","family":"Chen","sequence":"additional","affiliation":[]},{"given":"V.","family":"Mallet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"173_CR1","doi-asserted-by":"crossref","unstructured":"World-Health-Organization. Ambient air pollution: a global assessment of exposure and burden of disease. Tech rep. 2016. http:\/\/www.who.int\/phe\/publications\/air-pollution-global-assessment\/en\/.","DOI":"10.17159\/2410-972X\/2016\/v26n2a4"},{"issue":"2","key":"173_CR2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10546-008-9266-1","volume":"127","author":"M Milliez","year":"2008","unstructured":"Milliez M, Carissimo B. Computational fluid dynamical modelling of concentration fluctuations in an idealized urban area. Boundary-Layer Meteorol. 2008;127(2):241\u201359. https:\/\/doi.org\/10.1007\/s10546-008-9266-1.","journal-title":"Boundary-Layer Meteorol"},{"key":"173_CR3","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.atmosenv.2013.07.028","volume":"79","author":"Y Tominaga","year":"2013","unstructured":"Tominaga Y, Stathopoulos T. CFD simulation of near-field pollutant dispersion in the urban environment: a review of current modeling techniques. Atmos Environ. 2013;79:716\u201330. https:\/\/doi.org\/10.1016\/j.atmosenv.2013.07.028.","journal-title":"Atmos Environ"},{"key":"173_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.atmosenv.2018.04.009","volume":"184","author":"V Mallet","year":"2018","unstructured":"Mallet V, Tilloy A, Poulet D, Girard S, Brocheton F. Meta-modeling of ADMS-Urban by dimension reduction and emulation. Atmos Environ. 2018;184:37\u201346. https:\/\/doi.org\/10.1016\/j.atmosenv.2018.04.009.","journal-title":"Atmos Environ"},{"key":"173_CR5","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-1-4757-9128-0_48","volume-title":"Air pollution modeling and its application XII","author":"DJ Carruthers","year":"1998","unstructured":"Carruthers DJ, Edmunds HA, McHugh CA, Singles RJ. Development of ADMS-urban and comparison with data for urban areas in the UK. In: Gryning SE, Chaumerliac N, editors. Air pollution modeling and its application XII. Berlin: Springer; 1998. p. 467\u201375."},{"issue":"23","key":"173_CR6","doi-asserted-by":"publisher","first-page":"12253","DOI":"10.5194\/acp-11-12253-2011","volume":"11","author":"LA Lee","year":"2011","unstructured":"Lee LA, Carslaw KS, Pringle KJ, Mann GW, Spracklen DV. Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters. Atmos Chem Phys. 2011;11(23):12253\u201373. https:\/\/doi.org\/10.5194\/acp-11-12253-2011.","journal-title":"Atmos Chem Phys"},{"key":"173_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.atmosenv.2014.07.022","volume":"96","author":"P Armand","year":"2014","unstructured":"Armand P, Brocheton F, Poulet D, Vendel F, Dubourg V, Yalamas T. Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere. Atmos Environ. 2014;96:1\u201310. https:\/\/doi.org\/10.1016\/j.atmosenv.2014.07.022.","journal-title":"Atmos Environ"},{"issue":"7","key":"173_CR8","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.1002\/2015JD023993","volume":"121","author":"S Girard","year":"2016","unstructured":"Girard S, Mallet V, Korsakissok I, Mathieu A. Emulation and Sobol\u2019 sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident. J Geophys Res Atmos. 2016;121(7):3484\u201396.","journal-title":"J Geophys Res Atmos"},{"key":"173_CR9","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.envsoft.2014.11.020","volume":"64","author":"M Fallah Shorshani","year":"2015","unstructured":"Fallah Shorshani M, Andr\u00e9 M, Bonhomme C, Seigneur C. Modelling chain for the effect of road traffic on air and water quality: techniques, current status and future prospects. Environ Modell Softw. 2015;64:102\u201323. https:\/\/doi.org\/10.1016\/j.envsoft.2014.11.020.","journal-title":"Environ Modell Softw"},{"issue":"12","key":"173_CR10","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1016\/S1352-2310(99)00468-9","volume":"34","author":"A Russell","year":"2000","unstructured":"Russell A, Dennis R. NARSTO critical review of photochemical models and modeling. Atmos Environ. 2000;34(12):2283\u2013324.","journal-title":"Atmos Environ"},{"key":"173_CR11","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.atmosenv.2012.02.041","volume":"60","author":"Y Zhang","year":"2012","unstructured":"Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A. Real-time air quality forecasting, part II: state of the science, current research needs, and future prospects. Atmos Environ. 2012;60:656\u201376. https:\/\/doi.org\/10.1016\/j.atmosenv.2012.02.041.","journal-title":"Atmos Environ"},{"key":"173_CR12","unstructured":"Leurent F. On network assignment and demand-supply equilibrium: an analysis framework and a simple dynamic model. In: Proceedings of the European transport conference (ETC) 2003 held 8\u201310 October 2003, STRASBOURG, FRANCE. 2003."},{"issue":"1","key":"173_CR13","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3141\/2132-14","volume":"2132","author":"F Leurent","year":"2009","unstructured":"Leurent F, Aguil\u00e9ra V. Large problems of dynamic network assignment and traffic equilibrium: computational principles and application to Paris road network. Transp Res Rec. 2009;2132(1):122\u201332.","journal-title":"Transp Res Rec"},{"key":"173_CR14","unstructured":"Chen R, Mallet V. Pollemission software computing traffic emissions of atmospheric pollutants with copert-iv formulations. https:\/\/github.com\/pollemission."},{"key":"173_CR15","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/978-3-540-88351-7_37","volume-title":"Information technologies in environmental engineering, environmental science and engineering","author":"L Ntziachristos","year":"2009","unstructured":"Ntziachristos L, Gkatzoflias D, Kouridis C, Samaras Z. COPERT: a European road transport emission inventory model. In: Athanasiadis DIN, Rizzoli PAE, Mitkas PA, G\u00f3mez PD-IJM, editors. Information technologies in environmental engineering, environmental science and engineering. Berlin: Springer; 2009. p. 491\u2013504. https:\/\/doi.org\/10.1007\/978-3-540-88351-7_37."},{"issue":"39","key":"173_CR16","doi-asserted-by":"publisher","first-page":"7379","DOI":"10.1016\/j.atmosenv.2011.07.008","volume":"45","author":"L Soulhac","year":"2011","unstructured":"Soulhac L, Salizzoni P, Cierco F-X, Perkins R. The model SIRANE for atmospheric urban pollutant dispersion; part I, presentation of the model. Atmos Environ. 2011;45(39):7379\u201395. https:\/\/doi.org\/10.1016\/j.atmosenv.2011.07.008.","journal-title":"Atmos Environ"},{"key":"173_CR17","doi-asserted-by":"crossref","unstructured":"Prud\u2019homme C, Rovas DV, Veroy K, Machiels L, Maday Y, Patera AT, Turinici G. Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods. J Fluids Eng. 2002;124(1): 70\u201380. http:\/\/fluidsengineering.asmedigitalcollection.asme.org\/article.aspx?articleid=1429475.","DOI":"10.1115\/1.1448332"},{"key":"173_CR18","unstructured":"Chen R. Uncertainty quantification in the simulation of road traffic and associated atmospheric emissions in a metropolitan area. Thesis, Paris Est (May 2018). http:\/\/www.theses.fr\/2018PESC1029."},{"key":"173_CR19","unstructured":"CHAKIR R, Joly P, Maday Y, \u00a0Parnaudeau P. A non intrusive reduced basis method: application to computational fluid dynamics. In: 2nd ECCOMAS young investigators conference (YIC 2013), Bordeaux, France. 2013. https:\/\/hal.archives-ouvertes.fr\/hal-00855906."},{"key":"173_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cam.2017.12.044","volume":"337","author":"R Chakir","year":"2018","unstructured":"Chakir R, Hammond JK. A non-intrusive reduced basis method for elastoplasticity problems in geotechnics. J Comput Appl Math. 2018;337:1\u201317. https:\/\/doi.org\/10.1016\/j.cam.2017.12.044.","journal-title":"J Comput Appl Math"},{"key":"173_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apm.2019.05.012","volume":"76","author":"J Hammond","year":"2019","unstructured":"Hammond J, Chakir R, Bourquin F, Maday Y. PBDW: a non-intrusive Reduced Basis Data Assimilation method and its application to an urban dispersion modeling framework. Appl Math Modell. 2019;76:1\u201325. https:\/\/doi.org\/10.1016\/j.apm.2019.05.012.","journal-title":"Appl Math Modell"},{"key":"173_CR22","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.jcp.2019.01.031","volume":"384","author":"Q Wang","year":"2019","unstructured":"Wang Q, Hesthaven JS, Ray D. Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem. J Comput Phys. 2019;384:289\u2013307. https:\/\/doi.org\/10.1016\/j.jcp.2019.01.031.","journal-title":"J Comput Phys"},{"issue":"11","key":"173_CR23","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1016\/j.crme.2019.11.012","volume":"347","author":"N Demo","year":"2019","unstructured":"Demo N, Tezzele M, Rozza G. A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces. C R M\u00e9canique. 2019;347(11):873\u201381. https:\/\/doi.org\/10.1016\/j.crme.2019.11.012.","journal-title":"C R M\u00e9canique"},{"issue":"8","key":"173_CR24","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.2514\/1.2159","volume":"42","author":"T Bui-Thanh","year":"2004","unstructured":"Bui-Thanh T, Damodaran M, Willcox K. Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. AIAA J. 2004;42(8):1505\u201316. https:\/\/doi.org\/10.2514\/1.2159.","journal-title":"AIAA J"},{"key":"173_CR25","doi-asserted-by":"crossref","unstructured":"Quarteroni A, Manzoni A, Negri F. Reduced basis methods for partial differential equations: an introduction. Vol.\u00a092. Springer. 2015. https:\/\/books.google.fr\/books?hl=en&lr=&id=e6FnCgAAQBAJ&oi=fnd&pg=PP1&dq=quarteroni+manzoni+negri+reduced+basis+methods+for+partial+differential+equations+springer&ots=jVBOqponFX&sig=D_VExRlkwaJbPLh08oVWs7tyquU.","DOI":"10.1007\/978-3-319-15431-2_1"},{"key":"173_CR26","series-title":"Springerbriefs in mathematics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22470-1","volume-title":"Certified reduced basis methods for parametrized partial differential equations","author":"JS Hesthaven","year":"2016","unstructured":"Hesthaven JS, Rozza G, Stamm B. Certified reduced basis methods for parametrized partial differential equations., Springerbriefs in mathematicsBerlin: Springer International Publishing; 2016. https:\/\/doi.org\/10.1007\/978-3-319-22470-1."},{"key":"173_CR27","doi-asserted-by":"crossref","unstructured":"Kolmogoroff A. Uber die beste Annaherung von Funktionen einer gegebenen Funktionenklasse, Ann Math. 1936;37:107\u201310. http:\/\/www.jstor.org\/stable\/1968691.","DOI":"10.2307\/1968691"},{"key":"173_CR28","unstructured":"Chen R, Mallet V, Aguilera V, Cohn F, Poulet D. Metamodeling of a dynamic traffic assignment model at metropolitan scale, 43."},{"key":"173_CR29","unstructured":"Ruiwei C, Vivien M. Pollemission software computing traffic emissions of atmospheric pollutants with COPERT-IV formulations, original-date: 2016-01-21T17:19:00Z. 2016. https:\/\/github.com\/pollemission."},{"key":"173_CR30","volume-title":"COPERT 4: Computer programme to calculate emissions from road transport","author":"D Gkatzoflias","year":"2009","unstructured":"Gkatzoflias D, Kouridis C, Ntziachristos L, Samaras Z. COPERT 4: Computer programme to calculate emissions from road transport. Copenhagen: European Environment Agency; 2009."},{"key":"173_CR31","unstructured":"EEA. EMEP\/EEA air pollutant emission inventory guidebook\u2014Part B.1.A.3.b.iiv Road transport. 2016. https:\/\/www.eea.europa.eu\/publications\/emep-eea-guidebook-2016."},{"issue":"26","key":"173_CR32","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1016\/j.atmosenv.2005.06.023","volume":"39","author":"DC Carslaw","year":"2005","unstructured":"Carslaw DC. Evidence of an increasing NO2\/NOX emissions ratio from road traffic emissions. Atmos Environ. 2005;39(26):4793\u2013802. https:\/\/doi.org\/10.1016\/j.atmosenv.2005.06.023.","journal-title":"Atmos Environ"},{"key":"173_CR33","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.atmosenv.2012.02.028","volume":"54","author":"SD Beevers","year":"2012","unstructured":"Beevers SD, Westmoreland E, de Jong MC, Williams ML, Carslaw DC. Trends in NOx and NO2 emissions from road traffic in Great Britain. Atmos Environ. 2012;54:107\u201316. https:\/\/doi.org\/10.1016\/j.atmosenv.2012.02.028.","journal-title":"Atmos Environ"},{"issue":"1","key":"173_CR34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/2190-4715-24-21","volume":"24","author":"R Kurtenbach","year":"2012","unstructured":"Kurtenbach R, Kleffmann J, Niedojadlo A, Wiesen P. Primary NO2 emissions and their impact on air quality in traffic environments in Germany. Environ Sci Eur. 2012;24(1):21. https:\/\/doi.org\/10.1186\/2190-4715-24-21.","journal-title":"Environ Sci Eur"},{"issue":"6","key":"173_CR35","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1021\/es991320p","volume":"35","author":"JA Gillies","year":"2001","unstructured":"Gillies JA, Gertler AW, Sagebiel JC, Dippel WA. On-road particulate matter (PM2.5 and PM10) Emissions in the Sepulveda Tunnel, Los Angeles, California. Environ Sci Technol. 2001;35(6):1054\u201363. https:\/\/doi.org\/10.1021\/es991320p.","journal-title":"Environ Sci Technol"},{"issue":"38","key":"173_CR36","doi-asserted-by":"publisher","first-page":"6547","DOI":"10.1016\/j.atmosenv.2004.08.037","volume":"38","author":"X Querol","year":"2004","unstructured":"Querol X, Alastuey A, Ruiz CR, Arti\u00f1ano B, Hansson HC, Harrison RM, Buringh E, ten Brink HM, Lutz M, Bruckmann P, Straehl P, Schneider J. Speciation and origin of PM10 and PM2.5 in selected European cities. Atmos Environ. 2004;38(38):6547\u201355. https:\/\/doi.org\/10.1016\/j.atmosenv.2004.08.037.","journal-title":"Atmos Environ"},{"key":"173_CR37","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.atmosenv.2011.11.031","volume":"49","author":"L Soulhac","year":"2011","unstructured":"Soulhac L, Salizzoni P, Mejean P, Didier D, Rios I. The model SIRANE for atmospheric urban pollutant dispersion. PART II, validation of the model on a real case study. Atmos Environ. 2011;49:320\u201337. https:\/\/doi.org\/10.1016\/j.atmosenv.2011.11.031.","journal-title":"Atmos Environ"}],"container-title":["Advanced Modeling and Simulation in Engineering Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40323-020-00173-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40323-020-00173-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40323-020-00173-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T23:14:35Z","timestamp":1631056475000},"score":1,"resource":{"primary":{"URL":"https:\/\/amses-journal.springeropen.com\/articles\/10.1186\/s40323-020-00173-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,8]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["173"],"URL":"https:\/\/doi.org\/10.1186\/s40323-020-00173-2","relation":{},"ISSN":["2213-7467"],"issn-type":[{"type":"electronic","value":"2213-7467"}],"subject":[],"published":{"date-parts":[[2020,9,8]]},"assertion":[{"value":"20 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This research is supported by the French National Research Agency (ANR, the Agence Nationale de la Recherche), project ANR-13-MONU-0001, ESTIMAIR.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The City of Clermont-Ferrand provided traffic flow measurement data. SMTC (Syndicat Mixte des Transports en Commun de l\u2019agglom\u00e9ration clermontoise) provided the traffic network geometry for the agglomeration of Clermont-Ferrand and the static O-D matrix representing spatial traffic demand in the traffic assignment model. The SME NUMTECH provided data necessary for the model SIRANE, including geometrical features of the traffic network, meteorological data, emissions data, and background concentrations. Atmo Auvergne-Rhone-Alpes () provided pollutant concentration measurements used in the calculation of statistical scores.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37"}}