A Functional Land Use Regression Model for NO2 Concentration in the Italian Alpine Region

A Functional Land Use Regression Model for NO2 Concentration in the Italian Alpine Region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Functional Land Use Regression Model for NO2 Concentration in the Italian Alpine Region Paolo Girardi, Claudia Collarin, Ilaria Prosdocimi, Mauro Masiol This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7790380/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Air pollution is a key risk factor associated to adverse health outcomes. Although pollution levels are typically measured at limited locations where air monitoring stations are placed, it is important to have some knowledge of the spatial distribution of pollution to assess the exposure of (groups of) individuals. Furthermore, since pollution levels are influenced by local sources such as traffic, industrial activities, and land use, it is of interest to enrich the prediction of pollution levels using information on these explanatory variables. Land Use Regression (LUR) models have emerged as effective tools for this purpose, relating pollutant concentrations to geographical characteristics. However, traditional LUR models often focus on average concentrations, overlooking crucial intra-day variability that can impact health outcomes. To address this we introduce a novel functional LUR (FLUR) model designed to estimate hourly NO2 concentrations. This approach treats hourly pollutant measurements as functional data, capturing the continuous temporal dynamics of daily pollution levels. We applied this model to a comprehensive dataset of hourly NO2 measurements from 41 air monitoring stations in the Italian Alpine foothills and mountains collected throughout the year 2023. Our functional penalized regression model considers the hourly daily profile of log-transformed NO2 concentrations as dependent functional variable and a combination of scalar and functional variables as predictors. These variables may be related to meteorological information, key spatial predictors derived from GIS, or structural factors such as day of the week and day of the year.The choice of which predictors should be included in the predictive model was carried out through an innovative forward selection approach adapted to functional data. The approach is based on maximising the adjusted explained variance while avoiding concurvity in the selected predictors. This is achieved by taking into consideration the direction of the daily functional effect. Five key predictors influencing hourly NO 2 concentrations were selected: all buildings within 1000 m, mean slope within 100 m, primary roads within 2500 m, herbaceous land cover within 2500 m and average wind speed. Each predictor exhibits distinct temporal patterns of influence throughout the day. Our preliminary results on the final model, validated using a Leave-One-Station-Out Cross-Validation (LOOCV) procedure, reported a good overall fit (adjusted R2 = 60.5%, LOOCV R 2 = 58.0%) and good predictive accuracy. Compared to standard methods, the FLUR model provides an improved understanding of NO 2 exposure by recording its hourly variations and spatial determinants in a complex topographical region. nitrogen dioxide functional regression land use regression spatial prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 06 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7790380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530120711,"identity":"3620c012-8c96-4dac-af5d-d4208257889a","order_by":0,"name":"Paolo Girardi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACxgNAggfEkkioYGBgA7ESCOhB0nIGpoWAngMwhgRjG4yJR4s5++EDBz4w1MqYt599eOPhvMN5fBLJBxge/sCtxbInLeHgDIbjPDJn0o0tErcdLmaTSEvA6zCDAzkGh3kYjvFIMKSxSQC1JLbxnDHAr+X8G6gW/mdALXNAWs5/wK/lBtiWGh4JCZAtDUAt7D34Q8xyxjOgXwwOALU8Y7ZIOJYO1NJmcCAhDbcWc/7kgw8+VNTZS/CnMd78UWOdOL+Z+eHDHzZ4HAYhD6OKHsCtAaaFoQ6fmlEwCkbBKBjpAAAoN1A5cslBbwAAAABJRU5ErkJggg==","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":true,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Girardi","suffix":""},{"id":530120712,"identity":"f76c1e15-c0c7-472f-bace-ad6f1d505ac6","order_by":1,"name":"Claudia Collarin","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Collarin","suffix":""},{"id":530120715,"identity":"4625a191-b848-4ce3-bf3b-8973b5ecd8db","order_by":2,"name":"Ilaria Prosdocimi","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Ilaria","middleName":"","lastName":"Prosdocimi","suffix":""},{"id":530120717,"identity":"81cab818-3345-4d15-a46f-66104ca1c0b4","order_by":3,"name":"Mauro Masiol","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Masiol","suffix":""}],"badges":[],"createdAt":"2025-10-06 10:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7790380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7790380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95655956,"identity":"11548723-f1cc-42c8-9d33-8f77bdcb47a4","added_by":"auto","created_at":"2025-11-11 16:17:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3057441,"visible":true,"origin":"","legend":"","description":"","filename":"FunctionalLURmodelsub.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7790380/v1_covered_0416fb8d-568b-40b9-bc7f-afdb69711d38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Functional Land Use Regression Model for NO2 Concentration in the Italian Alpine Region","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"nitrogen dioxide, functional regression, land use regression, spatial prediction","lastPublishedDoi":"10.21203/rs.3.rs-7790380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7790380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution is a key risk factor associated to adverse health outcomes. Although pollution levels are typically measured at limited locations where air monitoring stations are placed, it is important to have some knowledge of the spatial distribution of pollution to assess the exposure of (groups of) individuals. Furthermore, since pollution levels are influenced by local sources such as traffic, industrial activities, and land use, it is of interest to enrich the prediction of pollution levels using information on these explanatory variables. Land Use Regression (LUR) models have emerged as effective tools for this purpose, relating pollutant concentrations to geographical characteristics. However, traditional LUR models often focus on average concentrations, overlooking crucial intra-day variability that can impact health outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this we introduce a novel functional LUR (FLUR) model designed to estimate hourly NO2 concentrations. This approach treats hourly pollutant measurements as functional data, capturing the continuous temporal dynamics of daily pollution levels. We applied this model to a comprehensive dataset of hourly NO2 measurements from 41 air monitoring stations in the Italian Alpine foothills and mountains collected throughout the year 2023. Our functional penalized regression model considers the hourly daily profile of log-transformed NO2 concentrations as dependent functional variable and a combination of scalar and functional variables as predictors. These variables may be related to meteorological information, key spatial predictors derived from GIS, or structural factors such as day of the week and day of the year.The choice of which predictors should be included in the predictive model was carried out through an innovative forward selection approach adapted to functional data. The approach is based on maximising the adjusted explained variance while avoiding concurvity in the selected predictors. This is achieved by taking into consideration the direction of the daily functional effect.\u003c/p\u003e\n\u003cp\u003eFive key predictors influencing hourly NO\u003csup\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/sup\u003e concentrations were selected: all buildings within 1000 m, mean slope within 100 m, primary roads within 2500 m, herbaceous land cover within 2500 m and average wind speed. Each predictor exhibits distinct temporal patterns of influence throughout the day. Our preliminary results on the final model, validated using a Leave-One-Station-Out Cross-Validation (LOOCV) procedure, reported a good overall fit (adjusted R2 = 60.5%, LOOCV R\u003csup\u003e2\u003c/sup\u003e = 58.0%) and good predictive accuracy.\u0026nbsp; Compared to standard methods, the FLUR model provides an improved understanding of NO\u003csub\u003e2\u003c/sub\u003e exposure by recording its hourly variations and spatial determinants in a complex topographical region.\u003c/p\u003e","manuscriptTitle":"A Functional Land Use Regression Model for NO2 Concentration in the Italian Alpine Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 05:43:19","doi":"10.21203/rs.3.rs-7790380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T14:17:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T14:11:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T16:08:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stochastic Environmental Research and Risk Assessment","date":"2025-10-06T10:15:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"843d67bd-9489-4242-a5e8-c8d55b3d7446","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T11:13:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 05:43:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7790380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7790380","identity":"rs-7790380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}