Modeling atmospheric aging of small-scale wood combustion emissions : distinguishing causal effects from non-causal associations
Leinonen, V., Tiitta, P., Sippula, O., Czech, H., Leskinen, A., Isokääntä, S., Karvanen, J., & Mikkonen, S. (2022). Modeling atmospheric aging of small-scale wood combustion emissions : distinguishing causal effects from non-causal associations. Environmental Science : Atmospheres, 2(6), 1551-1567. https://doi.org/10.1039/D2EA00048B
Published inEnvironmental Science : Atmospheres
© 2022 The Author(s). Published by the Royal Society of Chemistry
Small-scale wood combustion is a significant source of particulate emissions. Atmospheric transformation of wood combustion emissions is a complex process involving multiple compounds interacting simultaneously. Thus, an advanced methodology is needed to study the process in order to gain a deeper understanding of the emissions. In this study, we are introducing a methodology for simplifying this complex process by detecting dependencies of observed compounds based on a measured dataset. A statistical model was fitted to describe the evolution of combustion emissions with a system of differential equations derived from the measured data. The performance of the model was evaluated using simulated and measured data showing the transformation process of small-scale wood combustion emissions. The model was able to reproduce the temporal evolution of the variables in reasonable agreement with both simulated and measured data. However, as measured emission data are complex due to multiple simultaneous interacting processes, it was not possible to conclude if all detected relationships between the variables were causal or if the variables were merely co-variant. This study provides a step toward a comprehensive, but simple, model describing the evolution of the total emissions during atmospheric aging in both gas and particle phases. ...
PublisherRoyal Society of Chemistry (RSC)
ISSN Search the Publication Forum2634-3606
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingThis work was supported by the Academy of Finland Centre of Excellence (grant no. 307331), Academy of Finland Flagship funding (grant no. 337550), the Academy of Finland Competitive funding to strengthen university research profiles (PROFI) for the University of Eastern Finland (grant no. 325022) and for the University of Jyväskylä (grant no. 311877) and the Nessling foundation. Data collection for this study has been partly funded by the European Union's 10 Horizon 2020 Research and Innovation Programme through the EUROCHAMP-2020 Infrastructure Activity (grant no. 730997). Funding sources have no involvement in study design, data analysis, or preparation of the manuscript. ...
Showing items with similar title or keywords.
Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions Hyttinen, Noora; Pihlajamäki, Antti; Häkkinen, Hannu (American Chemical Society (ACS), 2022)We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The ...
Microscopic Insights Into the Formation of Methanesulfonic Acid–Methylamine–Ammonia Particles Under Acid-Rich Conditions Liu, Min; Myllys, Nanna; Han, Yaning; Wang, Zhongteng; Chen, Liang; Liu, Wei; Xu, Jing (Frontiers Media SA, 2022)Understanding the microscopic mechanisms of new particle formation under acid-rich conditions is of significance in atmospheric science. Using quantum chemistry calculations, we investigated the microscopic formation ...
Estimating the causal effect of timing on the reach of social media posts Valkonen, Lauri; Helske, Jouni; Karvanen, Juha (Springer Science and Business Media LLC, 2022)Modern companies regularly use social media to communicate with their customers. In addition to the content, the reach of a social media post may depend on the season, the day of the week, and the time of the day. We ...
A study on the fragmentation of sulfuric acid and dimethylamine clusters inside an atmospheric pressure interface time-of-flight mass spectrometer Alfaouri, Dina; Passananti, Monica; Zanca, Tommaso; Ahonen, Lauri; Kangasluoma, Juha; Kubečka, Jakub; Myllys, Nanna; Vehkamäki, Hanna (Copernicus GmbH, 2022)Sulfuric acid and dimethylamine vapours in the atmosphere can form molecular clusters, which participate in new particle formation events. In this work, we have produced, measured, and identified clusters of sulfuric acid ...
Predicting liquid–liquid phase separation in ternary organic–organic–water mixtures Hyttinen, Noora (Royal Society of Chemistry (RSC), 2023)Liquid–liquid phase separation (LLPS) affects the water uptake of aerosol particles in the atmosphere through Kelvin and Raoult effects. This study investigates LLPS in ternary mixtures containing water and two organic ...