Preprints
https://doi.org/10.5194/gmd-2020-13
https://doi.org/10.5194/gmd-2020-13
Submitted as: methods for assessment of models
 | 
24 Jan 2020
Submitted as: methods for assessment of models |  | 24 Jan 2020
Status: this preprint was under review for the journal GMD but the revision was not accepted.

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

Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen

Abstract. 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 (aging). 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen
 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen
Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen

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