Machine learning-enhanced high-resolution exposure assessment of ultrafine particles

Yudie Jianyao, Hongyong Yuan, Guofeng Su, Jing Wang, Wenguo Weng and Xiaole Zhang ()
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Yudie Jianyao: Tsinghua University
Hongyong Yuan: Tsinghua University
Guofeng Su: Tsinghua University
Jing Wang: ETH Zürich
Wenguo Weng: Tsinghua University
Xiaole Zhang: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Ultrafine particles (UFPs) under 100 nm pose significant health risks inadequately addressed by traditional mass-based metrics. The WHO emphasizes particle number concentration (PNC) for assessing UFP exposure, but large-scale evaluations remain scarce. In this study, we developed a stacking-based machine learning framework integrating data-driven and physical-chemical models for a national-scale UFP exposure assessment at 1 km spatial and 1-hour temporal resolutions, leveraging long-term standardized PNC measurements in Switzerland. Approximately 20% (1.7 million) of the Swiss population experiences high UFP exposure exceeding an annual mean of 104 particles‧cm−3, with a national average of (9.3 ± 4.7)×103 particles‧cm−3, ranging from (5.5 ± 2.3)×103 (rural) to (1.4 ± 0.5)×104 particles‧cm−3 (urban). A nonlinear relationship is identified between the WHO-recommended 1-hour and 24-hour exposure reference levels, suggesting their non-interchangeability. UFP spatial heterogeneity, quantified by coefficient of variation, ranges from 4.7 ± 4.2 (urban) to 13.8 ± 15.1 (rural) times greater than PM2.5. These findings provide crucial insights for the development of future UFP standards.

Date: 2025
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DOI: 10.1038/s41467-025-56581-8

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