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Integrating Low-Cost Sensors with Dispersion Modelling for High-Resolution Insights into Urban Air Quality.
Anna C O'Regan1, Henrik Grythe2, Philipp Schneider2
1Sustainable Futures Lab, School of Engineering & Architecture, University College Cork, Cork, Ireland; Sustainability Institute, University College Cork, Lee Rd, Sunday's Well, Cork T23 XE10, Ireland.
Environment International
|March 1, 2026
Summary
A new data fusion model significantly improves urban air quality predictions, revealing higher PM2.5 pollution in residential areas. This advanced modeling aids in developing targeted policies to reduce air pollution and protect public health.
Area of Science:
- Environmental Science
- Public Health
- Atmospheric Chemistry
Background:
- Urban air pollution poses significant health risks, necessitating accurate modeling for effective policy development.
- Existing air quality models may underestimate emissions from residential sources, leading to inaccurate exposure assessments.
Purpose of the Study:
- To develop and validate an advanced urban air quality model integrating sensor data.
- To compare the performance of a data fusion model against a baseline model.
- To enhance the assessment of air quality policies and identify pollution hotspots.
Main Methods:
- Development of a baseline urban air quality model.
- Integration of data from low-cost sensors and regulatory monitors into the baseline model to create a data fusion model.
- Comparison of model outputs and evaluation of FAIRMODE compliance and bias.
Main Results:
- The data fusion model provided high spatiotemporal resolution air quality data, particularly for PM2.5.
- Higher PM2.5 concentrations were observed during evening hours and winter months, with population-weighted exposure nearly double the baseline prediction.
- The data fusion model identified peak concentrations shifting to residential areas, up to 10 µg/m³ higher than the baseline, suggesting underestimation of residential emissions.
Conclusions:
- The data fusion model offers a more accurate assessment of urban air quality and public exposure.
- This improved modeling supports the development of targeted air quality policies, especially for reducing pollution from solid fuel burning.
- The study highlights the importance of integrating diverse data sources for effective urban air quality management and public health protection.
