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Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varie...

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Published in:Environmental pollution (1987) 2024-04, Vol.346, p.123664-123664, Article 123664
Main Authors: Amini, Heresh, Bergmann, Marie L., Taghavi Shahri, Seyed Mahmood, Tayebi, Shali, Cole-Hunter, Thomas, Kerckhoffs, Jules, Khan, Jibran, Meliefste, Kees, Lim, Youn-Hee, Mortensen, Laust H., Hertel, Ole, Reeh, Rasmus, Gaarde Nielsen, Christian, Loft, Steffen, Vermeulen, Roel, Andersen, Zorana J., Schwartz, Joel
Format: Article
Language:English
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Summary:Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 μm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100–150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital. [Display omitted] •Developed first machine-learning models for three UFP metrics in Copenhagen, Denmark.•Annual mean PNC, LDSA, and average particle size (APS) were 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm.•The cross-validated R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS.•Novel insights into the spatial variation of UFP exposure metrics in a European city.•Providing the opportunity to study PNC/LDSA health effects and effect modification by APS.
ISSN:0269-7491
1873-6424
DOI:10.1016/j.envpol.2024.123664