Technical note: A framework for causal inference applied to solar radiation and temperature effects on dissolved gaseous mercury

Abstract. Environmental science usually requires researchers to rely on observational data alone. However, researchers want to identify causal relationships and not only correlations between pollutant behaviour and other environmental factors such as weather. Previously it has been shown that solar radiation associates with the volatilisation and evasion of the hazardous pollutant mercury from sea surfaces into the atmosphere. Statistical and machine learning methods can help find and quantify such associations. However, association does not imply causation, and inferring causal relationships from observational data alone remains a significant challenge. Here, we aim to create an 'easy-to-follow' framework, to be used by environmental researchers, for using prior scientific knowledge encoded as graphical causal models to enable causal inference and to estimate effect sizes of different related factors using collected field data. We demonstrate the framework through a case study estimating the effect sizes of solar radiation and sea surface temperature on dissolved gaseous mercury (DGM) in seawater measured at the west coast of Sweden. Our causal analysis reveals that 32 % of the total effect of solar radiation on DGM is mediated indirectly via changes in sea surface temperature. Wind and instrumentation acted as confounders, biasing effect estimates by 4.5 %. Results from the case study show that our proposed framework allows for a rigorous design, validation, and reporting of causal inference in environmental science. It shows potential in modelling causes of pollutant dynamics and quantifying the effect of regulating policies such as the Minamata Convention For Mercury.