Leveraging Remote Traffic Data for Local Air Pollutant Estimation: A Scenario-Based Machine Learning Study Across London Monitoring Sites

[2608.23219] Leveraging Remote Traffic Data for Local Air Pollutant Estimation: A Scenario-Based Machine Learning Study Across London Monitoring Sites Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2608.23219 (cs) [Submitted on 21 Aug 2026] Title: Leveraging Remote Traffic Data for Local Air Pollutant Estimation: A Scenario-Based Machine Learning Study Across London Monitoring Sites Authors: Valeria Legaria-Santiago , Amadeo Arguelles , Magdalena Saldana-Perez , Jocelyn Richardson , Marcella Bona View PDF HTML (experimental) Abstract: Vehicular traffic is a major source of air pollution; however, the contribution of remotely acquired traffic information to local machine-learning (ML) air-pollution models remains insufficiently characterised. This study evaluates four interpretable tree-based ML models (Random Forest, Extra Trees, LightGBM, and XGBoost) under six predictor scenarios combining progressively larger predictor sets, ranging from remotely acquired traffic, meteorological, and temporal variables alone to the inclusion of measurements from one and four neighbouring monitoring stations, to estimate NO$_2$, PM$_{10}$, PM$_{2.5}$, and O$_3$ concentrations across several sites in London. ML model performance was compared with a ridge linear regression model as a baseline, with spatial interpolation methods and with a cross-site validation experiment. When modelling without data from neighbouring stations, the RMSE for NO$_2$ ranged from 9.73 to 11.66 $\mu$g/m$^3$ without traffic information, compared with 8.72 to 11.52 $\mu$g/m$^3$ when traffic information was included. Additionally, for NO$_2$, SHAP analyses indicate that traffic-related variables can contribute at levels comparable to pollutant measurements from neighbouring monitoring stations in traffic-dominated~environments. Comments: Accepted for publication in Atmosphere Subjects: Machine Learning (cs.LG) Report number: Atmosphere 2026, 17, 806, special Issue Advances in Air Pollution Data Analysis: From Classical Geostatistics to Big Data and Artificial Intelligence Cite as: arXiv:2608.23219 [cs.LG] (or arXiv:2608.23219v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.23219 Focus to learn more arXiv-issued DOI via DataCite Journal reference: https://www.mdpi.com/2073-4433/17/8/806 Related DOI : https://doi.org/10.3390/atmos17080806 Focus to learn more DOI(s) linking to related resources Submission history From: Marcella Bona [ view email ] [v1] Fri, 21 Aug 2026 13:11:34 UTC (115,653 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files ( details ) : Correlation_Matrix_Camden-EustonRoad.png Correlation_Matrix_CamdenKerbside.png Correlation_Matrix_LondonMaryleboneRoad.png Correlation_Matrix_Wandsworth-PutneyHighStreet.png Correlation_Matrix_Westminster-OxfordStreet.png OpenWeather_validation_temperature_wind.png time_series_observed_ML_Ridge_NO2.png time_series_observed_ML_Ridge_O3.png time_series_observed_ML_Ridge_PM10.png time_series_observed_ML_Ridge_PM25.png (5 additional files not shown) Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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