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

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1