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 stackin...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56581-8 |
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| author | Yudie Jianyao Hongyong Yuan Guofeng Su Jing Wang Wenguo Weng Xiaole Zhang |
| author_facet | Yudie Jianyao Hongyong Yuan Guofeng Su Jing Wang Wenguo Weng Xiaole Zhang |
| author_sort | Yudie Jianyao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d9d642a644844aeda8710f726b236ab8 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d9d642a644844aeda8710f726b236ab82025-02-02T12:33:22ZengNature PortfolioNature Communications2041-17232025-01-0116111510.1038/s41467-025-56581-8Machine learning-enhanced high-resolution exposure assessment of ultrafine particlesYudie Jianyao0Hongyong Yuan1Guofeng Su2Jing Wang3Wenguo Weng4Xiaole Zhang5School of Safety Science, Tsinghua UniversitySchool of Safety Science, Tsinghua UniversitySchool of Safety Science, Tsinghua UniversityInstitute of Environmental Engineering (IfU), ETH ZürichSchool of Safety Science, Tsinghua UniversitySchool of Safety Science, Tsinghua UniversityAbstract 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.https://doi.org/10.1038/s41467-025-56581-8 |
| spellingShingle | Yudie Jianyao Hongyong Yuan Guofeng Su Jing Wang Wenguo Weng Xiaole Zhang Machine learning-enhanced high-resolution exposure assessment of ultrafine particles Nature Communications |
| title | Machine learning-enhanced high-resolution exposure assessment of ultrafine particles |
| title_full | Machine learning-enhanced high-resolution exposure assessment of ultrafine particles |
| title_fullStr | Machine learning-enhanced high-resolution exposure assessment of ultrafine particles |
| title_full_unstemmed | Machine learning-enhanced high-resolution exposure assessment of ultrafine particles |
| title_short | Machine learning-enhanced high-resolution exposure assessment of ultrafine particles |
| title_sort | machine learning enhanced high resolution exposure assessment of ultrafine particles |
| url | https://doi.org/10.1038/s41467-025-56581-8 |
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