Sensitivity analysis of an updated bidirectional air–surface exchange model for elemental mercury vapor

Abstract

Abstract. A box model for estimating bidirectional air–surface exchange of gaseous elemental mercury (Hg0) has been updated based on the latest understanding of the resistance scheme of atmosphere–biosphere interface transfer. Simulations were performed for two seasonal months to evaluate diurnal and seasonal variation. The base-case results show that water and soil surfaces are net sources, while vegetation is a net sink of Hg0. The estimated net exchange in a domain covering the contiguous US and part of Canada and Mexico is 38.4 and 56.0 Mg as evasion in the summer and winter month, respectively. The smaller evasion in summer is due to the stronger Hg0 uptake by vegetation. Modeling experiments using a two-level factorial design were conducted to examine the sensitivity of flux response to the changes in physical and environmental parameters in the model. It is shown that atmospheric shear flows (surface wind over water and friction velocity over terrestrial surfaces), dissolved gaseous mercury (DGM) concentration, soil organic and Hg content, and air temperature are the most influential factors. The positive effect of friction velocity and soil Hg content on the evasion flux from soil and canopy can be effectively offset by the negative effect of soil organic content. Significant synergistic effects are identified between surface wind and DGM level for water surface, and between soil Hg content and friction velocity for soil surface, leading to ~50% enhanced flux compared to the sum of their individual effects. The air–foliage exchange is mainly controlled by surface resistance terms influenced by solar irradiation and air temperature. Research in providing geospatial distribution of Hg in water and soil will greatly improve the flux estimate. Elucidation on the kinetics and mechanism of Hg(II) reduction in soil/water and quantification of the surface resistances specific to Hg species will also help reduce the model uncertainty.