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

SUBMITTER: Jianyao Y 

PROVIDER: S-EPMC11782512 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature