A1 Journal article (refereed)
Modeling atmospheric aging of small-scale wood combustion emissions : distinguishing causal effects from non-causal associations (2022)
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
JYU authors or editors
Publication details
All authors or editors: Leinonen, Ville; Tiitta, Petri; Sippula, Olli; Czech, Hendryk; Leskinen, Ari; Isokääntä, Sini; Karvanen, Juha; Mikkonen, Santtu
Journal or series: Environmental Science : Atmospheres
eISSN: 2634-3606
Publication year: 2022
Publication date: 10/10/2022
Volume: 2
Issue number: 6
Pages range: 1551-1567
Publisher: Royal Society of Chemistry (RSC)
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1039/D2EA00048B
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84402
Abstract
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.
Keywords: firewood; particulate emissions; fine particles; organic compounds; aerosols; atmospheric chemistry; air quality; causality; statistical models
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1