Machine learning-enhanced high-resolution exposure assessment of ultrafine particles
Yudie Jianyao, Hongyong Yuan, Guofeng Su, Jing Wang, Wenguo Weng, Xiaole Zhang
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. This study develops a machine learning framework for national-scale ultrafine particle (UFP) exposure assessment in Switzerland, revealing 20% of the population faces high exposure levels, with significant implications for future UFP standards