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Application of advanced causal analyses to identify processes governing secondary organic aerosols

Journal Article · · Scientific Reports

Abstract

Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth’s atmosphere.

Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI ID:
2349152
Alternate ID(s):
OSTI ID: 2446810
Journal Information:
Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United Kingdom
Language:
English

References (22)

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol journal June 2017
A Mathematical Theory of Communication journal July 1948
Chaos, Fractals, and Noise book January 1994
A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition journal June 2015
On Robust Computation of Koopman Operator and Prediction in Random Dynamical Systems journal November 2019
On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems journal March 2020
Testing for causality journal January 1980
Fully coupled “online” chemistry within the WRF model journal December 2005
Koopman Operator Methods for Global Phase Space Exploration of Equivariant Dynamical Systems journal January 2020
Tight Coupling of Surface and In-Plant Biochemistry and Convection Governs Key Fine Particulate Components over the Amazon Rainforest journal January 2022
Random Forests journal January 2001
Applied Koopmanism journal December 2012
Effects of anthropogenic emissions on aerosol formation from isoprene and monoterpenes in the southeastern United States journal December 2014
Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system journal May 2016
Measuring Information Transfer journal July 2000
Causality preserving information transfer measure for control dynamical system conference December 2016
Identifying causal interaction in power system: Information-based approach conference December 2017
On information transfer in discrete dynamical systems conference January 2017
The Bidirectional Communication Theory--A Generalization of Information Theory journal December 1973
On Information Transfer-Based Characterization of Power System Stability journal September 2019
Investigating Causal Relations by Econometric Models and Cross-spectral Methods journal August 1969
Data-Driven Approach for Inferencing Causality and Network Topology conference June 2018

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