Machine learning-enhanced high-resolution exposure assessment of ultrafine particles.
Where this comes from
- Record sourced from PubMed, PMID 39885206.
- Also identified by DOI 10.1038/s41467-025-56581-8 and PMC identifier 11782512.
- Licence recorded as CC BY-NC-ND.
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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 10<sup>4</sup> particles‧cm<sup>-3</sup>, with a national average of (9.3 ± 4.7)×10<sup>3</sup> particles‧cm<sup>-3</sup>, ranging from (5.5 ± 2.3)×10<sup>3</sup> (rural) to (1.4 ± 0.5)×10<sup>4</sup> particles‧cm<sup>-3</sup> (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 PM<sub>2.5</sub>. These findings provide crucial insights for the development of future UFP standards.