Forensic air quality monitoring using a polynomial enhanced random forest regression framework

Forensic air quality monitoring using a polynomial enhanced random forest regression framework | 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 Forensic air quality monitoring using a polynomial enhanced random forest regression framework Kunal Goyal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8973114/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Low-cost PM 2.5 sensors utilized for air quality monitoring in industrial port regions are subject to non-linear optical interferences resulting from the hygro- scopic growth of aerosols. In the marine industrial environment of the Baton Rouge Industrial Corridor, these distortions produce a systematic “Truth Gap,” leading to the overestimation of mass concentrations and undermining the reliability of environmental auditing. This study evaluates the perfor- mance of Polynomial-Enhanced Random Forest Regression (PERFR), a physics-aware calibration framework developed to mitigate these environmen- tal interferences. Utilizing a five-month longitudinal dataset colocated with the Baton Rouge Capitol AQS reference station (Site ID: 220330009), the algorithm integrates mass, humidity, temperature, and surface pressure measurements into a multidimensional Interaction Manifold (Γ). The identification of critical deliquescence-stagnation points within this manifold facilitates a piecewise sea- sonal adjustment of the sensor signal. Experimental results demonstrate that the PERFR approach reduces the Normalized Mean Bias (NMB) from 14.87% to −0.27%, showing significant statistical improvement over conventional multi- variable regression algorithms. This framework provides a robust methodology for aligning low-cost sensor performance with regulatory-grade forensic standards. Air quality index (AQI) environmental sensors gravimetric methods hygroscopic growth particulate matter (PM2.5) Polynomial-Enhanced Random Forest Regression (PERFR) relative humidity (RH) The Truth Gap Full Text Additional Declarations Competing interest reported. The authors declare the following financial interests: the methodologies described in this article, specifically the PERFR framework and the associated truth-gap diagnostic model, are the subject of a pending patent application. The authors declare no other competing financial or non-financial interests. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers invited by journal 23 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 14 Apr, 2026 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-8973114","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625259914,"identity":"0fc6e666-a27d-4950-9686-81adc14321a2","order_by":0,"name":"Kunal Goyal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYBACCTBZYAPh8RCvxSANopoULYdJ0CLZv/iYxA+D89H2EgmMD962EaFFWuJZmmSPwe3cHokEZsO5xGiRkzhjJsED0cImzUusFsk/BudAWth/E6VFmr/HTJrH4ADYFmaitEjOYEu2ljFIzu0587BZcs45IrRInD988OabCrvc9vbkgx/elBGhhUEigQUSNwyMDcSoBwL+A8wfiFQ6CkbBKBgFIxUAAJFyMoK2+lqDAAAAAElFTkSuQmCC","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Kunal","middleName":"","lastName":"Goyal","suffix":""}],"badges":[],"createdAt":"2026-02-26 04:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8973114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8973114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107293885,"identity":"c05d9f53-aa55-46e0-9f17-8876132db429","added_by":"auto","created_at":"2026-04-20 06:23:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":379472,"visible":true,"origin":"","legend":"","description":"","filename":"DiscoverSensorsV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8973114/v1_covered_b6ad2a44-a53c-42b3-999c-9db79ad6c01a.pdf"}],"financialInterests":"Competing interest reported. 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