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

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 PM_2.5. These findings provide crucial insights for the development of future UFP standards.