The 3D biogeochemical marine mercury cycling model MERCY v2.0 – linking atmospheric Hg to methyl mercury in fish

Abstract

Abstract. Mercury (Hg) is a pollutant of global concern. Due to anthropogenic emissions, the global Hg burden has been ever increasing since preindustrial times. Hg emitted into the atmosphere gets transported on a global scale and ultimately reaches the oceans. There it is transformed into highly toxic methylmercury (MeHg) that effectively accumulates in the food web. The international community has recognized this serious threat to human health and in 2017 regulated Hg under the UN Minamata Convention. Currently, the first effectiveness evaluation of the Minamata Convention is being prepared and, in addition to observations, models play a major role in understanding environmental Hg pathways and in predicting the impact of policy decisions and external drivers (e.g. climate, emission, and land-use change) on Hg pollution. Yet, the available model capabilities are mainly limited to atmospheric models covering the Hg cycle from emission to deposition. With the presented model MERCY v2.0 we want to contribute to the currently ongoing effort to further our understanding of Hg and MeHg transport, transformation, and bioaccumulation in the marine environment with the ultimate goal of linking anthropogenic Hg releases to MeHg in sea food. Here, we present the governing equations and parameters implemented in the MERCY model and evaluate the model performance for two European shelf seas, the North-and Baltic Sea. With the presented model evaluation we want to establish a set of general quality criteria that can be used for evaluation of marine Hg models. The evaluation is based on a rigid statistical framework developed for the quantitative evaluation of atmospheric chemistry transport models. Using the approach, we show that the MERCY model can reproduce observed average concentrations of individual Hg species (normalized mean bias: HgT 17 %, Hg0 2 %, MeHg -28 %) in two complex coastal oceans. Moreover, it is able to reproduce the observed seasonality and spatial patterns. We find that the model error for HgT(aq) is mainly driven by the limitations of the physical model setup in the coastal zone and the poor quality of data on Hg in major rivers (i.e.: Schelde and Elbe). In addition, the model error in calculating vertical mixing and stratification contributes to the total HgT model error. For the vertical transport we find that the widely used particle partitioning coefficient for organic matter of log(kd)=5.6 is too low for the coastal systems. For Hg0 the model performance is at a level where further model improvements will be difficult to detect. For MeHg, there is still a lack in the basic understanding of the processes governing methylation and demethylation. While the model can reproduce average MeHg concentrations this lack in understanding hampers our ability to reproduce the observed value range. Finally, we evaluate Hg and MeHg concentrations in biota and show, that modelled values are within the range of observed levels of accumulation in phytoplankton, zooplankton, and fish. The results of the model evaluation prove the feasibility of developing marine Hg models with similar predictive capability as established atmospheric chemistry transport models. Our findings also highlight important knowledge gaps in the dynamics controlling methylation and bioaccumulation that, if closed, could lead to important improvements of the model performance.

Mercury Accumulation Pathways in a Model Marine Microalgae: Sorption, Uptake, and Partition Kinetics

“…The empirical partition coefficient ( K d ) is extensively used to quantify the scavenging of dissolved elements in natural waters . However, the magnitude of K d for Hg remains uncertain and is a major source of uncertainty in marine Hg cycle models, − since interactions between Hg and other solutes, especially DOM, significantly impact K d . Notably, K d decreases with increasing organic matter .…”

“…Understanding the interactions between mercury and phytoplankton is fundamental for modeling the Hg behavior in oceans. To improve these models, comprehensive information on Hg speciation, partitioning, and bioaccumulation dynamics is essential . However, the mechanisms of algal Hg uptake and the factors regulating this process in aquatic ecosystems are still not well-defined. ,, Even though there is a general understanding of phytoplankton Hg uptake, an intercomparable approach to how fast and to what degree this mechanism takes place is still lacking.…”

A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry

“…For example, Zhang et al (2020) and Rosati et al (2022) coupled Hg cycling with a biogeochemical and ecological model, considering the uptake to and release of Hg from marine biota. The lasted developed biogeochemical multi-compartment model for Hg cycling MERCY v2.0, developed by Bieser et al (2023), includes all currently known processes controlling marine Hg cycling.…”