Air Pollution and Deep Learning in Prevention, New Challenges and New Solutions

Air Pollution and Deep Learning in Prevention, New Challenges and New Solutions | 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 Air Pollution and Deep Learning in Prevention, New Challenges and New Solutions Silvia Soledad Moreno-Gutiérrez, Héctor Hugo Siliceo-Cantero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707178/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Globally, industrialization has driven a socio-economic transformation that catalyzed the development of the modern technological era. However, despite its benefits for quality of life (QoL) and social well-being, its collateral effects have been severe; excessive emission of greenhouse gases intensifies climate instability, exacerbates environmental pollution, and increases public health risks. In fact, the WHO reports 7 million annual premature deaths, 80% linked to poor air quality (AQ). In Central America, the situation is critical due to socioenvironmental vulnerability, particularly in regions such as the Atmospheric Basin of Tula (ABT) in Mexico, an industrial area declared a sanitary emergency due to its lethality and epidemiological risk. The current scenario reveals three structural trends of growing uncertainty, two adverse; 1) environmental and 2) socio-health, and a third aimed at addressing them; the technology through deep learning (DL) neural models with evolutionary preventive potential. This study analyzed environmental conditions, health risks, and QoL in ABT, based on seven alarming findings, seven DL models (three recurrent, two of classification, and two of regression) were developed applying the CRISP-DM methodology. CO\textsubscript{2} achieved R 2 = 0.82, Explained Variance (EV)= 0.80, and RMSE = 5.07; O\textsubscript{3} + NO\textsubscript{2} obtained R 2 = 0.88, EV = 0.88, and RMSE = 16.44; PM\textsubscript{10} reached R 2 = 0.98, EV = 0.99, and RMSE = 20.89; blood pressure (BP) showed R 2 = 0.99, EV = 0.98, and RMSE = 0.00012; for chronic disease (CD) risk and AQ achieved accuracy rates of 98%, 98%, sensitivity of 97%, 93%, and F1-score of 98%, 95%, respectively; QoL attained R 2 = 0.99, EV = 0.99, and RMSE = 0.000974, scores of 0.00115 physical health, 0.002469 psychological, 0.000981 social relationships, and 0.000974 environment. DL proves to be a structural trend capable of representing and predicting dynamic health-ecological phenomena, this is an unprecedented preventive tool for confronting complex future scenarios, reducing risks, and supporting decision-making toward plausible outcomes. Climate change deep learning air pollution health risks predictive models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 03 Oct, 2025 Editor assigned by journal 26 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 24 Sep, 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. 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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-7707178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528769159,"identity":"26fcb51e-c173-4324-8839-f51676d0d16f","order_by":0,"name":"Silvia Soledad 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Solutions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, deep learning, air pollution, health risks, predictive models","lastPublishedDoi":"10.21203/rs.3.rs-7707178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, industrialization has driven a socio-economic transformation that catalyzed the development of the modern technological era. However, despite its benefits for quality of life (QoL) and social well-being, its collateral effects have been severe; excessive emission of greenhouse gases intensifies climate instability, exacerbates environmental pollution, and increases public health risks. In fact, the WHO reports 7 million annual premature deaths, 80% linked to poor air quality (AQ). In Central America, the situation is critical due to socioenvironmental vulnerability, particularly in regions such as the Atmospheric Basin of Tula (ABT) in Mexico, an industrial area declared a sanitary emergency due to its lethality and epidemiological risk. The current scenario reveals three structural trends of growing uncertainty, two adverse; 1) environmental and 2) socio-health, and a third aimed at addressing them; the technology through deep learning (DL) neural models with evolutionary preventive potential. This study analyzed environmental conditions, health risks, and QoL in ABT, based on seven alarming findings, seven DL models (three recurrent, two of classification, and two of regression) were developed applying the CRISP-DM methodology. CO\\textsubscript{2} achieved R\u003csup\u003e2\u003c/sup\u003e = 0.82, Explained Variance (EV)= 0.80, and RMSE = 5.07; O\\textsubscript{3} + NO\\textsubscript{2} obtained R\u003csup\u003e2\u003c/sup\u003e = 0.88, EV = 0.88, and RMSE = 16.44; PM\\textsubscript{10} reached R\u003csup\u003e2\u003c/sup\u003e = 0.98, EV = 0.99, and RMSE = 20.89; blood pressure (BP) showed R\u003csup\u003e2\u003c/sup\u003e = 0.99, EV = 0.98, and RMSE = 0.00012; for chronic disease (CD) risk and AQ achieved accuracy rates of 98%, 98%, sensitivity of 97%, 93%, and F1-score of 98%, 95%, respectively; QoL attained R\u003csup\u003e2\u003c/sup\u003e = 0.99, EV = 0.99, and RMSE = 0.000974, scores of 0.00115 physical health, 0.002469 psychological, 0.000981 social relationships, and 0.000974 environment. DL proves to be a structural trend capable of representing and predicting dynamic health-ecological phenomena, this is an unprecedented preventive tool for confronting complex future scenarios, reducing risks, and supporting decision-making toward plausible outcomes.\u003c/p\u003e","manuscriptTitle":"Air Pollution and Deep Learning in Prevention, New Challenges and New Solutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 13:55:20","doi":"10.21203/rs.3.rs-7707178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T07:24:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T03:40:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99802568997632557476057003394105938046","date":"2025-11-06T05:02:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300728179758714590301777584830979095058","date":"2025-11-05T15:38:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T05:12:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6638812531147980664683907684341928657","date":"2025-10-03T13:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48920375010171467818895492588739786935","date":"2025-10-03T10:52:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T10:47:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-26T09:36:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T09:34:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Atmosphere","date":"2025-09-24T22:25:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db95a26c-f2d2-4823-b9b9-1e62209fbce7","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T09:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 13:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7707178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7707178","identity":"rs-7707178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}