GMDD

Geoscientific Model Development Discussions

GMDD

Geosci. Model Dev. Discuss.

1991-962X

Göttingen, Germany

10.5194/gmd-2020-13

Atmospheric aging of small-scale wood combustion emissions (model MECHA 1.0) – is it possible to distinguish causal effects from non-causal associations?

Leinonen

Ville

¹ Tiitta

Petri

https://orcid.org/0000-0003-4694-3648

² Sippula

Olli

² ³ Czech

Hendryk

https://orcid.org/0000-0001-8377-4252

² ⁶ Leskinen

Ari

⁴ ¹ Karvanen

Juha

⁵ Isokääntä

Sini

https://orcid.org/0000-0001-6307-7206

¹ Mikkonen

Santtu

https://orcid.org/0000-0003-0595-0657

¹ ²

Department of Applied Physics, University of Eastern Finland, Kuopio, Finland

Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland

Department of Chemistry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland

Finnish Meteorological Institute, Kuopio, Finland

Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland

now at: Cooperation group <q>Comprehensive Molecular Analytics (CMA)</q>, Helmholtz Zentrum München, 81379 München, Germany

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24 01 2020

2020 1 34

2020

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://gmd.copernicus.org/preprints/gmd-2020-13/

The full text article is available as a PDF file from https://gmd.copernicus.org/preprints/gmd-2020-13/gmd-2020-13.pdf

Primary emissions of wood combustion are complex mixtures of hundreds or even over a thousand compounds, which pass through a series of chemical reactions and physical transformation processes in the atmosphere (<q>aging</q>). This aging process depends on atmospheric conditions, such as concentration of atmospheric oxidizing agents (OH radical, ozone and nitrate radicals), humidity and solar radiation, and is known to strongly affect the characteristics of atmospheric aerosols. However, there are only few models that are able to represent the aging of emissions during its lifetime in the atmosphere. In this work, we implemented a model (Model for aging of Emissions in environmental CHAmber, MECHA v 1.0) to describe the evolution by differential equation system. The model performance was first evaluated using two different, simulated datasets. The purpose of the evaluation was to investigate the ability of the model to (1) find the correct relationships between the variables in the dataset and (2) to evaluate the accuracy of the model to reproduce the evolution of variables in time. Subsequently, the model was implemented to wood combustion exhaust in atmosphere, based on a dataset from smog chamber experiments. Evaluation in simulated datasets served as a basis of the drawings made from modeled aging of the residential wood combustion emission. We found that the model was able to reproduce the evolution of the variables in time reasonably well. By using the state of the art detection algorithms for causal structures, we could unveil a large number of relationships for measured variables. However, as the emission data is complex in its nature due to multiple processes interacting with each other, for many relationships it was not possible to say if there was a causal pathway or if the variables were just covarying. This study serves as the first step towards a comprehensive model for the description of the evolution of the whole emission in both gas- and particle phase during atmospheric aging. We present contributions to challenges faced in this kind of modeling and discuss the possible improvements and expected importance of those for the model.