Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

Abstract

Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiälä, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been wellestablished to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.

MegaSense: Feasibility of Low-Cost Sensors for Pollution Hot-spot Detection

“…pollution-aware navigation. Beyond the linear regression analysis of the measurement data, a non-linear correlation method, such as [38], could yield further patterns in the results.…”

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

“…This method has been widely used in various modern disciplines of science and technology, such as medical imaging [33], search engine [34] and DNA sequencing [35]. In the field related to atmospheric and environmental sciences, MI has been introduced to large data sets to detect nonlinear relationship between new-particle formation and other ambient variables in Hyytiälä Forest, Finland, as described in our previous work [36]. In this case, we adopt the same strategy for finding the relationship between a pollutant proxy and other measured variables.…”

“…The area contained by both circles is the joint entropy H(X, Y) and the orange area is the mutual information between X and Y, I(X; Y). Therefore, MI can be found by calculating As used in Zaidan et al [36], we also adopt a nearest-neighbour MI implementation based on Kraskov et al [37]. The nearest neighbour concept assumes the space Z = (X, Y).…”

“…where symbol sign represents a signum function and notation |.| is the absolute value. The mathematical details of the used MI method can be found in Kraskov et al [37], and its implementation in this field can be found in Zaidan et al [36].…”

Indoor Air Quality Monitoring Using Infrastructure-Based Motion Detectors

“…Indeed, the scatter plot and Pearson correlation coefficient are used for investigating linear correlation between two variables, that is typically very effective for non-complex processes. Non-linear correlation analysis should be performed further to investigate how the measured and unmeasured are interacting [18].…”