Global high-resolution ultrafine particle number concentrations through data fusion with machine learning
Problem
No global high-resolution datasets of ultrafine particle (UFP) number concentrations existed, limiting assessment of a critical but under-measured health risk.
Why it matters
Atmospheric pollution causes millions of excess deaths annually, and UFPs penetrate deeply into the body causing systemic health impacts.
What the AI did
An XGBoost model fused ground station measurements with global environmental and anthropogenic variables to predict annual particle number concentrations (2010–2019) at 1 km resolution over land.
Results
R² ≥ 0.9; mean relative error value truncated in abstract
Impact
Delivers the first global 1 km PNC/UFP datasets (2010–2019), enabling UFP health-risk research.
Record
| Venue | Scientific Data |
|---|---|
| Published | 2025 |
| Work type | data-paper |
| Authors | Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, T. Christoudias, Andrea Pozzer, Constantine Dovrolis, Jos Lelieveld |
| Citations | 9 (OpenAlex, at import time) |
| References | 60 |
| Model or method | XGBoost |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41597-025-06055-9 |
| Open access | gold |
| Keywords | ultrafine particles, particle number concentration, XGBoost, data fusion, air pollution |
Importance rating
major — millions of lives, an industry or a field
Abstract states atmospheric pollution causes millions of excess deaths annually and UFPs are a critical health risk; first global 1 km PNC/UFP datasets enable health-risk research at global scale.
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