EGU2020-16961, updated on 13 Apr 2023
https://doi.org/10.5194/egusphere-egu2020-16961
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Applying causal discovery algorithm to find predictors for transformation process of wood combustion emission

Ville Leinonen1, Petri Tiitta2, Olli Sippula2,3, Hendryk Czech2,4, Ari Leskinen5,1, Juha Karvanen6, Sini Isokääntä1, and Santtu Mikkonen1,2
Ville Leinonen et al.
  • 1University of Eastern Finland, Department of Applied Physics, Finland (ville.j.leinonen@uef.fi)
  • 2University of Eastern Finland, Department of Environmental and Biological Sciences, Finland
  • 3University of Eastern Finland, Department of Chemistry, Finland
  • 4Helmholtz Zentrum München, Cooperation group “Comprehensive Molecular Analytics (CMA)”, Germany
  • 5Finnish Meteorological Institute, Kuopio, Finland
  • 6University of Jyväskylä, Department of Mathematics and Statistics, Finland

Aerosols and their transformation process in atmosphere have significant effects on climate. Transformation process is a complex combination of physical and chemical reactions. Multiple oxidizing agents and other factors, such as radiation, affect the transformation process. Characterization of these factors and their strength is a problem, where advanced methods might help to gain more understanding.

In this work, we modeled transformation of wood combustion emission measured in the environmental chamber by using causal modeling (Pearl, 2009). The aim of the study was to use state-of-the-art causal discovery methods to search causal pathways between measured variables: precursors and particle products. The data used in the modelling are introduced in Tiitta et al. (2016).

In addition to wood combustion experiments, we simulated artificial datasets to understand abilities of the model. We wanted to evaluate the accuracy of our model to confirm the correct structure between variables and reproduce the measured transformation. This helps us to understand the model performance in real datasets.

We found that model could reproduce the measured evolution well. The structure between emission parts was not completely matching to prior assumption. Usually incorrect predictors in the modeled structure are highly correlated with correct causes.

 

References:

Pearl, J.: Causality : Models, Reasoning and Inference., Cambridge University Press., 2009.

Tiitta et al., Atmos. Chem. Phys., 16, 13251-13269, 2016.

How to cite: Leinonen, V., Tiitta, P., Sippula, O., Czech, H., Leskinen, A., Karvanen, J., Isokääntä, S., and Mikkonen, S.: Applying causal discovery algorithm to find predictors for transformation process of wood combustion emission, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16961, https://doi.org/10.5194/egusphere-egu2020-16961, 2020.

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