Is there a causal relationship between Particulate Matter (PM10) and air Temperature data? An analysis based on the Liang–Kleeman information transfer theory

Abstract

Abstract In the literature, it is well known that mineral dust play a key role in the atmospheric radiation budget. How to identify the cause–effect relationship between mineral dust and climatic parameters remains a crucial issue in atmospheric science and environment. In this study, the causal relation between particulate matter that have an aerodynamic diameter less than 10 μ m diameter ( P M 10 ) and air Temperature ( T ) is investigated for different time scales. For this purpose, two normalization schemes based on San Liang (2014)’s information flow formula and the classical convergent cross mapping introduced by Sugihara et\xa0al. (2012) were applied to eleven years of daily time series recorded in Guadeloupe archipelago. Both methods showed there is a bidirectional causality between the studied parameters. Indeed, we noticed that P M 10 concentrations tend to stabilize T values. This phenomenon has been attributed to a greenhouse effect which is strongly linked to African dust seasonality. During the high dust season, this effect is 13.2 times greater than in the low season. On the other hand, we found that T values tend to make P M 10 concentrations more uncertain in the low dust season while they homogenize P M 10 fluctuations in the high season. All these behaviors could be assigned to the impact of T values on P M 10 dry deposition velocity. To conclude, our results showed that information flow approach is an efficient tool to extract the cause–effect relationship between two dynamical events in atmospheric science, i.e. a field where several parameters interact simultaneously.