Global high-resolution ultrafine particle number concentrations through data fusion with machine learning

Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, T. Christoudias, Andrea Pozzer, Constantine Dovrolis, Jos Lelieveld

Scientific Data 2025 data-paper cited by 9

importance 8/10 solved Air Quality and Health Impacts environmental health stage: research

No global high-resolution datasets of ultrafine particle (UFP) number concentrations existed, limiting assessment of a critical but under-measured health risk.

Atmospheric pollution causes millions of excess deaths annually, and UFPs penetrate deeply into the body causing systemic health impacts.

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.

R² ≥ 0.9; mean relative error value truncated in abstract

Delivers the first global 1 km PNC/UFP datasets (2010–2019), enabling UFP health-risk research.

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
8 / 10

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.

Basis: stated. The source itself states the scale. Stated scale: millions of excess deaths annually (air pollution); global 1 km datasets 2010-2019. Method and limits.

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