Work

Using open data to derive parsimonious data-driven models for uncovering the influence of local traffic and meteorology on air quality: The case of Madrid

Year: 2025

Type: article

Abstract: Air pollution remains a critical public health and environmental challenge, particularly in urban areas where traffic emissions and meteorological conditions strongly influence air quality. While Machine Learning (ML) techniques have been increasingly used to model pollutant concentrations, many existing studies rely on complex architectures that often integrate multiple heterogeneous data sources.In contrast, this study presents a parsimonious, data-driven ML model that predicts local hourly concentrations of key pollutants---NO2, O3, PM2.5, and PM10---in Madrid using only open data sources. A key factor of our approach is the incorporation of hourly road traffic data collected in the immediate vicinity of each pollutant monitoring station as a predictor. This localized traffic information, combined with local meteorological data, allows our model to outperform other existing solutions that often depend on historical and/or proprietary data.Our results clearly demonstrate that better data might surpass the benefits of more complex ML architectures.The model achieves strong predictive accuracy, with test R2 scores ranging from 0.787 to 0.874 for NO2, 0.765 to 0.855 for O3, 0.483 to 0.744 for PM2.5, and 0.635 to 0.941 for PM10. Overall, the model outperforms similar solutions for the city of Madrid, highlighting the significant impact of localized traffic data in improving pollutant concentration predictions, particularly in areas where emissions are dominated by road traffic.Additionally, we conducted a sensitivity analysis to assess the impact of reducing vehicle emissions on local NO2 levels, offering actionable insights for policymakers. The findings highlight the potential of open-data-driven models in urban air quality management, providing a scalable, cost-effective, and interpretable tool to support evidence-based decision-making and environmental policy design.

Source: Environmental Pollution

DOI: 10.1016/j.envpol.2025.126691

Authors: Koorosh Kazemi, Anton Vernet, Alexandre Fabregat

Institution: Universitat Rovira i Virgili

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $4,090