{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:02:41Z","timestamp":1790697761557,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) offer flexible, three-dimensional access for air pollution monitoring, but rotor-induced aerodynamic disturbance can bias onboard sensor readings, an effect not yet well characterized for multi-rotor platforms. This paper presents a physics-informed framework unifying rotor wake characterization, turbulence zone classification, pollutant sensitivity analysis, sensor placement strategy, and bias correction into one design tool. Grounded in actuator disk momentum theory and literature-constrained relationships, it predicts or bounds measurement bias across nine pollutants and multiple rotor configurations, quantitative for seven sensor classes and provisional for O3 and VOCs. It also introduces a four-zone contamination classification and a two-stage bias correction for motion-induced and environmental factors. Hover and constant-speed co-location flight tests on a 21-inch quadrotor, comparing a UAV-mounted sensor against a fixed reference under Zone 4 conditions, support these predictions: observed bias (3\u201310% in hover; 6\u201315% at 5 m\/s, across NO2, CO, O3, PM10, and PM2.5) was directionally consistent with the predictions, though correlation dropped as low as r = 0.44 in forward flight, below typical validation thresholds. The tests thus support the framework\u2019s qualitative trends more than quantitative agreement, with wind-tunnel and multi-speed validation as natural next steps. The framework offers a reproducible foundation for UAV sensor integration design, uncertainty estimation, and campaign planning.<\/jats:p>","DOI":"10.3390\/s26196178","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:14:37Z","timestamp":1790694877000},"page":"6178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UAV-Induced Measurement Bias in Air Pollution Sensing: A Physics-Informed Framework for Characterization, Sensor Placement, and Correction"],"prefix":"10.3390","volume":"26","author":[{"given":"Farhad","family":"Samadzadegan","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 11365-4563, Iran"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0985-2978","authenticated-orcid":false,"given":"Erfan","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 11365-4563, Iran"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide, WHO Press."},{"key":"ref_2","unstructured":"Health Effects Institute (2024). State of Global Air 2024: Special Report, Health Effects Institute."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.envint.2014.11.019","article-title":"The rise of low-cost sensing for managing air pollution in cities","volume":"75","author":"Kumar","year":"2015","journal-title":"Environ. 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