Tuukka Petäjä, Ph.D.
Affiliations: | Physics | University of Helsinki, Helsingfors, Finland |
Area:
Atmospheric aerosolWebsite:
https://tuhat.halvi.helsinki.fi/portal/en/persons/tuukka-petaja(a9f2bbd1-d3ec-4665-b455-b5d5665b123f).htmlGoogle:
"Tuukka Petäjä"Mean distance: 11375.1
Parents
Sign in to add mentor| Markku Kulmala | grad student | 2006 | Univ. Helsinki | |
| (On Measurements of Formation, Growth, Hygroscopicity, and Volatility of Atmospheric Ultrafine Particles) | ||||
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Publications
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| Chen X, Ma W, Zheng F, et al. (2024) Identifying Driving Factors of Atmospheric NO with Machine Learning. Environmental Science & Technology |
| de Jonge RW, Xavier C, Olenius T, et al. (2024) Natural Marine Precursors Boost Continental New Particle Formation and Production of Cloud Condensation Nuclei. Environmental Science & Technology |
| Rörup B, He XC, Shen J, et al. (2024) Temperature, humidity, and ionisation effect of iodine oxoacid nucleation. Environmental Science: Atmospheres. 4: 531-546 |
| Li J, Hua C, Ma L, et al. (2024) Key drivers of the oxidative potential of PM in Beijing in the context of air quality improvement from 2018 to 2022. Environment International. 187: 108724 |
| Fung PL, Savadkoohi M, Zaidan MA, et al. (2024) Corrigendum to "Constructing transferable and interpretable machine learning models for black carbon concentrations" [Environ. Int. 184 (2024) 108449]. Environment International. 108561 |
| Garcia-Marlès M, Lara R, Reche C, et al. (2024) Inter-annual trends of ultrafine particles in urban Europe. Environment International. 185: 108510 |
| Marten R, Xiao M, Wang M, et al. (2024) Assessing the importance of nitric acid and ammonia for particle growth in the polluted boundary layer. Environmental Science: Atmospheres. 4: 265-274 |
| Blichner SM, Yli-Juuti T, Mielonen T, et al. (2024) Process-evaluation of forest aerosol-cloud-climate feedback shows clear evidence from observations and large uncertainty in models. Nature Communications. 15: 969 |
| Fung PL, Savadkoohi M, Zaidan MA, et al. (2024) Constructing transferable and interpretable machine learning models for black carbon concentrations. Environment International. 184: 108449 |
| Li D, Huang W, Wang D, et al. (2024) Nitrate Radicals Suppress Biogenic New Particle Formation from Monoterpene Oxidation. Environmental Science & Technology |