for their 2010–2020 simulations. Sea surface concentrations of Hg(0) from the oceanic models will be averaged into ensemble monthly means for 2010–2020, which can then be provided back to the atmospheric models for input into their simulations. We will conduct two to three iterations of this approach to reach convergence of the ensemble mean of atmospheric and oceanic baseline model simulations. This procedure will produce 2010–2020 ensemble mean sea surface Hg(0) concentrations (which will be the standard inputs for further atmospheric simulations) and ensemble mean deposition and Hg(0) air concentration fields (which will be the standard inputs for further oceanic simulations).

- **Stage 3a: mass balance simulations with updated rate constants.** Mass balance models require globally averaged estimates of rate constants for processes that transfer Hg between different environmental compartments (see Table F7). The ensemble mean data for the atmospheric and oceanic baseline simulations (2010–2020) will be used to calculate updated rate constants needed for the mass balance simulations. Rate constants for terrestrial pools will be estimated based on the past literature (Amos et al., 2013, 2015; Qureshi et al., 2011). In doing so, we will update the GBC box models to be consistent with the state-of-the-art 3D atmospheric and oceanic models for the purposes of this study. Uncertainty bounds for the rate constants will also be estimated from the variability of individual atmospheric and oceanic models within the ensemble, for use in sensitivity simulations of the mass balance model. The legacy re-emission trend and magnitude predicted by the GBC box model can be used as input for further sensitivity simulations in the 3D atmospheric and oceanic models.

- **Stage 3b: perturbation simulations in atmospheric and oceanic models.** Atmospheric models will run perturbation simulations for 2010–2020 (Table F8), e.g., turning off all anthropogenic Hg emissions. The individual atmospheric models’ deposition and concentration fields will be averaged into ensemble monthly mean fields. The atmospheric ensemble mean fields will be passed on to the oceanic models as inputs for their perturbation simulations. As opposed to Stage 2, a selection of perturbation experiments will be considered for multiple iterative rounds of simulations for the atmosphere-ocean coupling, depending on the computational expense and time constraints. For example, a coupled atmosphere-ocean “nature simulation” for 2010–2020, where primary anthropogenic emissions are kept fixed at the 2010 level and the Hg trend is solely driven by natural factors, will be performed using the iterative approach.

- **Stage 4: further sensitivity analysis in models.** Atmospheric, oceanic, and mass balance models will conduct further sensitivity simulations, e.g., testing the uncertainty of model simulations to different driver datasets, conducting idealized emission trend experiments, and testing the uncertainty in different model parameterizations (e.g., air-sea exchange parameterizations) and input fields (e.g., Hg and MeHg loads from rivers in the ocean models). During this phase, atmospheric and oceanic models do not require coupling for their individual experiments.

## 5.2 Three-dimensional atmosphere and ocean simulations

The proposed coordinated multimodel atmospheric and oceanic model simulations for MCHgMAP are presented in Tables F8 and F9. Most of the model simulations will be performed for the period 2010–2020 for historical trends and from 2020 to 2050 for the future scenarios, except for several optional sensitivity simulations, which can be conducted for individual years. Appropriate model spinup to reach steady-state conditions is required; a consecutive simulation from 1980 to 2020 starting from semi-realistic initial conditions is recommended for ocean models, while a spinup of approximately 5 years is needed for atmospheric models. The primary and secondary emissions for atmospheric models over land are provided by emission inventories and mechanistic air-land Hg exchange model simulations (Sect. 3), and over oceans the boundary conditions are exchanged between atmospheric and ocean models, as described in the previous section (Fig. 8). The model simulations in Tables F8 and F9 are divided into five groups, in accordance with their purposes:

i. The “baseline” simulation (A1; O1) aims to obtain global patterns and temporal trends of atmospheric and oceanic Hg concentrations and fluxes utilizing state-of-the-art model configurations for model evaluation (Sect. 6) and analysis (Sects. 7 and 8) of observed Hg levels based on the modeling. The simulation will be performed using temporally varying default set of drivers (marked in bold in Tables F5 and F6) that are harmonized as much as possible between the models (using a combination of updated harmonized and model-specific input datasets and parameterizations).

ii. The “Source contributions” group (A2.1–A2.6; O2.1–O2.6) is focused on assessment of the relative contributions of various primary and secondary emissions and releases from anthropogenic and natural sources to Hg levels in the air and ocean. It consists of perturbation simulations with zeroed emissions from specific source types.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Diagram showing how the coupling between the atmospheric, oceanic, and mass balance models will be achieved by conducting the work in stages and exchanging information between the model types in MCHgMAP.</figcaption></figure>

iii. The “Trend analysis” group (A3a.1–A3c.12; O3.1–O3.14) is intended to evaluate contributions of various anthropogenic and environmental drivers to recent changes in Hg levels over the considered period (2010–2020). This group is divided into three subgroups: “Contributions of various drivers to trends” (A3a.1–A3a.4), “Effects of changes in various drivers on trends” (A3b.1–A3b.7), and “Contributions of HTAP region anthropogenic emission changes to trends” (A3c.1–A3c.12). HTAP regions are the geographical world regions used in previous modeling experiments organized by the LRTAP Task Force on Hemispheric Transport of Air Pollution (Galmarini et al., 2017). These regions are defined in Appendix C. The perturbation simulations in the first and third subgroups are carried out with one driver fixed at the values corresponding to 2010 for the whole period, with other drivers varying in accordance with the baseline simulation. The perturbation simulations in the second subgroup are carried out with one driver varying as in the baseline simulation and all of the other drivers fixed at values corresponding to 2010. The comparison of the perturbation simulations with the results of the baseline simulation provides contributions to or effects on the considered drivers of the trend in Hg levels.

iv. The “Sensitivity analysis” group (A4.1–A5.10; O4.1–O4.7) is divided into two subgroups: “Uncertainty experiments” (A4.1–A4.8) and “Idealized experiments” (A5.1–A5.10). The first subgroup of simulations diagnoses modeling uncertainties using alternative driver datasets when available (e.g., multiple anthropogenic emission inventories may be available for the time period 2010–2020) or alternative parameterizations (e.g., multiple air–sea exchange parameterizations could be compatible with available measurements). In the “Idealized experiments” subgroup, sensitivity of Hg levels and trends to a driver or parameterization is assessed by replacing it with a hypothetical option. For example, to test the importance of accurate speciation in emission inventories, an extreme scenario could be simulated where all anthropogenic Hg is emitted as Hg(0).

v. The “Future scenarios” group (A6.1–A6.3, O5.1–O5.3) estimates future changes of Hg levels over the considered period (2020–2050) using available scenarios of anthropogenic emissions and projected environmental conditions and ascribes the estimated changes to various anthropogenic and environmental drivers.

Changes in primary anthropogenic emissions and releases and environmental conditions (physical and biochemical) form the two groups of forcings (or drivers) responsible for changes or trends in environmental Hg levels through their influences on Hg processes (i.e., Hg transport, biochemistry, and flux exchanges). In the proposed experimental design, the contributions of these two groups of drivers are first separated via the “nature simulation” (using coupled A3a.1 and O3.1), where primary anthropogenic emissions are kept fixed at the 2010 level and the Hg trend is solely driven by natural factors. The anthropogenic contribution is then further divided between contributions of ASGM (A3a.2), Minamata Annex D (A3a.3), and other sources. It is important to note
that the contribution of changes in anthropogenic emissions is itself modified by the concurrently occurring environmental changes, which is reflected in these estimates. Isolating individual contributions of changes in various environmental conditions (such as meteorological, land, and ocean parameters) is difficult due to their interdependencies. Nonetheless, the individual effects of changes in a suite of environmental drivers on Hg levels can be examined by selectively allowing temporal variations of these drivers or their impacted Hg processes (e.g., Hg transport and secondary emissions and releases), while keeping all other drivers fixed in perturbation experiments (A3b.1–A3b.7). In addition, proposed idealized experiments (A5.1–A5.10), despite their simplicity, can reveal fundamental response characteristics of the model drivers or parameterizations and inform their potential impacts on future Hg cycling.

Tables F8 and F9 represent an extensive list of potential simulations, yet one must account for time and resource restrictions of participating models. Therefore, the model experiments are ranked in three priority levels (Tables F8 and F9): (1) “Core simulations”, expected to be performed by all global models; (2) “Optional simulations”, expected to be performed by as many models as possible; and (3) “Future simulations”, expected to be conducted as a third set of model simulations following the modeling and analysis of historical Hg levels. Both global and regional models can take part in the simulation program. The time-variable boundary conditions for regional modeling experiments will first be extracted and stored from multimodel simulations of global models. The model output variables (Tables F11 and F12) will be stored in open-data format (netCDF) with appropriate metadata. This includes the definition of new official Climate and Forecast (CF) convention tags for marine Hg variables (Davis et al., 2020) (see Appendix D).

## 5.3 Multimedia mass balance simulations

Given the dominant role of secondary emissions in contemporary Hg levels, quantifying their changes is important for the attribution of observed temporal trends of Hg in recent decades and future projections of environmental Hg cycling. Currently, it is difficult to fully account for the temporal trend in secondary emissions and releases based on observations or 3D Hg models alone. Multimedia mass balance box models have been used to trace the movement of all-time anthropogenic mercury emissions and releases and their influence on Hg levels in environmental matrices (Amos et al., 2015).

Multimedia mass balance simulations (see Sect. 2.4) will serve three primary MCHgMAP objectives: (1) determination of the sensitivity of environmental Hg levels and trends to uncertainty in emission histories and lifetimes of global Hg cycling, (2) attribution of secondary Hg emissions and releases and environmental Hg levels and trends to all time anthropogenic Hg sources, and (3) response of global Hg cycling to future anthropogenic emission scenarios. Mass bal-

ance models serve an additional purpose for the coordinated MCHgMAP simulations, which is to provide the changes in secondary emissions and releases (i.e., legacy emissions and releases) and/or environmental levels driven by historic changes in anthropogenic Hg emissions and releases as inputs to 3D atmospheric and ocean models. Mass balance model simulations represent a global geographic domain and will generally be performed for a period of more than 500 model years (e.g., 1500–2020, baseline simulation). Simulations will produce annual arrays of multimedia reservoir Hg levels and fluxes that can be stored as tables for analysis.

To account for uncertainties of Hg in emissions, reservoirs, and inter-compartmental fluxes, a mass balance model ensemble will be produced (see Fig. 9). Sensitivity simulations can be evaluated against available independent constraints (see below) in a similar manner to Amos et al. (2015). The list of simulations to be performed with the model ensemble is given in Table F10. The legacy component of environmental Hg will be developed and evaluated following the methodology of Angot et al. (2018).

The plan for the construction of revised mass balance model ensemble (steps 1–4 below) and its simulations (steps 5–7 below; Table F10) is as follows:

i. *Update rate coefficients:* update central estimates for rate coefficients based on state-of-the-science review of contemporary Hg reservoir magnitude and exchange flux estimates (from models and observations). Rate coefficients used in baseline steady-state simulations from 3D air and ocean models will be considered.

ii. *Develop the base model* (steps involving model simulation): use a perturbational approach to define the set of rates and historical emissions and releases that are consistent with evaluation metrics (see below).

iii. *Evaluate the model:* the combination of feasible rate coefficients and emissions and releases trajectories will be constrained by model ability to reproduce:

a. Contemporary reservoir magnitudes (e.g., Sect. 4.1 and 4.3)

b. Contemporary fluxes (e.g., Sect. 4.2 and 4.4)

c. Historical trends in environmental archives (Sect. 4.5)

Model evaluation metrics should reflect global magnitudes/trends, which may require rescaling or rebalancing due to differences in regional sampling density of observations.

iv. *Model ensemble:* all box models (i.e., combinations of rate coefficients and emissions and releases trajectories) that successfully reproduce global magnitudes/trends
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Schematic of process for constructing mass balance model ensemble.</figcaption></figure>

(step 3) will compose the model ensemble (i.e., baseline simulation M1; Table F10) used in the following analysis simulations.

v. *Characterize secondary emissions and release trends* (steps involving model simulation): quantify contributions of legacy and recent primary emissions to recent temporal trend (i.e., 2010–2020) in global Hg levels and secondary emissions from the land and oceans (Simulations M2 to M11 in Table F10). Uncertainties in contributions will be bounded using the model ensemble.

vi. *Perform attribution of temporal trends to primary sources* (steps involving model simulation): quantify the fraction of all-time trend in global Hg levels and secondary emissions and releases attributable to specified primary source categories (Simulations M12 to M17 in Table F10).

vii. *Project future effects of policy interventions* (steps involving model simulation): quantify temporal trend in Hg levels and secondary emissions and releases under future anthropogenic emission scenarios (simulation M18 and M19 in Table F10).

Further development of spatially resolved mass balance models (e.g., hemispheric- or ocean-basin-resolved compartments) including Hg speciation would improve the analysis of all-time Hg trends. Importantly, accounting for changes in intermedia exchange rates due to changes in environmental conditions such as oxidant concentrations or precipitation regimes, or changes in terrestrial Hg export to coastal ocean due to accelerated permafrost thaw and erosion is needed to perform sensitivity simulations to quantify response of secondary emissions and releases and reservoir Hg burdens under future climate change scenarios.

# 6 Model evaluation

Evaluation of the MME simulations against observational data is of key interest and relevance both scientifically and to the MC and LRTAP effectiveness evaluations. The primary goal of this model evaluation is to assess the robustness of baseline historical simulations to facilitate observationally constrained attribution of observed changes in global Hg levels to anthropogenic and natural forcings and to predict future Hg levels. The discrepancies between modeling and measured data indicate areas of increased uncertainty and provide directions for further improvement of the models, model inputs (e.g., emission data) or measurement data. Additionally, in the regions where measurements are absent or for the parameters and processes which are not directly measured, the intercomparison of results from different models can provide additional information on the level of uncertainties. To facilitate the intermodel comparison and the evaluation of the modeling results against observations, the spatially resolved variables from 3D models will be interpolated from the model-specific grids to a unified latitude–longitude grid and to the points of measurement sites’ location, respectively. The evaluation of mass balance model historical simulations is discussed in the preceding section (Sect. 5.3). The following sections present the MME approach for the evaluation of recent Hg levels and trends simulated by the 3D atmosphere and ocean models.

## 6.1 Atmospheric models

Comparison of model simulations with measurement data is a standard evaluation procedure applied for various purposes – model development, integrated analysis and evaluation of modeling results, and process studies. A variety of single-model and multimodel studies have used measurements to support research of Hg dispersion on a global scale (Horowitz et al., 2017; Shah et al., 2021; Zhang and Zhang,
2022) or in the polar regions (Angot et al., 2016; Dastoor et al., 2022a, b) to evaluate emission inventories and their trends (De Simone et al., 2016, 2017a; Zhang et al., 2016b; Giang et al., 2018) and to study Hg chemical mechanisms (Shah et al., 2016; Song et al., 2018; Saiz-Lopez et al., 2018, 2020) and other processes in the atmosphere (Gencarelli et al., 2017; Travnikov et al., 2017; Bieser et al., 2017).

A variety of available measurement data will be used for evaluation of the multimodel atmospheric simulations, including ground-based measurements from global and regional networks, aircraft measurements, ship and terrestrial flux measurements. In addition, the simulated concentrations of Hg species, wet and dry deposition, air-surface exchange fluxes and relative contribution of various emission sources and sectors will be compared between models. Table F11 summarizes the output variables to be used in the model evaluation. Different characteristics of atmospheric Hg will be evaluated in both the model-measurement and model-model comparisons, as follows:

- *Spatial distributions* (comparison with ground-based measurements): the comparison will include temporally averaged Hg levels, spatial correlation, and gradients. It is important to evaluate different measured parameters (e.g., Hg(0)/Hg(II) concentrations, Hg wet deposition). This comparison can evaluate the models' atmospheric chemistry and global mass balance of sources/sinks.

- *Temporal trends* (comparison with ground-based measurements): the comparison will include temporal changes at individual sites or average values over different regions. It is important to combine different measured parameters (Hg(0) concentration, Hg wet deposition) since they characterize the relative contributions of global and regional sources. This comparison can provide evaluation of the modeled changes driven by emissions (anthropogenic and legacy) and effects of other drivers (e.g., meteorology, land cover, leaf area index LAI, and sea ice). In addition, the analysis of seasonal variations provides insights into major sources and sinks of atmospheric Hg.

- *Vertical profiles* (comparison with aircraft measurements): the comparison will be focused on vertical distribution of Hg species (Hg(0), Hg(II)) at certain locations. The analysis provides additional insights into free tropospheric processes (Hg chemistry) and the effect of emission sources (e.g., power plants or volcanoes).

- *Air-surface exchange* (comparison with ship and terrestrial flux measurements): this will consist of a comparison of Hg concentrations (Hg(0), Hg(II)) and estimated air-sea exchange fluxes at locations of ship measurements and comparison of Hg dry or throughfall deposition and exchange fluxes at terrestrial sites.

- *Spectral analysis* (comparison with ground-based measurements, model intercomparison): in addition to the traditional model-measurement comparison using scatter plots and statistical metrics (e.g., bias and correlation, $R^2$), spectral analysis will be used to examine and compare model and observed data (Sirois et al., 1995; Bowdalo et al., 2016). This will further help in determining which data characteristics (e.g., seasonal cycles or diurnal cycles) are best reproduced by the models.

The model evaluation is limited by the sparse geographical and temporal coverage of surface observations (particularly, for wet deposition and speciated Hg). Moreover, vertical profiles are only available from limited aircraft observations, mostly of total gaseous Hg (THg) or Hg(0). The model-observation comparison can be used to analyze spatial representativeness of atmospheric Hg observations and improve their networks by identifying the regions of important gaps.

## 6.2 Ocean models

There are few marine Hg model studies evaluating individual models against observations (Zhang et al., 2019c; Kawai et al., 2020; Rosati et al., 2022; Bieser et al., 2023), and so far no ensemble studies have been performed. Here, we propose a framework for the first marine Hg model ensemble study. The study will focus on evaluating individual models and ensemble simulations against observations. For this, we will create a harmonized database including surface measurements and vertical profiles of marine Hg species (the Hg Ocean Observational Database; see Sect. 4.3 and 4.4). Table F12 summarizes the output variables considered for the model performance evaluation. Air-sea exchange fluxes will be investigated together with those calculated by the atmospheric model (Sect. 6.1). Through repeated offline coupling of atmosphere and ocean models, we will investigate whether the flux estimates of the compartmental models converge. For areas and variables where observations are sparse or are not available (see, e.g., Fig. 7), we will conduct an intercomparison evaluation of the individual models to identify the uncertainty range of the model results. For this, hydrodynamic, biogeochemical, and mercury variables will be investigated independently. Identifying disagreements between the models will help to identify major sources of uncertainty and to discover processes that are currently not resolved or are unknown.

The ensemble evaluation will be based on established performance criteria for model and ensemble analysis (Solazzo and Galmarini, 2016), as proposed for use in ocean model evaluation by Bieser et al. (2023). The central metric for model evaluation is the mean squared error (MSE) (Eq. 1).

$$ \frac{1}{N} \sum_{i=1}^{N} (\text{mod}_i - \text{obs}_i)^2 \quad (1) $$
based on the method proposed by Solazzo and Galmarini (2016), the MSE can be broken down into its components bias (accuracy), variance (precision), and covariance (unexplained error), allowing for a more detailed model assessment (Eqs. 2–5). The bias represents the systematic error. It is strongly influenced by model drivers and is the quantity models are typically optimized for. The variance and covariance parts of the error are the most relevant for model developers, as these are direct results of the model itself. The amplitude error (variance) especially can be an indicator of missing or unresolved processes in the model, while the remaining unexplained error (mMSE) can be interpreted as unsystematic fluctuations. This is the least troublesome error as it represents fluctuations in the natural system not resolved by the model.

$$MSE = bias^2 + var(mod) + var(obs) - 2cov(mod, obs) \quad (2)$$

$$MSE = (mod - obs)^2 + (\sigma_{mod} - \sigma_{obs})^2 + 2(1 - r)\sigma_{mod}\sigma_{obs} \quad (3)$$

$$MSE = (mod - obs)^2 + (\sigma_{mod} - r\sigma_{obs})^2 + mMSE \quad (4)$$

$$mMSE = \sigma_{obs}^2 (1 - r^2) \quad (5)$$

Finally, we will use the model quality objective (MQO) (Thunis et al., 2013; Carnevale et al., 2014). The MQO is a statistical measure for the possibility to improve a model given the available observations. This is achieved by including the uncertainty of the observations with which the model is compared. Especially for areas with spare observations and large measurement uncertainties, even a model with a large error might be in a range where it cannot be reasonably improved upon unless more abundant or more precise observations become available. This is relevant as for some Hg species the measurement uncertainty can be in the same order of magnitude as the overall model uncertainty. As no ocean model ensemble studies have been conducted so far, the MCHgMAP exercise will be used to test and further develop these methods into a robust framework that can be used during future EE efforts.

For the MCHgMAP ocean Hg simulations, the initial step will be to identify to what degree Hg species concentrations across regions and times agree with observations. Starting from this base case scenario, the models will be used to investigate the impact of individual drivers on marine Hg cycling and air-sea exchange. We will compare the baseline scenario (O1) to surface observations and vertical profiles for total Hg and individual Hg species to evaluate the ability of the models to reproduce observed spatiotemporal variations. Air-surface exchange will be investigated in unison with the atmospheric models, including the impact of model simulations with alternative air-sea parameterizations (O4.5 and O4.6).

For the ocean model evaluation, there are currently some important limitations. Marine observations for evaluation are sparse (Sect. 4.3 and 4.4). This is especially true for Hg and MeHg in the bottom of the food web (phytoplankton and

zooplankton), but also with regards to water, sediment, and fish. While this is a limitation for this study, the result of the model evaluation will help to identify regions of interest that should be targeted for additional observational campaigns. Further, for the water, plankton, and biota observations that exist, there is little to no information on the seasonality of Hg concentrations.

Further development of a framework for the evaluation and intercomparison of ocean model simulations is recommended to facilitate standard methodologies across EE cycles of the MEAs. Also, results from coupled multi-compartment models should be integrated into the intercomparison evaluation once they become available. Regions of particular interest, identified during the MCHgMAP work, where no or insufficient observations are currently available, should be prioritized when planning research cruises. As marine food web models develop further, more focus should be added to evaluate and compare Hg and MeHg levels in marine biota. This could include the integration of phytoplankton and zooplankton into the Ocean Observational Database (see Sect. 4.3) and the use of Hg concentrations in fish from existing databases for observation-model evaluation (Ammar et al., 2024; Bieser et al., 2023).

# 7 Modeling analysis: effectiveness evaluation

The following sections discuss the analysis of the proposed MCHgMAP MME simulations to address the key questions relevant to the MC and LRTAP effectiveness evaluations (Appendix A, questions 7 to 17). Sections 7.1 and 7.2 describe the analysis approach for the detection and attribution of global and regional spatiotemporal patterns and trends of Hg in the air and ocean. Section 7.3 discusses the development of the global mass balance of the environmental Hg levels and movements based on coordinated multimodel simulations.

## 7.1 Atmosphere

### 7.1.1 Current levels

Levels of Hg concentration and deposition substantially differ from region to region over the globe, being affected by a number of anthropogenic and environmental drivers. These include remoteness from significant anthropogenic and natural sources, prevailing atmospheric flows and circulation, spatial variability of the oxidation potential of the atmosphere, precipitation patterns, and properties of Earth's surface including land cover type and characteristics of vegetation (Obrist et al., 2018). The highest Hg concentration and deposition levels occur in the proximity to emission sources in industrial and highly populated regions as well as in regions with Hg intentional use (ASGM) (Pacyna et al., 2016; UNEP, 2019). Air concentrations are generally higher in the Northern Hemisphere because of the predominant location of
anthropogenic emissions north of the Equator (Travnikov et al., 2017; Horowitz et al., 2017; Shah et al., 2021). Hg deposition is elevated over forest-covered land, in locations with intensive chemical transformations of Hg(0) to oxidized Hg forms, and areas with high precipitation amounts (Dastoor et al., 2022a; Zhou et al., 2021; Zhang and Zhang, 2022). Given the limited geographical coverage of existing measurement networks, which are globally inhomogeneous and mostly restricted to the continents, the model simulations can provide a more complete picture of Hg level distribution over the globe. The MME simulations can be used to develop spatial patterns of Hg concentration and deposition and their changes over the period 2010–2020 and explain these in terms of contributions of different emission sources (anthropogenic, geogenic, secondary) and influences of environmental drivers on Hg processes.

Global distributions of atmospheric Hg concentrations and deposition have previously been studied using both monitoring (e.g., Sprovieri et al., 2016, 2017) and modeling (Chen et al., 2014, 2015; De Simone et al., 2016; Horowitz et al., 2017; Shah et al., 2021; Zhou et al., 2021; Zhang and Zhang, 2022). Detailed model analysis of Hg levels has also been performed on regional scales (Gencarelli et al., 2014; Wang et al., 2014a; Ye et al., 2018a; Xu et al., 2022). Along with single-model studies, several multimodel assessments and intercomparisons have been carried out to provide more reliable information on Hg levels, spatial distribution, and source apportionment (Angot et al., 2016; Travnikov et al., 2017; Dastoor et al., 2022a, b). The Global Mercury Assessment 2018 (UNEP, 2019) provides a comprehensive analysis of Hg atmospheric pathways, transport, and fate on a global scale, including multimodel estimates of Hg concentrations and deposition into various terrestrial and aquatic regions in 2015, evaluation of model ensemble results against available observations, and attribution of Hg deposition to different source regions and emission sectors. Uncertainties of the model-derived source–receptor relationships were estimated by De Simone et al. (2017a) using statistical processing of an ensemble of multiple simulations. In addition, a simplified statistical emulator was developed based on the output of a global transport model to simulate changes in anthropogenic Hg fluxes in a source–receptor manner for evaluation of various emission scenarios (De Simone et al., 2017a, 2021, 2022).

## Spatial pattern detection

The spatial patterns of Hg levels on a global scale and across remote and affected regions will be identified using the baseline simulation of the multimodel ensemble as well as perturbation simulations (Table F8). Simulated maps of Hg concentration and deposition from the baseline simulation (A1) will be analyzed and explained through comparison with spatial patterns of emissions, meteorological parameters (e.g., precipitation), chemical reactants (e.g., Br, O₃,

and OH), and land cover types. This analysis will be quantitatively aided by modeled spatial distributions of individual contributions from wet and dry deposition processes and source contributions from various emissions to the spatial patterns of air concentrations and/or total deposition (Simulations A2.1–A2.6). The analysis will be performed for different years of the period 2010–2020 to reveal their interannual variability and its driving factors. The simulated distributions of the near-surface air concentrations of Hg(0) and Hg wet-deposition fluxes will be evaluated against available observations as annual and seasonal averages. The global maps of Hg levels will also be compared between individual models, particularly for Hg cycling characteristics, which are sparsely measured, e.g., Hg(II) dry deposition and the air–surface Hg(0) exchange flux. The model evaluation and intermodel discrepancies will provide quantitative information on the uncertainty of simulated spatial patterns. Additional insight into the uncertainties of modeled spatial patterns will be gained from sensitivity simulations with alternative or idealized sets of emissions, environmental drivers, and process parameterizations (Table F8, Simulations A4.1–A5.10).

## Attribution of levels

Mercury emitted into the atmosphere is subject to long-range transport and exchange with other environmental compartments. Therefore, Hg levels in the atmosphere and other media consist of contributions of various emission sources distributed worldwide. Source attribution of Hg concentrations and deposition will be performed with a set of the multimodel perturbation simulations “Source contributions” (Table F8, Simulations A2.1–A2.6). These simulations will be carried out for the period 2010–2020 under the same conditions as the baseline (A1), but with emissions of a certain source type zeroed. Subsequent comparison of the results of the “source contribution” perturbation simulations with the baseline simulation will provide individual contributions of particular source types to Hg levels in each year of the period. Furthermore, the influences of interannual variability in meteorology and other environmental conditions on source attribution of Hg levels can be examined by replacing the baseline simulations for 2011–2020 in the attribution analysis with trend perturbation simulations where various emissions are kept fixed at 2010 level but meteorology and other environmental variables are allowed to vary (A3a.1–A3a.4).

The source types considered include all anthropogenic emissions (A2.1), sources regulated by MC (Annex D) (A2.6), emissions from ASGM (A2.5), geogenic emissions (A2.2), wildfire emissions (A2.4), and secondary emissions from land (A2.3) and the ocean. Contribution of the ocean secondary emissions will be estimated by subtraction of all other contributions from the baseline simulation. Direct estimation of the oceanic re-emission source contribution by zeroing its emissions is not feasible because of the bi-directional character of the air–water
exchange of Hg(0), which depends on Hg concentrations in both air and seawater and cannot be decoupled. For this reason, the method applied does not allow tagging contributions of different re-emission sources through the air-water interface but calculates Hg(0) evasion from aquatic surfaces as the leftover difference after attributing all other emission sources.

Intercomparison of the source attribution results obtained by different models will provide information on the uncertainty level of performed estimates. In contrast to the previous multimodel assessment (UNEP, 2019), the proposed simulations will be conducted for the multiyear period that will allow analysis of the temporal dynamics of relative contributions of emission sources to Hg levels in different geographical regions. In addition, following a similar approach to the existing HERMES emulator (De Simone et al., 2020), the output of these simulations could be used to create publicly available statistical emulators of Hg deposition for the multimodel ensemble. Thus, the model data could be flexibly extended to further policy scenarios relevant to the Minamata Convention.

## Limitations and recommendations

Anthropogenic emission inventories play a dominant role in the model-estimated spatial patterns and source attribution of Hg levels. Therefore, the uncertainties in anthropogenic emission data (i.e., geospatial distribution, temporal variation, and chemical speciation) are major factors in limiting the reliability of the modeling results. Additional uncertainties are also introduced by model parameterizations of atmospheric transformations and exchange processes, including atmospheric chemistry and air-surface exchange fluxes. Limited spatial coverage of available monitoring data complicates evaluation of the simulated global patterns of Hg levels. Additionally, limitations of time and computational resources might not allow tracking of emission sources through the air-ocean interface in source attribution experiments using a coupled atmospheric and ocean modeling approach (see Sect. 5.1). Similarly, the MCHgMAP experimental design does not fully track the contributions of various primary emissions across the air-soil interface. Therefore, the source attribution analysis lacks the influence of contemporary emissions on the modification of recycling of Hg between the atmosphere, soil, and ocean. This error is likely insignificant for the air-soil exchange due to the relatively long residence time of Hg deposited in soils (Amos et al., 2013) but might be important across the air-ocean interface.

Continuous updates and refinement of anthropogenic Hg emission inventories including geospatial, seasonal, and annual variations and chemical speciation are recommended for improving the future EEs of the MEAs. Model parameterizations of atmospheric Hg chemistry and air-surface exchange fluxes also require further refinement to reduce uncertainties in spatial patterns and source attribution estimates.

More precise estimation of contributions of contemporary primary emission sources relative to legacy sources requires iterative coupling of atmospheric, ocean, and terrestrial models or fully coupled multimedia Earth system models. Evaluation of the source attribution estimates can also benefit from development and use of the Hg isotopic fractionation modeling tools (Song et al., 2022).

### 7.1.2 Temporal trends

Numerous drivers can affect the trends of atmospheric Hg concentrations and deposition measured at surface stations globally. For example, previous studies have highlighted the role of drivers, including changing regional and global emission trends (Olson et al., 2020; Zhang et al., 2016b), decreasing ocean legacy re-emissions (Soerensen et al., 2012), meteorological variability (Dastoor et al., 2022b), and land use change (Jiskra et al., 2018). By designing simulations that investigate the impact of these individual drivers on atmospheric Hg trends in multiple Hg models, we aim to improve the detection and attribution of Hg trends. The proposed spatially resolved simulations for 2010–2020 can be used to assess the causes of trends at different monitoring stations and to quantify the impact of MC policies on observed Hg trends. In addition, the impacts of non-Minamata Convention factors on observed Hg trends (e.g., meteorology, legacy re-emissions, biomass burning, or land use change) will be identified.

There have been an array of observational studies investigating past atmospheric trends of Hg. At measurement stations in North America and Europe, atmospheric Hg concentrations have generally declined from the 1990s until the present (Custódio et al., 2022; Roberts et al., 2021; Olson et al., 2020; Zhang et al., 2016b; Cole et al., 2014). The trend magnitude varies depending on the station; for example, total gaseous mercury (TGM) declined at Mace Head (1996–2020) by $1.6 \pm 3.9 \% \text{ yr}^{-1}$ (Custódio et al., 2022), for North Atlantic ship cruises (1990–2009) by $2.5 \pm 0.5 \% \text{ yr}^{-1}$ (Soerensen et al., 2012), and at Alert (1995–2011) by $0.9 \pm 0.3 \% \text{ yr}^{-1}$ (Cole et al., 2013). Trends in North America and Europe have been nonlinear over time, with several studies reporting stagnated trends for a period around 2008 (Custódio et al., 2022; Weiss-Penzias et al., 2016; Lyman et al., 2020b). In other regions, atmospheric Hg measurements began later (generally after 2005) and there are fewer stations, leading to more uncertain regional trends. Available atmospheric Hg trends in East Asia show declines over the last decade in the Yangtze River Delta (Tang et al., 2020), Xiamen (Shi et al., 2022), Okinawa (Marumoto et al., 2019), and Taiwan (Nguyen et al., 2019), and the magnitude of the trends is similar to those observed in North America and Europe. On the other hand, constant or increasing trends in atmospheric Hg have been observed recently the Southern Hemisphere sites Cape Point (2007–2017) and Amster-
dam Island (2012–2017) (Slemr et al., 2020) and in Siberia (2011–2020) (Mashyanov et al., 2022).

In contrast to the decline in atmospheric Hg concentrations in recent decades at measurement sites in the Northern Hemisphere (NH), global anthropogenic emission inventories report increases in Hg emissions (e.g., AMAP/UN Environment, 2019). Atmospheric Hg models have been useful tools to assess the drivers of Hg trends and evaluate the accuracy of emission assumptions and biogeochemical cycle parameterizations. Zhang et al. (2016b) conducted GEOS-Chem simulations between 1990 and 2010 and compared with available observations globally, and suggested potential refinements in both the magnitude and speciation of anthropogenic Hg emissions. Since that time, more Hg observations have been collected, continuously providing us with a better picture of global Hg trends. At the same time, trend detection and attribution methods for atmospheric compounds have become more sophisticated (Chang et al., 2021). Feinberg et al. (2024) reanalyzed the observed Hg(0) trend in recent decades and detected a decline in most regions of the NH; applying comprehensive modeling, the authors suggest that the most likely driver of the declining Hg(0) trend in NH is decline in anthropogenic emissions (currently not reflected in global inventories). The factors contributing to the discrepancy between observed Hg(0) trends and global emission inventories will be investigated further using the proposed MME simulations.

## Trend detection

Robust trend analysis methods will be applied to both observed and modeled Hg concentrations and deposition. Multiple trend analysis methods will be considered to maximize the information obtained from observed and modeled time series (Table 3). The most recent modeling study to consider observed trends on the global scale used linear regression methods (Zhang et al., 2016b), while the recent AMAP report applied the seasonal Mann–Kendall test and the Theil–Sen trend estimator to analyze Arctic trends (MacSween et al., 2022). There are more complex approaches available (Table 3) to (1) evaluate nonlinear changes in trends (kernel regression and generalized additive models, GAMs); (2) quantify trends across the statistical distribution of Hg measurements, e.g., the trends in extreme values (quantile regression); and (3) include meteorological covariates in regressions (e.g., temperature and humidity) to remove the influence of meteorological variability from the trend analysis (e.g., Shi et al., 2022), which can be achieved with multiple approaches (e.g., multiple linear regression, GAMs, and quantile regression) (Chang et al., 2023).

We would analyze trends in the baseline simulation A1 (Table F8) from available atmospheric Hg models, comparing these to observations of atmospheric Hg concentrations and deposition for 2010–2020. The baseline simulation would be conducted with drivers (i.e., anthropogenic emissions, legacy re-emissions, oxidant concentrations, and meteorology) that vary temporally over 2010–2020, representing the state-of-the-art knowledge for processes driving Hg cycling. Simulated trends at the grid cells of measurement stations will be extracted to compare with available measurements. We would evaluate the accuracy of the baseline models for capturing measured trends for 2010–2020, using an array of metrics ($R^2$, mean absolute and relative biases, fraction of sites at which a model predicts the correct sign of a trend). The comparison of trends can be summarized for available observation media (Hg(0), THg, and Hg wet deposition) and specific regions where measurements are available.

Trends would also be compared between individual Hg models in the baseline simulation. In this analysis, we would not be limited to analyzing only areas where measurement stations are available. Modeled trends in atmospheric Hg concentrations will be calculated globally, over each hemisphere, and for all HTAP regions. Trends in Hg fluxes (e.g., dry deposition over land, wet deposition, and ocean-air Hg exchange) will be compared between models. Since all models will conduct simulations with harmonized emission datasets, differences between modeled trends will be attributed to other environmental drivers which will not be harmonized between models (i.e., meteorology, land surface parameters) and the individual parameterizations of Hg cycling. In addition, each individual model can conduct sensitivity simulations (A4.1–A5.10 in Table F8) using alternative anthropogenic and secondary emissions and parameterizations to diagnose the impact of these drivers on trends. This combination of intermodel and inter-simulation analysis will demonstrate the sensitivity of Hg trends to structural and parametric uncertainties in available Hg models. By comparing model temporal trends across global regions, uncertainty estimates in modeling results will be calculated and scientific gaps in modeling will be identified.

## Attribution of trends

The proposed list of multimodel ensemble simulations would allow for attribution of Hg trends to individual emission sources and environmental drivers (Simulations A3a.1–A3b.7; see Table F8). For example, we will conduct a 2010–2020 simulation with fixed anthropogenic emissions at 2010 values (Simulation A3a.1). By comparing this simulation to the baseline simulation (A1, where anthropogenic emissions vary for 2010–2020), the influence of anthropogenic emission changes on Hg trends can be quantified. This attribution analysis can be conducted at different spatial scales (globally, hemispherically, regionally, and at locations of measurement stations) to better quantify the role of anthropogenic emissions in observed Hg trends.

Similar to the exercise proposed above for Simulation A3a.1, other drivers can be investigated by conducting simulations with 2010 fixed values. Simulations could be con-
<table><caption>**Table 3.** Trend analysis methods that could be applied to observed and simulated Hg time series.</caption><thead><tr><td>Method</td><td>Description</td><td>Advantages</td><td>Example study for Hg</td></tr></thead><tbody><tr><td>(Multi)linear regression</td><td>Assumes a linear relationship between dependent variable (e.g., Hg(0)) and independent variable(s), e.g., time and/or other covariates. The ordinary least squares loss function is minimized to calculate mean estimates for regression coefficients.</td><td>– Simple to interpret<br/>– Can consider covariates</td><td>Zhang et al. (2016b)</td></tr><tr><td>(Seasonal) Mann–Kendall test and Theil–Sen trend estimator</td><td>Robust non-parametric method to calculate median trend, by considering the median difference between each pair of observations.</td><td>– Does not require normally distributed data<br/>– Simple to interpret</td><td>MacSween et al. (2022)</td></tr><tr><td>Generalized additive models (GAMs)</td><td>The observations are modeled as a sum of smooth functions of dependent variables, with regularization of functions used to avoid overfitting.</td><td>– Flexible for nonlinear trends<br/>– Can consider covariates</td><td>Shi et al. (2022); Wu et al. (2020)</td></tr><tr><td>Quantile regression</td><td>The trend in different parts of the observed distribution (i.e., quantiles) can be calculated.</td><td>– Analyzes trends across distribution (e.g., extreme or baseline values)<br/>– Can consider covariates</td><td>Feinberg et al. (2024)</td></tr><tr><td>Kernel regression</td><td>The observations are modeled non-parametrically using smoothing kernel functions</td><td>– Can calculate nonlinear trends</td><td>Custódio et al. (2022)</td></tr></tbody></table>

To quantify the impacts of ASGM emissions (A3a.2), Annex D anthropogenic emission sources (A3a.3), and geogenic emissions (A3a.4). These sensitivity simulations would illustrate the roles of these diverse drivers in Hg trends between 2010 and 2020. Among other uses, the model products could be used to map the principal driver influencing Hg trends for each individual grid cell globally.

Effects related to the environmental conditions are difficult to disentangle, as many of the individual drivers ( meteorology, land surface conditions, and secondary emissions) are intertwined. Additional optional simulations will probe the effects of changes in various environmental drivers on trends for diagnostic purposes. For these simulations, we will vary one driver between 2010–2020 and keep all other drivers fixed at 2010 levels. Proposed simulations will consider the effect in 2010–2020 trends of varying transport (A3b.1), wildfire emissions (A3b.2), terrestrial secondary emissions (A3b.3), ocean Hg concentrations (A3b.4), land surface conditions (A3b.5), chemical reactants (A3b.6), and anthropogenic emission speciation (A3b.7). The contributions of anthropogenic emission changes from different HTAP regions will be explored with Simulations A3c.1–A3c.11.

The model intercomparison will highlight whether the influence of drivers is comparable between the different models. Also, by analyzing the role of different trend drivers, we will be able to better understand differences in the modeled baseline trends and discrepancies between the models and observations. The previous global multimodel Hg study focused on an individual simulation year (Travnikov et al., 2017). Therefore, the transient 2010–2020 intercomparison simulations present a novel opportunity to evaluate the variety of model responses to changing Hg emissions and environmental drivers. In this way, MCHgMAP initiative will refine our understanding of the drivers of recent Hg trends and help with designing further simulations in subsequent EE efforts to isolate the impacts of MC and LRTAP activities on Hg trends.

## Limitations and recommendations

Currently, the key limitations adding uncertainties to modeling trend analysis are the scarce information available regarding the trend in anthropogenic emissions from certain sources (e.g., for ASGM), geographically limited observations (mainly from North America and Europe) that cover
the full 2010–2020 time period, and a lack of field or laboratory studies investigating the response of Hg fluxes (deposition and secondary emissions) to changes in environmental drivers. For example, the impact of land cover and vegetation properties on Hg uptake depends on model parameterizations of Hg(0) dry deposition calibrated to available field measurements (Zhou et al., 2021; Feinberg et al., 2022). However, there are few measurements of Hg vegetation uptake in tropical forests with high biological productivity, including no measurements in tropical regions of Asia or Africa. Although the proposed 10-year simulations would be longer than any previous Hg multimodel activity, the relatively short time period presents challenges to trend analysis owing to the role meteorological interannual variability in driving trends over shorter time periods as shown in studies using mechanistic modeling (Dastoor et al., 2022b) and statistical approaches (Shi et al., 2022; Wu et al., 2020).

A long-term Hg measurement record that is both regionally representative and process-representative and improved reporting of Hg emissions from various sectors can improve trend analysis in future EE efforts. Inverse models have been used in different atmospheric chemistry applications to calculate temporally and spatially varying emission fields using Bayesian inference and available observations (Bousquet et al., 2011; Park et al., 2021). Inverse modeling could aid in diagnosing changes in anthropogenic Hg emissions driven by the Hg MEA policies. However, until now, inverse modeling applications for Hg cycling have been limited in scope, both spatially and temporally (Song et al., 2015; Denzler et al., 2017). In the future, a diverse set of inverse Hg modeling methods could be applied to available atmospheric observations in order to produce estimates of Hg emission trends and their uncertainties.

## 7.2 Ocean

### 7.2.1 Current levels

The principal source of oceanic Hg is atmospheric deposition, air-sea exchange of gaseous elemental Hg(0), river input, and coastal erosion (Dastoor et al., 2022a; Amos et al., 2014; Mason and Fitzgerald, 1996). Much of oceanic Hg input is of anthropogenic origin (Driscoll et al., 2013), since the anthropogenic influence on global Hg cycling is significant. However, the response (time and level) of oceanic Hg levels to ongoing changes in anthropogenic emissions is currently uncertain. In addition to ongoing anthropogenic emissions, the oceanic response time is highly dependent on contributions from natural (geogenic) and historical (anthropogenic legacy emissions) sources (Zhang et al., 2014b). Thus, the design of MME simulation experiments needs to include ocean models that can track the contributions of different anthropogenic and geogenic sources of Hg emissions to water column Hg levels at observational sites and across global regions. The attribution of Hg emissions from different sources

can be traced in several classes: geogenic, anthropogenic and secondary (separated by land, ocean, and wildfires); different anthropogenic Hg emission sectors; and anthropogenic Hg emission sources influenced by the MEAs.

Several previous modeling studies have focused on the impact of anthropogenic emissions on oceanic Hg levels (Chen et al., 2018; Corbitt et al., 2011). However, no 3D bio-geochemical ocean model has been used to source-track Hg emissions over historic periods. Corbitt et al. (2011) used a tagged version of the GEOS-Chem model to track the fractional contribution of sources of atmospheric deposition to the surface ocean. Several previous modeling studies have examined the trends in evasion of Hg(0) from the oceans and their controlling factors: (i) global numerical models (Soerensen et al., 2010; Zhang et al., 2019c; Huang and Zhang, 2021), (ii) regional models (e.g., Fisher et al., 2013; Bieser and Schrum, 2016), and (iii) global box models (Mason and Sheu, 2002; Sunderland and Mason, 2007; Amos et al., 2013; Chen et al., 2018). It is estimated that the surface ocean Hg concentrations and its evasion into the atmosphere has increased by a factor of 3–5 since preindustrial times (Outridge et al., 2018; Zhang et al., 2014a; Huang and Zhang, 2021). Zhang et al. (2014b, 2016b) tracked the perturbation by anthropogenic Hg emissions in the past 600 years using a 3D ocean model and found that in surface waters 77% of the Hg is of anthropogenic origin. Overall, modeling suggests that most of the Hg(0) being evaded comes from Hg that was recently added to the ocean (60%–70%), and that deeper water Hg sources are therefore a smaller component of the flux (Soerensen et al., 2010). Sunderland and Mason (2007) found that the contemporary ocean is not in a steady state with present-day atmospheric Hg inputs. Hg concentrations will therefore continue to increase, on average, if present-day emissions remain constant. If the ocean were to reach a steady state with present-day atmospheric Hg inputs, anthropogenic Hg enrichment would increase to ~80% in the surface ocean and >150% in the deep ocean. Two-thirds of the present Hg fluxes (such as deposition on land and the ocean) are directly or indirectly of anthropogenic origin (primary or legacy anthropogenic emissions). Elimination of the anthropogenic load in the ocean and atmosphere will take hundreds of years if not more after the termination of all anthropogenic emissions (Amos et al., 2014).

### Spatial pattern detection

The global distribution of aqueous mercury species in the ocean is determined primarily by the global patterns of (a) deposition, governed by location of emissions, atmospheric transport and reduction potential, (b) primary productivity, which affects the conversion of aqueous Hg(II) to Hg(0) and MeHg, (c) settling of Hg sorbed to organic particles, and (d) upwelling/resuspension of sedimented Hg. Gaining a comprehensive understanding of Hg levels and spatial patterns in the global ocean, especially in sensitive
regions, is critical for accurately assessing the risk of human exposure and the reliability of the MC EE.

Moreover, deposition plays an important role in the dis-
tribution of Hg. Deposition is high in the western North
Atlantic and western North Pacific, which are downwind
of large industrial regions of the eastern US and East Asia
(Strode et al., 2007). Due to increasing emissions from Asia
and decreasing emissions from the US, deposition to the At-
lantic has decreased while increasing in the Pacific over the
last decades. These key regions, including the tropics, west-
ern North Atlantic and western North Pacific, should there-
fore be treated as “affected” by anthropogenic emissions.
Also, for the tropics not located downwind of source areas,
model simulations of Hg in the open oceans show elevated
concentrations because of the prominent atmospheric depo-
sition due to high precipitation in the Inter-Tropical Con-
vergence Zone (ITCZ) in combination with increased atmo-
spheric oxidation rates producing Hg(II) for dry deposition
(Zhang et al., 2019c; Soerensen et al., 2014). While there
are no large point sources of Hg in the Arctic, atmospheric
transport of Hg(0) to this region still results in a significant
anthropogenic impact to the Arctic Ocean (Schartup et al.,
2022). Although considered as a “remote” region, Hg levels
in the Arctic Ocean are therefore very sensitive to anthro-
pogenic Hg emissions (Dastoor et al., 2022a). In the future,
large quantities of Hg from thawing permafrost might be re-
leased into the Arctic.

The baseline simulation (O1, Table F9) aims to obtain
global levels and spatial patterns of oceanic Hg utilizing
state-of-the-art model configurations to perform global-scale
simulations of oceanic Hg transport. The oceanic models in-
corporates boundary conditions (i.e., atmospheric Hg(0) con-
centrations, Hg(II) deposition fluxes, and Hg from rivers)
from atmospheric and mass balance model results and
present-day ocean biogeochemistry fields from ocean mod-
els such as Zhang et al. (2020) and Bieser et al. (2023). Due
to the sparse nature of marine observations in general, and
the lack of cruises during the stormy season and an absence
of repeated (interannual and intra-annual) measurements in
particular, a comprehensive evaluation of spatial patterns can
only be achieved by combining observations and models.
Here, the ensemble approach will help to reduce the uncer-
tainty introduced by the models through the identification of
regions of (dis)agreement between different models.

Attribution of levels

To comprehensively evaluate the contributions of various
Hg sources to oceanic Hg levels and spatial patterns, sim-
ulations will be conducted using available ocean Hg models
(O2.1–O2.6; Table F9). The contributions of geogenic and
anthropogenic Hg emission sources (O2.1–O2.2) to oceanic
total Hg concentrations at observation sites and across global
regions will be determined by performing oceanic simula-
tions using Hg deposition inputs from the respective atmo-

spheric MME experiments (see Sect. 7.1.1). The oceanic
models will be used further to identify the relative contribu-
tions of different anthropogenic emission sectors to oceanic
Hg levels, such as the contributions of anthropogenic emis-
sions from sources influenced by the MC (O2.5–O2.6). To
account for non-atmospheric emission sources, the relative
contribution of riverine input will be assessed using sim-
ulations with riverine emissions from Liu et al. (2021b)
and without riverine export (O2.3). An additional exper-
iment (O2.4) will investigate the contribution of Hg and
MeHg bioaccumulation in marine biota in the lower food
web (phytoplankton and zooplankton) to oceanic Hg distri-
bution and air–sea exchange by disabling food web interac-
tions.

Limitations and recommendations

A major limitation is the sparse availability of marine mea-
surements of speciated Hg with large parts of the ocean still
unsampled. From a model perspective, the chemical mecha-
nisms for methylation and demethylation processes are still
not understood. In the coastal ocean, the complex interac-
tions between Hg and other components in river water and
sediment, such as dissolved organic matter and suspended
particles and biota have yet to be fully reproduced by a
model. Assumptions made about key parameters in oceanic
Hg biochemistry models introduce errors in the estimation of
Hg cycling in the transition zone from freshwater to marine
waters (e.g., change in pH).

Another limitation of current global ocean models is that
they do not accurately represent coastal dynamics. Eddies
are dissipated at much smaller scales than the grid resolu-
tion in the turbulent boundary layers, and advection schemes
governed by turbulent mixing cannot be captured precisely
by these coarse-resolution ocean models (Sarmiento et al.,
2004; Wunsch and Ferrari, 2004). Furthermore, these mod-
els cannot adequately reflect transport and mixing conditions
in the continental shelves as global models usually lack tides,
which are major drivers of coastal mixing. Higher-resolution
or unstructured grid models are necessary for capturing the
complex processes at the land–sea–air interface. Here, we
will perform additional runs evaluating the impact of model
resolution. Models with unstructured grids will be used to
selectively increase model resolution in coastal areas (Loge-
mann et al., 2021). Furthermore, for the estimation of differ-
ent source origins of oceanic Hg levels and their re-emission,
there are computational problems with conducting historic
(decennia or millennia) 3D model simulations, including
source–receptor relationship tracking. For future EE efforts,
we recommend that model simulations (mass balance mod-
els and fully coupled 3D models) with a focus on tracking
Hg source origins (geography- and time-dependent) be de-
veloped further, building on the work of Corbitt et al. (2011),
Amos et al. (2013), and Chen et al. (2018).
## 7.2.2 Temporal trends

The relevance of detecting and attributing changes in the concentration and distribution of Hg in the marine environment over time to the MC on Hg is significant. This can be accomplished using a combination of observational data, modeling studies, and statistical analyses. Estimates of temporal trends from observational data are not feasible due to the lack of long-term stations and sporadic cruising activity. However, ocean Hg models can be effective tools for addressing these gaps in our understanding of temporal trends in Hg levels in the ocean. Understanding the temporal trends provides a way to directly monitor progress towards reducing exposure to MMHg and other public health and the UN Sustainable Development Goals related to Hg pollution, particularly in coastal and indigenous communities that rely on fish and seafood as a primary food source. It is also essential for identifying changes in sources and pathways of ocean Hg levels, which can inform strategies for reducing anthropogenic Hg emissions and protecting human and ecosystem health.

Ocean Hg models have emerged as a powerful tool to provide valuable insights into the drivers and impacts of Hg pollution in the ocean over time. For example, 2D slab models of the oceanic mixed layer employed to mainly focus on air-sea exchange (Strode et al., 2007; Soerensen et al., 2010), only include inorganic Hg chemistry and transportation and use GEOS-Chem model results of atmospheric Hg. These are mainly concerned with seasonal temporal variability. Multi-box biogeochemical model (Soerensen et al., 2016a; Amos et al., 2013) are also developed to simulate Hg budget and response to emission inventory. Combined with projections of future anthropogenic Hg emission scenarios, Giang and Selin (2016) used Amos et al. (2013) and Schartup et al. (2022) used Soerensen et al. (2016a) to assess future changes in Hg levels. Three-dimensional biogeochemical models including ecosystem interactions are increasingly applied to simulate the spatiotemporal dynamics of Hg species in the ocean including bioaccumulation in the marine food web (Zhang et al., 2020; Rosati et al., 2022; Bieser et al., 2023; Zhu et al., 2023).

### Trend detection

It is currently not possible to estimate temporal trends of oceanic Hg from observational data due to the lack of long-term monitoring and only sporadic cruising activity. Ocean Hg models are potent tools for filling the gaps in our knowledge of temporal trends of Hg levels in the ocean. Some models perform offline simulations for a certain amount of time with repeated external forcing from rivers and atmospheric deposition (Zhang et al., 2020), or fixed field (Rosati et al., 2022) or archived field (Bieser et al., 2023). Others are coupled to an atmospheric model using a monthly, weekly, or even hourly (Zhang et al., 2014b, 2019c) interval for data exchange. To capture the temporal trends associated with

changes in emissions, the atmosphere-ocean coupled model simulations (discussed in Sect. 5) might provide a more justifiable time-dependent simulation than previous offline ocean model simulations.

Trends in the baseline simulation (O1; Table F9) of oceanic Hg models will be examined and compared to available measurements of Hg levels (e.g., THg or MMHg) in the ocean for 1980-2020 (when data are available, keeping in mind that these are sparse, especially for the early decades, and that clean sampling techniques had not always been introduced in early observation campaigns: see Sects. 4.3 and 4.4). The baseline simulation will be forced with upper-boundary conditions (i.e., atmospheric Hg concentrations and deposition fluxes) from atmospheric and mass balance models and ocean biogeochemistry fields from present-day situations to capture short- and long-term changes in oceanic Hg levels. The ocean physics field that is used to drive the transport of Hg in the models varies temporally over 1980-2020. We will extract simulated trends for those measurement locations where observational data spanning several years have been built up and compare them with the available measurement data. Statistical metrics such as $R^2$ in regression analysis and the model performance evaluation criteria described in Sect. 6.2 will be applied to assess whether the baseline simulation can recapitulate the observed trend direction for the period 1980-2020.

Similar analysis will be applied to compare trends across multiple oceanic Hg models in the baseline simulation. The analysis will not be constrained to areas where measurements are available. Global trends will be calculated, along with trends for different regions and all oceans, rather than specific locations with observations. Differences in trends for Hg fluxes, such as sea-air exchange, particle sinking, and sedimentation will also be compared across different models. Through this comparative analysis, any differences or similarities in trends will be identified, providing insights into the underlying processes that drive these trends.

### Attribution of trends

The proposed trend analysis perturbation simulations (O3.1-O3.14, Table F9) will enable the attribution of Hg trends to emission sources and environmental conditions in the ocean. The trend attribution analysis can be performed at different spatial scales (globally, hemispherically, regionally, and at observation locations). Perturbation simulation O3.1 allows for the separation of the impacts of changes in anthropogenic emissions and environmental factors. A 2010-2020 simulation will be conducted to examine the effect of changing meteorological conditions on oceanic Hg by keeping all other factors fixed to 2010 while changing the meteorological conditions. Furthermore, Simulations O3.3-O3.14 will estimate the influences of changes in anthropogenic emissions in various global regions on oceanic Hg trends. Comparison across multiple models will indicate whether the impact of drivers
is similar among the models. Resolving the role of different
drivers also provides an opportunity to better interpret biases
between simulated baseline trends and observations, together
with model differences. Through this attribution exercise, we
aim to improve our understanding of the drivers that are re-
sponsible for the observed Hg trends in the ocean. This will
aid in the design of future EE cycles, which will focus on
isolating the impacts of Minamata Convention activities on
Hg trends.

Limitations and recommendations

As the ocean has a much slower turnover time compared to
the atmosphere, a time span of 10 years might be insuffi-
cient to detect certain trends at a significant level. This is
especially true for Hg(II) methylation and bioaccumulation
trends. Several Hg biogeochemical processes such as methy-
lation or demethylation and sorption or desorption to organic
material remain highly uncertain. Without proper parameter-
izations of these processes, it is difficult to accurately cap-
ture and attribute temporal trends. Finally, in this study in-
dependent models for atmosphere and ocean will be coupled
through sequential iterations (see Sect. 5.1). In future EE ef-
forts, fully interactive multimedia models should be devel-
oped and applied.

7.3 Environmental mass balance

The mass balance of budgets between environmental Hg sources (anthropogenic and natural), burdens, movements, and burial provide a quantitative summary of the global Hg cycling. The mass balance implies that changes in Hg levels in any environmental compartment of the biosphere over time would result in compensating changes in other interacting compartments. Thus, assessment of mass balance is useful for explaining the linkages between temporal trends of Hg in environmental matrices. Further, the knowledge of relative magnitudes of Hg budgets provides insight into how curbing anthropogenic Hg emissions under the MEAs is likely to affect Hg levels in different media.

Given the biogeochemical complexity and sparseness of
Hg observations, global Hg budgets have been commonly
reported based on 3D and multimedia mass balance model
simulations using compilations of anthropogenic and natu-
ral Hg emissions (Outridge et al., 2018). However, increased
observations during the last decade have enabled some obser-
vational budgets, notably for terrestrial–atmosphere Hg ex-
change (Sommar et al., 2020; Zhou et al., 2021; Zhou and
Obrist, 2021) and marine Hg burden (Bowman et al., 2020).
With increasing number and accuracy of models, modeling
estimates of global atmospheric Hg budgets have been suc-
cessively revised (Selin et al., 2008; Amos et al., 2013; Song
et al., 2015; Cohen et al., 2016; Horowitz et al., 2017; Shah
et al., 2021; Kawai et al., 2020; Zhou et al., 2021; Fein-
berg et al., 2022; Zhang and Zhang, 2022; Sonke et al.,

2023). For example, updated understanding of vegetation and
soil uptake of Hg(0) led to the revision of Hg(0) deposi-
tion budget to land up from 1200 (Horowitz et al., 2017)
to 2850 ± 500 Mg yr⁻¹ (Sonke et al., 2023). The Hg mass
balance for global ocean has been developed using 3D and
mass balance models (Sunderland and Mason, 2007; Seme-
niuk and Dastoor, 2017; Zhang et al., 2019c), including in-
teractions with marine biota (Zhang et al., 2020). On the re-
gional scale, a combination of observations and models have
been used to estimate Hg mass balance (Arctic: Soerensen et
al., 2016a; Sonke et al., 2018; Petrova et al., 2020; Dastoor
et al., 2022a; Mediterranean: Cossa et al., 2022; China: Feng
et al., 2022a).

Reported global Hg budgets thus far are mostly based on
individual modeling estimates. However, a combination of
MME and observed estimates have been used to develop Arc-
tic Hg mass balance (Dastoor et al., 2022a), which led to im-
proved understanding of Arctic Hg cycling and key knowl-
edge gaps in the region. In this study, global environmental
budgets of total Hg (THg) (i.e., Hg anthropogenic and natural
emissions and releases, loadings, and inter-media exchange
and burial fluxes) will be developed based on the coordinated
baseline MME simulation; budget details such as exchange
fluxes for different land cover types and Hg speciation will
be added. Since human exposure to Hg toxicity is domi-
nated by the consumption of seafood, mass balance budgets
of both THg and MeHg (the Hg form that bio-magnifies in
food chains) in the global ocean will be developed. Range of
uncertainties in global Hg budgets will be determined based
on updated synthesis of monitoring data and multimodel ex-
periments examining sensitivity to uncertainties in emissions
and physical and biogeochemical processes.

The Hg mass balance uncertainty estimates developed in
this study will help prioritize Hg monitoring and research ar-
eas. While there has been progress on observations of global
air-vegetation-soil flux exchange, air-cryosphere and air–
ocean–sediment Hg exchanges remain understudied. Hg sta-
ble isotopes studies have led to novel constraints on speci-
ated Hg pathways in terrestrial ecosystems (Enrico et al.,
2016; Obrist et al., 2017; Zhou et al., 2017; Yuan et al.,
2019b); these methods can be further utilized to reduce er-
rors in flux estimates of other matrices. Understanding the
role of legacy Hg recycling is important for evaluating the
recovery of environmental Hg under the MC implementa-
tion (Amos et al., 2013, 2015). Global-change-induced alter-
ations in processes such as net primary production, wildfires,
permafrost and sea ice cycles, and river discharge can sig-
nificantly shift the global Hg mass balance (Denkenberger et
al., 2012; Hsu-Kim et al., 2018; Schaefer et al., 2020). Diag-
nosing recent perturbations of global Hg mass balance due to
climate change stressors is currently difficult due to a lack of
long-term observations in key environmental matrices. De-
velopment of past Hg records from environmental archives
and mechanistic 3D atmosphere–land–ocean biogeochemi-
cal Hg models would help in constraining the contemporary
Hg mass balance and modeling its future trajectory under
global change.

8 Modeling analysis: process understanding

Mechanistic models integrate available knowledge of Hg sources and biogeochemical processes of environmental matrices to estimate Hg levels. Spatiotemporal inconsistencies of modeled Hg levels with observations suggests inaccuracies or knowledge gaps in primary drivers (emissions and releases and environmental conditions) and processes of Hg and their model representations. Sensitivity of modeled Hg levels to these inaccuracies or knowledge gaps can be diagnosed using model simulations and statistical analysis, guiding further development of Hg models, emission inventories, and field and laboratory studies investigating environmental Hg processes.

Limited progress on large-scale process-based terrestrial-
hydrological modeling is recognized as an outstanding sci-
ence gap here. Further research to monitor and model ter-
restrial Hg is critical for determining the lifetime and at-
tribution (to anthropogenic and natural sources) of Hg in
major terrestrial compartments and its coupling with atmo-
spheric and marine Hg. Bishop et al. (2020) reviewed re-
cent advances in terrestrial Hg cycling and recommended in-
creased routine analyses of terrestrial Hg and process studies
using isotopes and micrometeorological and microbial tech-
niques to characterize spatiotemporal variations across the
atmosphere-terrestrial-water interface. Also, increased mea-
surements of Hg levels and mobilization properties at con-
taminated sites (e.g., industrial and mining operations, in-
cluding ASGM) are required to adequately represent their
role in releasing Hg into the atmosphere and global sur-
face waters. Sonke et al. (2023) recommended monitoring
of Hg concentrations in major regional rivers and their tribu-
taries to assess the integrated influences of global change on
terrestrial Hg cycling. In the following sections, atmospheric
and oceanic model simulations probing Hg process under-
standing are discussed.

8.1 Atmospheric models

Previous modeling studies have explored the influence of
anthropogenic forcing on the simulation of atmospheric
Hg concentration and deposition. Uncertainties in atmo-
spheric Hg emission inventories impact the prediction of
source-receptor relationships of Hg (De Simone et al., 2016,
2017a), which are important for estimating the impacts of
MC policy on the global distribution of Hg deposition. Pre-
vious studies have used models to evaluate Hg emission in-
ventories against observational evidence (Hg air concentra-
tions and deposition). The impacts of Hg speciation (Hg(0)
vs. Hg(II)) in emission inventories was analyzed by Bieser
et al. (2014), Zhang et al. (2016b), and Giang et al. (2018),

finding that accurate representation of emission speciation
is crucial for capturing the impacts of Hg emission reduc-
tions on atmospheric Hg concentrations and deposition. The
impacts of other types of anthropogenic forcing (e.g., land
use change) have been less frequently investigated. Previous
generations of models showed limited impacts of future land
cover and vegetation change on atmospheric Hg (Zhang et
al., 2016a), yet these models likely underestimated the im-
portance of Hg(0) dry deposition in the global Hg budget
(Zhou et al., 2021; Feinberg et al., 2022). A recent modeling
study implied that land use change impacts on atmospheric
Hg deposition can be on par with direct anthropogenic emis-
sion impacts (Feinberg et al., 2023). The choice of other in-
put driver datasets can play a role in simulated Hg cycling,
including the choice of meteorological input data (Dastoor et
al., 2015; Giang et al., 2018).

The prediction of atmospheric Hg cycling depends not
only on the input driving datasets, but also on the design
of the model parameterization of Hg biogeochemistry. There
are several published studies investigating the impact of the
Hg chemical scheme on the spatial distribution and season-
ality of Hg concentrations and deposition (Saiz-Lopez et
al., 2020, 2018; Ye et al., 2018b; De Simone et al., 2017a;
Travnikov et al., 2017). The parameterizations of air-surface
exchange also play an important role in the emissions and
fate of atmospheric Hg. Zhang et al. (2019c) tested five
parameterizations of air-sea Hg(0) exchange, determining
that global net Hg(0) emissions range between 2840 and
3710 Mg yr⁻¹, with strong regional differences between pa-
rameterizations. The choice of Hg(0) dry-deposition param-
eterization can strongly impact the magnitude of Hg(0) up-
take by vegetation (Zhou et al., 2021; Feinberg et al., 2022).
Even with the same model parameterizations, the horizontal
resolutions of Hg models can impact the simulation of wet
deposition due to finer-resolution models being better able to
capture deep convection (Xu et al., 2022; Zhang et al., 2012).

By constructing simulations in a diverse set of atmospheric
Hg models with updated harmonized boundary conditions,
we can identify current knowledge gaps in the biogeochem-
ical Hg cycle and test key uncertainties in available models.
Sensitivity and uncertainty simulations will provide further
insight into the drivers of Hg trends and spatial distribution
and inspire further developments to improve cause-effect re-
lationships in Hg cycling.

MCHgMAP will explore the impacts of several anthro-
pogenic and environmental forcing factors on Hg cycling. In
addition to the previously discussed baseline (A1) and trend
analysis (A3a.1–A3b.7) simulations for 2010–2020, an ar-
ray of atmospheric model simulations is proposed to test the
influence of alternative emission datasets and parameteriza-
tions (A4.1–A4.8). Additional idealized sensitivity simula-
tions (A5.1–A5.10) are proposed to investigate the sensitiv-
ity of Hg model budgets in a single year (2015) to individual
processes. This set of simulations is idealized in the sense
that rough perturbations are imposed on drivers (e.g., A5.1:
all Hg emissions are released as Hg(0)) or model parameters (e.g., A5.3: enhancing Hg oxidation rates by +50%). These experiments can probe whether existing models differ in their sensitivities to individual drivers or biogeochemical parameters, enabling the prioritization of key uncertainties for future EE efforts. The idealized experiments can also explore potential impacts of future global change, e.g., increases in LAI due to global greening (A5.8) or the disappearance of sea ice (A5.10).

Currently, there are limited options for anthropogenic Hg emission inventories, especially continuous time series using a consistent methodology covering the years 2010–2020 or longer (see Sect. 3.2.1), restricting the ability to investigate emission uncertainties and sensitivity to modeling results. Chemical mechanisms differ among the models because of knowledge gaps and uncertainties in atmospheric Hg and Br chemistry, but the interpretation of differences in model-simulated chemistry is made more difficult due to limited observations of speciated Hg and potential biases in these observations (Lyman et al., 2020a). Finally, the sensitivity analysis performed with atmospheric models decoupled from the aquatic and terrestrial compartments does not account for changes in Hg levels in other environmental media affecting secondary emissions to the atmosphere. The internal consistency of this analysis would be improved by Hg simulations in Earth system models (e.g., Zhang and Zhang, 2022), yet the computational expense of dynamically coupled models implies that atmosphere-alone sensitivity simulations are worthwhile for probing process uncertainties.

## 8.2 Ocean models

Previous single model process studies have investigated (1) the impact of riverine Hg loads (Fisher et al., 2012; Soerensen et al., 2012; Zhang et al., 2015b), (2) the role of organic matter in marine Hg cycling and methylation (Soerensen et al., 2016a), (3) air–sea exchange (Bieser and Schrum, 2016; Zhang et al., 2023a), (4) the impact of nutrients on methylation and bioaccumulation (Soerensen et al., 2016b), and (5) the impact of adding interactions with the marine ecosystem (Zhang et al., 2019c; Rosati et al., 2022; Bieser et al., 2023). However, so far, no systematic model intercomparison or ensemble study has been performed for oceanic Hg cycling. MCHgMAP will, for the first time, use a set of marine Hg models driven by harmonized input data and model parameterizations. Thus, the baseline (O1) scenario (Table F9) will be used to quantify the range of currently available models (Table F2) based on harmonized input data and process parameterizations (Table F6). So far it is unknown how marine Hg models compare and whether spatial or seasonal patterns are in model (dis)agreement.

The initial evaluation will focus on comparing the baseline scenario model simulations to identify regions, times, and species for which the model’s baseline results differ (see

Sect. 6.2). The models will be evaluated against observations with a focus on regions and species where models are not in agreement with each other. The initial evaluation will also include hydrodynamic (e.g., circulation patterns, overturning time, regional stratification and upwelling) and biogeochemical (e.g., nutrients, phytoplankton and zooplankton concentrations and seasonality) variables so as to understand the discrepancies of non-harmonized internal model processes. Further, a range of simulations (O4.1–O4.7) attributing uncertainties to individual external and internal modeling factors are proposed.

- *Anthropogenic emissions:* the model uncertainty due to emission inventories and speciation will be estimated using different atmospheric Hg boundary conditions from the atmospheric models. For this, there are three scenarios (O4.1–O4.3) using alternative anthropogenic emission inventories as model drivers. This will help in understanding the uncertainty due to Hg total emission, and their spatial distribution and speciation. The latter is of special importance, as Hg speciation in emission, especially from the largest source sector of coal combustion, has been changing due to new technologies such as desulfurization. Depending on the fraction of Hg emitted in oxidized or particulate form, even the identical total Hg emissions can lead to vastly different spatial distributions of Hg deposition to global oceans since the atmospheric lifetime of elemental Hg(0) is an order of magnitude higher than that of the other species.

- *Methylation–demethylation:* the exact processes that govern methylation and demethylation are still not fully understood. To better understand the model uncertainty due to different chemical mechanisms for Hg transformation, scenario O4.4 tests the impact of the chemical mechanism by harmonizing reactions and rates for Hg transformations.

- *Air–sea exchange:* with an annual Hg net evasion of 3000–4000 Mg from the ocean, this is the largest Hg flux into the atmosphere. However, this process is not well understood and only indirectly observable. Estimates of global air–sea exchange are based on either co-located Hg(0) measurements in the air and surface ocean or from global atmospheric models. Simulations O4.5 and O4.6 will use alternative parameterizations for the two-layer air–sea exchange model to estimate the uncertainty due to this process. Moreover, by coupling to the atmospheric models, we will create consistent multi-compartment estimates, taking into account atmospheric and marine processes and quantifying the model uncertainty due to the air–sea exchange parameterization.

- *Model resolution:* simulation O4.7 examines the impact of marine model resolution. This is of interest as
low resolution global models cannot resolve the coastal
shelf seas. However, these regions have the largest bi-
ological productivity and are thus potentially both an
important sink for Hg and a region of enhanced methy-
lation. The impact of the shelf oceans and the neces-
sity of high-resolution ocean models to resolve this pro-
cess for carbon cycling was recently shown by Mathis
et al. (2022).

- *Secondary emissions*: a central question in the global Hg cycle is what fraction of marine Hg evasion into the atmosphere stems from previously deposited anthropogenic Hg emissions. This secondary emission fraction will be estimated based on Simulations O2.1 and O2.2, where contemporary anthropogenic and geogenic emissions are zeroed out.

- *Bioaccumulation*: the central reason for nations to agree on the MC is the threat to human health posed by Hg and MeHg in seafood. With biogeochemical marine Hg models, it is possible to quantify methylation and uptake of Hg in the marine ecosystem and the impact of external stressors like anthropogenic emissions and climate change thereon. Understanding the influence of biotic Hg is important for the distribution of oceanic Hg. For example, a recent study (Amptmeijer and Bieser, 2023) shows that Hg uptake by phytoplankton can lead to higher MeHg concentrations in the surface ocean as well as increased Hg(II) sedimentation and Hg(0) air-sea exchange. Simulation O2.4 investigates the role of Hg bioaccumulation in the lower food web (phytoplankton and zooplankton) and sedimentation of Hg sorbed to organic particles on marine Hg.

9 Modeling future scenarios

Within the limitations and uncertainties outlined above, and
quantified through the proposed simulations, models are es-
sential tools for projecting future trajectories of Hg levels
in the air and ocean. This includes projecting the influence
of scenarios of implementation of the MC and other MEAs,
as well as changes in Hg levels that result from changing
environmental conditions (due to climate and other global
changes).

Appropriate methods for modeling changes in atmosphere
and ocean Hg levels depend strongly on the timescale of in-
terest. With respect to the immediate time frame of the first
MC EE, projecting changes over a few years requires consid-
eration of changes in the contemporary cycle (see Fig. 1). For
changes over several years to decades, the long-term cycle
becomes increasingly relevant (Amos et al., 2013). It is im-
portant to note that changes to the long-term cycle that affect
atmospheric and ocean Hg concentrations on timescales of
several years or more are driven not only by past emissions,

but also by the cycling of ongoing and future Hg emissions
(Selin, 2018).

A number of previous studies have used atmospheric mod-
els to project atmospheric concentrations of Hg and changes
in wet and dry deposition, under future conditions and both
regionally and globally. Pacyna et al. (2016) used two global
chemical transport models (GLEMOS and ECHMERIT) to
project changes in future Hg concentrations and deposition
at a global scale under policy scenarios relating to current
pollution control and climate policies. Angot et al. (2018)
simulated the same emission scenarios using the GEOS-
Chem model. Other efforts have also drawn on global sce-
narios to quantify potential changes in source–receptor rela-
tionships (Chen et al., 2018; Corbitt et al., 2011; Zhang et
al., 2021a) and regional impacts (Giang et al., 2015; Lei et
al., 2014; Schartup et al., 2022). Regarding the ocean, there
are fewer existing studies that project future ocean Hg con-
centrations in different scenarios (consistent with the more
general limitation in the number of models that exist, as
noted in Sect. 2.2). Zhang et al. (2021b) simulated five differ-
ent future anthropogenic emission scenarios (ranging from
a > 80% reduction to a 2.5-fold increase) using a coupled
3D atmosphere-ocean model. A recent assessment of the
Arctic environment simulated likely changes in both atmo-
spheric and oceanic Hg there in future emission scenarios
(Schartup et al., 2022).

A few attempts have used various degrees of model cou-
pling to address the combined impact of future and legacy
changes, accounting for the influence of future emission
scenarios on the long-term cycle. One study calculated
that extending emission reductions over 5 years led to a
14% decrease in local-scale Hg deposition (Angot et al.,
2018). These estimates are affected by the uncertainties in
timescales of inter-compartmental cycling and the overall
mercury budget: Zhang et al. (2023a) recently calculated
a lower sensitivity of atmospheric levels to anthropogenic
emissions than had been estimated in previous models, due
to a larger role of legacy emissions from the terrestrial and
marine environments.

Section 3.2.2 (“Future emissions”) outlines progress to-
wards developing future emission scenarios for Hg that lever-
age the best-available information on trajectories of different
economic activities (including related efforts to limit green-
house gas emissions by reducing fossil fuel use) with up-
dated data on control measures. Understanding the relative
importance of each of these drivers through different emis-
sion scenarios (Table E12) will be important for differentiat-
ing the influences of Minamata-related legislation, ambitious
end-of-pipe control measures, and net-zero climate targets on
levels of Hg in the atmosphere and ocean.

Simulations A6.1–A6.3 are designed to provide informa-
tion on the relative impact of changes in atmospheric concen-
trations of mercury resulting from changes in anthropogenic
emissions, environmental factors, and combined influences.
Because each model setup is different, it will be important
in these simulations to clearly differentiate how the impacts of past and future emissions are treated, the latter of which will also influence long-term cycling, as well as the source of future environmental conditions. For those scenarios that do not account of the time-varying legacy contribution from future scenarios, they will reflect a lower bound. As the impact of different sources adds linearly in the global mercury cycle, it will also be possible to derive source–receptor relationships useful for policy and future evaluation from the additional atmospheric simulations for the present (an example of such a strategy is presented by De Simone et al., 2020). In this study, we can update the legacy emission estimates for the atmospheric models by offline coupling to the ocean and making use of mass balance models. For the marine model simulations, O5.1–O5.3 will mirror the atmospheric setup. To allow for the identification of significant trends, the simulation will cover a time span of 30 years.

Scenario A2.1, the perturbation simulation with no anthropogenic emissions, will also be informative in understanding and bounding the potential for anthropogenic actions to affect mercury concentrations. Examining the model response to these scenarios, particularly with coupled and multimedia simulations, will provide important uncertainty bounds on future policy-relevant analysis. Quantifying the mercury response to zeroing-out emissions will be an important metric through which to compare model processes, similar to the zero-emissions commitment (ZEC) concept used for Earth system models for CO₂ (MacDougall et al., 2022).

In all future scenarios, it will also be important to account for variability (especially with respect to future climate influences). While single-year constant meteorological comparison is appropriate for evaluating impacts of near-term emission changes, multiple years or even decades of simulation will be required to account for natural variability in the climate system and its influence on atmospheric chemical processes (Garcia-Menendez et al., 2017; Giang et al., 2018). In addition, differences among models reflect systematic uncertainties in key processes underlying the mercury cycle. Moving forward, model intercomparisons and model analysis in combination with increasing data constraints, including through the activities proposed above, will help narrow key uncertainties that hamper current ability to project future scenarios.

## 10 Summary

The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP), an international ensemble modeling initiative to inform the effectiveness evaluations of the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution, has been introduced. The MCHgMAP is intended to address the key policy questions of these multilateral environmental agreements (MEAs) by using an up-to-date Hg modeling capacity.

(1) What are the contributions of anthropogenic emissions and releases and other Hg sources to current Hg levels observed in the air, biota, humans, and other media? (2) How have these contribution levels changed over time and over the timeline of the MEA? (3) How do the contribution levels and their trends vary geographically at the global scale? (4) What are the contributions of anthropogenic emissions and releases and other drivers to the temporal trends in observed Hg levels across global regions? (5) How are observed Hg levels expected to change in the future?

This overview paper is an important preparatory stage of the MCHgMAP, which will be followed by individual publications on different aspects of the study. Sections 1 to 4 provide a synthesis of what is currently known and not known about the estimates of mercury primary and secondary emissions, observations (direct monitoring and natural archives) and single- to multi-compartmental mercury models, and how these advances can be exploited to develop mechanistic understanding of the past, present, and future fates of environmental mercury. The project aims at improving comparability across the participating models by employing a consensus set of emissions and environmental conditions (where possible), and constraining model evaluation with common observation datasets. Methodologies for developing harmonized model drivers including historical and future primary and secondary emissions as well as biochemical fields that can be used in core and optional simulations are described. Sections 5 and 6 describe the rationale, design, implementation, and evaluation of multimodel experiments. The subsequent Sects. 7 to 9 present analysis of modeling results, i.e., detection and attribution of spatial patterns and temporal trends, identification of uncertainties, and future projections, to address the policy questions. Additionally, all sections provide recommendations for the areas of mercury research (monitoring and modeling) that are needed to improve effectiveness evaluations of the Hg MEAs going forward.

A challenge of analyzing the fate of emitted mercury is that it can recycle between the atmosphere, land, and ocean (i.e., secondary emissions and releases), and as a result, past and present emissions can continue to affect the environment on timescales of decades to centuries. There have been significant developments in marine and land Hg observations and modeling since previous ensemble Hg modeling studies which were mainly focused on atmospheric processes. Availing new advances, the MCHgMAP utilizes a coordinated simulation approach between single medium (atmosphere, land, and ocean) and multimedia Hg models to allow and measure the influence of changes in secondary Hg exchanges on environmental Hg cycling. The MCHgMAP *baseline* and *perturbation* ensemble simulations are designed to robustly analyze global and regional environmental Hg levels, i.e., detection and attribution of spatial gradients and temporal trends, understanding of global mass balance between sources, sinks and burdens, and future projections under different scenarios of implementation of the Minamata and
other MEAs and changing environmental conditions. Addi-
tional model experiments, *uncertainty* and *idealized*, are de-
signed to assess the sensitivity of modeled levels and trends
to their drivers and underlying processes to guide future de-
velopments of emission inventories, and field and labora-
tory studies investigating environmental mercury. Finally, the
model experiments are prioritized into three tiers – core, op-
tional, and future simulations – to ensure a systematic and
comprehensive analysis and flexibility in the plan to encour-
age participation of a variety of mercury models from the
scientific community.

**Appendix A: The policy questions**

The coordinated multimedia, multimodel simulations in MCHgMAP are designed to analyze current and future levels and temporal trends of environmental Hg on global and regional scales to inform the effectiveness evaluations of the MC and LRTAP conventions. The MCHgMAP activities are targeted to address the following policy questions (divided into six categories), in alignment with the objectives outlined in the Minamata monitoring guidance UNEP/MC/COP.4/INF/12 (Guidance on monitoring mercury and mercury compounds to support the effectiveness evaluation of the Minamata Convention|Minamata Convention on Mercury, 2023) and the data analysis plan of the MC Open-Ended Science Group (UNEP/MC/COP.5/INF/24). The first set of questions is aimed at developing and/or examining the levels and trends of environmental Hg drivers, i.e., primary and secondary Hg emissions and releases and environmental conditions. The next five categories of policy questions address the analysis of environmental Hg spatiotemporal patterns and trends, i.e., their detection, attribution, process understanding, future projections, and mass balance budgets. In the following list, “mercury levels” refers to Hg concentrations and/or fluxes.

A. Driving sources and environmental conditions of envi-
ronmental mercury

(a) What are the current levels and temporal trends of primary Hg emissions and releases from anthropogenic and natural sources across the globe?

(b) What are the current levels and temporal trends of secondary Hg emissions and releases from biomass burning, soils, and oceans across the globe?

(c) What are the current levels and temporal trends of environmental variables related to Hg processes across the globe?

(d) What are the fractional contributions of changes in anthropogenic Hg emissions and releases and environmental conditions to changes in secondary Hg emissions and releases over the period ca. 2010 to present across the globe?

(e) How are anthropogenic Hg emissions and releases expected to change in the future in different scenarios of implementation of the Minamata and other conventions across global regions?

(f) How are secondary Hg emissions and releases from biomass burning, soils, and oceans expected to change in the future under changing anthropogenic Hg emissions and releases and environmental conditions across global regions?

B. Detection of spatiotemporal features of environmental mercury

7. What are the spatial patterns and temporal trends of Hg levels in the air and ocean across the globe?

8. What is the spatial representativeness of current levels and temporal trends of Hg observed in the air and ocean?

9. How are spatial patterns and temporal trends of Hg levels in the air and ocean related to patterns and trends of other pollutants or environmental variables on the global scale?

C. Attribution of spatiotemporal features of environmental mercury

10. What are the fractional contributions of pri-
    mary (anthropogenic and natural) and secondary
    Hg emissions and releases to current Hg levels ob-
    served in the air and ocean across global regions?

11. How have the fractional contributions of pri-
    mary (anthropogenic and natural) and secondary
    Hg emissions and releases to levels of Hg ob-
    served in the air and ocean changed over the period
    ca. 2010 to the present across global regions?

12. What is the fractional contribution of anthro-
    pogenic Hg emissions influenced by the convention
    to current Hg levels observed in the air and ocean
    across global regions?

13. How has the fractional contribution of anthro-
    pogenic Hg emissions influenced by the convention
    to Hg levels observed in the air and ocean changed
    over the period ca. 2010 to the present across global
    regions?

14. What are the fractional contributions of changes in primary anthropogenic and natural Hg emissions and releases and environmental conditions to changes in Hg levels observed in the air and ocean over the period ca. 2010 to the present across global regions?

15. What is the fractional contribution of changes in anthropogenic Hg emissions influenced by the convention to changes in Hg levels observed in the air and ocean over the period ca. 2010 to the present across global regions?
16. In each world region, how much of the contribution of changes in primary anthropogenic Hg emissions to changes in Hg levels observed in air over the period ca. 2010 to the present are due to emissions within the region versus outside the region?

D. *Estimation of mass balance budgets of environmental mercury*

17. What is the global mass balance of Hg between budgets of emissions, releases, levels, and movements in the environment?

E. *Process understanding of environmental mercury*

18. How consistent are the spatial patterns and temporal trends of Hg levels observed in the air and ocean with estimates from current mechanistic models across global regions?

19. How consistent are the anthropogenic emission inventories and inventory data with spatial patterns and temporal trends of Hg levels observed in the air and ocean on the global scale?

20. How sensitive are the spatial patterns and temporal trends of Hg levels in the air and ocean to various primary and secondary Hg emissions and releases as well as environmental conditions across global regions?

21. How sensitive are the spatial patterns and temporal trends of Hg in the air and ocean to various Hg processes across global regions?

F. *Future projections of environmental mercury*

22. How are Hg levels in the air and ocean expected to change in the future in different scenarios of implementation of the Minamata and other conventions across global regions?

23. How are Hg levels in the air and ocean expected to change in the future under changing environmental conditions across global regions?

**Appendix B: Harmonized air-sea flux formulation**

The exchange of mercury between the ocean and the atmosphere plays an important role in the cycling of mercury in the global environment. Available parameterizations of mercury air-water exchange used in modeling and measurement studies still vary considerably indicating an incomplete understanding of the process and lack of direct measurements. We here present the parameterizations recommended for use in the MCHgMAP multimodel experiments.

The two-layer (film) model by Liss and Slater (1974), which is the traditional parameterization used to determine the Hg air-water gas exchange flux, is recommended. It

<figure><img src="image_12.png" /></figure>

**Figure B1.** Wind speed dependence of air-water gas transfer velocities from various parameterizations often applied in Hg modeling and measurement studies scaled to a Schmidt number of 660 (CO₂ in seawater at 20 °C). Nightingale et al. (2000a) parameterizations 1 and 2 (in solid dark blue) are both commonly used in Hg work and give almost identical results. The dark-blue dotted line refers to Nightingale et al. (2000b), the orange line to Wanninkhof (1992), the green line to Wanninkhof (2014), the light-blue line to Liss and Merlivat (1986), the yellow line to McGillis et al. (2001), and the pink line to Osterwalder et al. (2021).

is possible to neglect the gas-side part of the transfer velocity in the model as the gas-side conductance for Hg is much higher than that on the water side ($k_w \ll k_a H'$) (Nerentorp Mastromonaco et al., 2017a). We further recommend the parameterization of the gas transfer velocity ($k_w$) by Wanninkhof (2014) that is based on the updated methodology of Wanninkhof (1992) and that is in close agreement with parameterizations based on gas exchange process studies over the oceans (e.g., Nightingale et al., 2000a, b) (Fig. B1). Recent micrometeorological measurements by Osterwalder et al. (2021) provide estimates of $k_w$ determined directly for Hg. However, this is the first time that this method has been used for Hg flux evaluation, and more measurements are needed to validate the result. Therefore, the application of this alternative parameterization is only recommended for uncertainty analysis.

The temperature dependence of the gas transfer velocity of Hg is defined by the Schmidt number of Hg (Schmidt number is a ratio of water kinematic viscosity to the diffusion coefficient of a gas). The biggest differences between available parameterizations for the Schmidt number of Hg relate to the diffusion coefficient of Hg(0). For the diffusion coefficient of Hg(0) in the baseline simulations, we recommend the widely used parameterization by Wilke and Chang (1955).
<figure><img src="image_13.png" /><figcaption>Figure B2. (a) Temperature dependence of the Hg⁰ diffusion coefficient. (b) Temperature dependence of the Schmidt number of Hg(0) in seawater (from Wilke and Chang (1955) in green and Kuss (2014) in blue).</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Figure B3. Temperature dependence of air-seawater gas transfer velocity for Hg(0) (from Wilke and Chang, 1955, and Wanninkhof, 2014 – green; Kuss, 2014, and Wanninkhof, 2014 – blue).</figcaption></figure>

An alternative choice for sensitivity tests is the experimentally determined approximation from Kuss (2014), which provides lower values for the diffusion coefficient and consequently larger values for the Schmidt number (Fig. B2). Parameterizations of water kinematic viscosities by Kestin et al. (1978) and Sharqawy et al. (2010; freshwater and saltwater versions) do not differ significantly from each other. Of the two available parameterizations of the water kinematic viscosity for seawater (Wanninkhof, 1992; Sharqawy et al., 2010), we suggest the latter to be more universal since it provides values for a wide range of salinity. Figure B3 shows the temperature dependence on the transfer velocity for parameterizations recommended compared to the alternative combination.

The equations below outline all recommendations for Hg(0) air-water exchange parameterizations for the model experiments under MCHgMAP. Parameterization of Hg(0) exchange flux between air and seawater ($S = 35 \text{ g kg}^{-1}$) approach:

$$F_{\text{Hg}} = k_w \left( c_w - \frac{c_a}{H'} \right) \left[ \text{ng m}^{-2} \text{ h}^{-1} \right]. \quad (\text{B1})$$

$c_a$ and $c_w$ are concentrations of Hg(0) in air and water [ng m⁻³].

$$H' = \exp\left(-\frac{2404.3}{T} + 6.92\right) \quad (\text{B2})$$

(Andersson et al., 2008a). T is the water temperature [K].

$$k_w = 0.25 u_{10}^2 \left( \frac{Sc_{\text{Hg}}}{660} \right)^{-0.5} \times 10^{-2}, \quad [\text{m h}^{-1}] \quad (\text{B3})$$

(Wanninkhof, 2014). $u_{10}$ is wind speed at 10 m height [m s⁻¹].

$$Sc_{\text{Hg}}^{\text{sw}} = v_{\text{sw}}/D_{\text{Hg}}^{\text{sw}}, \quad v_{\text{sw}} = \eta_{\text{sw}}/\rho_{\text{sw}} \quad (\text{B4})$$

(Sharqawy et al., 2010).

$$\eta_{\text{sw}} = \eta_{\text{fw}} (1.064 + 6.067 \times 10^{-4} t - 2.753 \times 10^{-6} t^2), \quad [\text{g cm}^{-1} \text{ s}^{-1}] \quad (\text{B5})$$

$$\eta_{\text{fw}} = 4.2844 \times 10^{-4} + (0.0157(t + 64.993)^2 - 9.13)^{-1}, \quad [\text{g cm}^{-1} \text{ s}^{-1}] \quad (\text{B6})$$

$$\rho_{\text{sw}} = 1.028 - 4.970 \times 10^{-5} t - 5.575 \times 10^{-6} t^2, \quad [\text{g cm}^{-3}] \quad (\text{B7})$$

t is water temperature [°C];

$$D_{\text{Hg}} = \frac{7.4 \times 10^{-10} T \sqrt{\Phi_w M_w}}{\eta V_b^{0.6}}, \quad [\text{cm}^2 \text{ s}^{-1}] \quad (\text{B8})$$

(Wilke and Chang, 1955). T is the water temperature [K]; $\Phi_w = 2.26$ is the solvent association factor (Hayduk and Laudie, 1974); $M_w = 18.01$ is the molecular weight of water [g mol⁻¹]; $V_b = 12.74$ is the molar volume of mercury at its normal boiling temperature [cm³ mol⁻¹] (Loux, 2004; Soerensen et al., 2010); $\eta = \eta_{\text{sw}}$ is the dynamic viscosity of seawater [g cm⁻¹ s⁻¹].
## Appendix C: Geographic regions for multimodel analysis

<figure><img src="image_15.png" /><figcaption>**Figure C1.** Geographical source and receptor regions for atmospheric model analysis.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Figure C2.** Aquatic receptor regions for ocean model analysis (FAO major fishing areas, https://www.fao.org/fishery/en/area/search, last access: 1 May 2025).</figcaption></figure>
<table><caption>**Table C1.** Geographical regions for model analysis.</caption><thead><tr><th>Code</th><th>Fishing area</th><th>Code</th><th>Fishing area</th><th>Code</th><th>Fishing area</th></tr></thead><tbody><tr><td>18</td><td>Arctic Sea</td><td>47</td><td>Southeastern Atlantic</td><td>71</td><td>Western central Pacific</td></tr><tr><td>21</td><td>Northwestern Atlantic</td><td>48</td><td>Antarctic Atlantic</td><td>77</td><td>Eastern central Pacific</td></tr><tr><td>27</td><td>Northeastern Atlantic</td><td>51</td><td>Western Indian Ocean</td><td>81</td><td>Southwestern Pacific</td></tr><tr><td>31</td><td>Western central Atlantic</td><td>57</td><td>Eastern Indian Ocean</td><td>87</td><td>Southeastern Pacific</td></tr><tr><td>34</td><td>Eastern central Atlantic</td><td>58</td><td>Antarctic Indian Ocean</td><td>88</td><td>Antarctic Pacific</td></tr><tr><td>37</td><td>Mediterranean and Black Sea</td><td>61</td><td>Northwestern Pacific</td><td></td><td></td></tr><tr><td>41</td><td>Southwestern Atlantic</td><td>67</td><td>Northeastern Pacific</td><td></td><td></td></tr><tr><th>Type</th><th>List/description</th><th></th><th></th><th></th><th></th></tr><tr><td>Geographical regions (Fig. C1)</td><td>1. High Arctic<br>2. Low Arctic<br>3. North America<br>4. Europe/Mediterranean<br>5. Eastern Europe/North Asia<br>6. Central America/Caribbean<br>7. South America<br>8. Africa<br>9. Middle East<br>10. South Asia<br>11. East Asia<br>12. Southeast Asia<br>13. Oceania<br>14. Antarctica</td><td></td><td></td><td></td><td></td></tr><tr><td>Land cover types (for each geographical region)</td><td>1. Freshwater bodies<br>2. High vegetation<br>3. Low vegetation<br>4. Crops<br>5. Urban areas<br>6. Bare lands/ice cover</td><td></td><td></td><td></td><td></td></tr><tr><td>Aquatic regions (FAO major fishing areas, Fig. C2)</td><td>1. Arctic Sea<br>2. Northwestern Atlantic<br>3. Northeastern Atlantic<br>4. Western central Atlantic<br>5. Eastern central Atlantic<br>6. Mediterranean and Black Sea<br>7. Southwestern Atlantic<br>8. Southeastern Atlantic<br>9. Antarctic Atlantic<br>10. Western Indian Ocean<br>11. Eastern Indian Ocean<br>12. Antarctic Indian Ocean<br>13. Northwestern Pacific<br>14. Northeastern Pacific<br>15. Western central Pacific<br>16. Eastern central Pacific<br>17. Southwestern Pacific<br>18. Southeastern Pacific<br>19. Antarctic Pacific</td><td></td><td></td><td></td><td></td></tr><tr><td>Affected regions</td><td>Shelf sea and coastal regions</td><td></td><td></td><td></td><td></td></tr></tbody></table>
## Appendix D: File format for data submission

The following formatting rules are required for data submission:

1. All netCDF files should adhere to COARDS conventions. These conventions suggest appropriate attributes (name, units, etc.) for the dimensions of a netCDF file (including time, lev, lat, and long). More details about the requested coordinate dimensions and their metadata can be found in the Supplement Excel spreadsheet detailing the format of output variables (MCHgMAP_atm_output_variables_v4.xlsx). One can use the script isCoards provided by the GEOS-Chem support team to verify whether your file is COARDS-compliant (ignore errors related to _bnds variables or GCHP-specific errors). See here for instructions.

2. netCDF files should be concatenated to reduce the number of files that need to be opened and make I/O more efficient. We have suggested using one file per output variable per month or year, requiring concatenation of individual files. Yearly files are requested for monthly averaged data. Monthly files are requested for daily averaged data and the diurnal-cycle-averaged variables.

3. All netCDF files should be provided with global attributes that describe information about the institution, model experiment, model version and type, contact person, and additional comments. For a full list, see the Excel spreadsheet (MCHgMAP_atm_output_variables_v4.xlsx).

4. All data should be stored in float precision where possible (we do not want to store too much data, so double precision is unnecessary).

5. netCDF files should be deflated (compressed) to reduce their size. This can be done with the command nccopy - dl in_file.nc out_file.nc.

6. A README file should be included detailing the data source and contents. It would be helpful to include expected results in this README file, especially for input emission files (i.e., global emission totals for each species or source).

7. Where possible, include scripts used to produce the data. For example, if an input emission file is being submitted, include the script where these emission data are created.

8. Each requested variable (listed in MCHgMAP_atm_output_variables_v4.xlsx in the Supplement) should be provided in a separate file containing all time steps for a year (for monthly averages) or month (for daily or diurnal averages). The files should be constructed according to the convention mchgmap_<ModelName>_<ExperimentName>_<VariableName>_<VerticalCoordinateType>_<Period>_<Frequency>.nc where: <ModelName> should be chosen so that the model's name, model version, and possibly the institution can be identified. No underscores (_) are allowed in <ModelName>, use hyphens (-) instead. Restrict <ModelName> to a maximum of 20 characters. <ExperimentName> should correspond to the model experiment codes from Tables F8 to F10. <VariableName> should correspond to the output variable short names in the Excel spreadsheet (MCHgMAP_atm_output_variables_v4.xlsx). <VerticalCoordinateType> is the surface or model level from the output spreadsheet. <Period> is the year and/or month of the data, in format YYYY (monthly data) or YYYYMM (daily data). <Frequency> refers to time-invariant, monthly, daily, or diurnal.

9. Several variables (related to meteorological data) are only requested for the baseline simulations. A column in MCHgMAP_atm_output_variables_v4.xlsx details whether the variables are necessary for all of the simulations or only the baseline simulation.
## Appendix E: Anthropogenic mercury emissions

### E1 Historical anthropogenic emissions

<table><caption>Table E1. Overview of the anthropogenic atmospheric Hg emission inventories.</caption><thead><tr><th>Region</th><th>Dataset</th><th>Methodology</th><th>Year</th><th>Grid accuracy</th><th>Specification</th><th>Uncertainty (estimated year)<sup>a</sup></th><th>References</th></tr></thead><tbody><tr><td>Global</td><td>AMAP</td><td>- Sector-specific emission factor<br/>- Sectoral activity data by nation</td><td>1990–2005</td><td>1° × 1° (1990)<br/>0.5° × 0.5° (1995–2005)</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>NA</td><td>Pacyna and Pacyna (2002); Pacyna et al. (2006a); AMAP/UNEP (2008)</td></tr><tr><td>Global</td><td>AMAP/GMA</td><td>- Emission factor based on mass-balance approach<br/>- Sectoral activity data by nation</td><td>2010, 2015</td><td>0.5° × 0.5° (2010)<br/>0.25° × 0.25° (2015°)</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-10% to<br/>+27% (2015)</td><td>AMAP/UNEP (2013); AMAP/UN Environment (2019); Steenhuisen and Wilson (2015, 2019)</td></tr><tr><td>Global</td><td>EDGAR</td><td>- Emission factor based on mass-balance approach<br/>- Sectoral activity data by nation</td><td>1970–2012</td><td>0.1° × 0.1°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-26% to<br/>+33% (2012)</td><td>Muntean et al. (2014, 2018)</td></tr><tr><td>Global</td><td>STREETS<sup>b</sup></td><td>- Emission factor based on transformed normal distribution function<br/>- Sectoral activity data by nation or region</td><td>2000–2015</td><td>1° × 1°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-20% to<br/>+44% (2015)</td><td>Streets et al. (2011, 2019a, b)</td></tr><tr><td>Global</td><td>WHET</td><td>Updated STREETS dataset with country-specific estimates</td><td>1990, 2000, 2010</td><td>1° × 1°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-33% to<br/>+60% (2010)</td><td>Zhang et al. (2016b)</td></tr><tr><td>North America</td><td>NA</td><td>Coupling US TRI dataset with Canadian dataset</td><td>1990</td><td>NA</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-12% to<br/>+22% (1990)</td><td>Walcek et al. (2003)</td></tr><tr><td>America</td><td>US NEI</td><td>Facility reporting</td><td>1970–2017<br/>(triennial)</td><td>NA</td><td>THg</td><td>NA</td><td>US EPA (2022a)</td></tr><tr><td>America</td><td>US TRI</td><td>Facility reporting</td><td>1987–2021<br/>(Annual)</td><td>NA</td><td>THg</td><td>NA</td><td>US EPA (2022b)</td></tr><tr><td>Canada</td><td>Canada NPRI</td><td>Facility reporting</td><td>1994–2021<br/>(Annual)</td><td>NA</td><td>THg</td><td>NA</td><td>NPRI (2022)</td></tr><tr><td>China</td><td>Abacas-EI-Hg</td><td>- Emission factor based on mass-balance approach<br/>- Facility-level activity data and pollution control in key sectors</td><td>1978–2017</td><td>0.25° × 0.3125°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-19% to<br/>+21% (2017)</td><td>Wu et al. (2016); Zhang et al. (2015a); Liu et al. (2019a)</td></tr><tr><td>EU</td><td>NA</td><td>- Sector-specific emission factor<br/>- Sectoral activity data by nation</td><td>1980–2000</td><td>0.5° × 0.5°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-26% to<br/>+33% (2000)</td><td>Pacyna et al. (2006b)</td></tr><tr><td>India</td><td>NA</td><td>- Sector-specific emission factor<br/>- Sectoral activity data</td><td>2010</td><td>NA</td><td>THg</td><td>-80% to<br/>+300%</td><td>Chakraborty et al. (2015)</td></tr><tr><td>Thailand</td><td>NA</td><td>- Emission factor based on mass-balance approach<br/>- Facility-level activity data</td><td>2018</td><td>0.1° × 0.1°</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-8% to<br/>+8%</td><td>Bich Thao et al. (2021)</td></tr><tr><td>Korea</td><td>NA</td><td>- Emission factor based on mass-balance approach<br/>- Facility-level activity data</td><td>2007</td><td>NA</td><td>Hg(0), Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub></td><td>-20% to<br/>+20%</td><td>Kim et al. (2010)</td></tr></tbody></table>
<table><caption>**Table E1. Continued.**</caption><thead><tr><th>Region</th><th>Dataset</th><th>Methodology</th><th>Year</th><th>Grid accuracy</th><th>Specification</th><th>Uncertainty (estimated year)<sup>a</sup></th><th>References</th></tr></thead><tbody><tr><td>Australia</td><td>NA</td><td>– Industrial sources: obtained from national emission inventory<br/>– Other sources: sector-specific emission factor and activity data</td><td>2000–2019</td><td>Distributed emissions at 0.25° resolution and point-source emissions at 0.1° resolution</td><td>Hg(0), Hg(II)<sub>g</sub>, Hg(II)<sub>p</sub></td><td>–35 % to +35 %</td><td>MacFarlane et al. (2022)</td></tr><tr><td>70 countries (available on 21 Dec 2022)</td><td>MIA</td><td>– Emission factor based on UNEP toolkit or local field experiment<br/>– Sectoral-specific activity data</td><td>Vary by country</td><td>NA</td><td>THg</td><td>NA</td><td>UNEP (2022a)</td></tr><tr><td>20 countries (available on 21 Dec 2022)</td><td>NR</td><td>– Sectoral-specific emission factor and activity data</td><td>Vary by country</td><td>NA</td><td>THg</td><td>NA</td><td>UNEP (2022b)</td></tr><tr><td>44 countries (available on 21 Dec 2022)</td><td>PRTR</td><td>– Facility reporting</td><td>Vary by country</td><td>THg</td><td>NA</td><td>NA</td><td>UNECE (2022)</td></tr></tbody></table>

Notes: a If the studied inventory involves emission in multiple years, only the uncertainty range of the latest year is listed in the table. b The Hg emission inventory compiled by Streets et al. (2011, 2019a, b) is referred to as STREETS in this paper. c Also available at 0.1° grid resolution. NA – not available; Hg(0) – gaseous elemental Hg; Hg(II)<sub>g</sub> – gaseous oxidized Hg; Hg(II)<sub>p</sub> – particulate-bound Hg; MIA – Hg initial assessment; NR – national report to UNEP; THg – total Hg.

<table><caption>**Table E2.** Sectoral emissions in different global anthropogenic atmospheric Hg emission inventories.</caption><thead><tr><th rowspan="2">Sources</th><th colspan="3">Emissions in 2010</th><th colspan="2">Emissions in 2015</th></tr><tr><th>AMAP/GMA</th><th>EDGAR</th><th>STREETS</th><th>AMAP/GMA</th><th>STREETS</th></tr></thead><tbody><tr><td>Artisanal and small-scale gold mining</td><td>679.0</td><td>727</td><td>726.8</td><td>838.0</td><td>775.1</td></tr><tr><td>Biomass burning</td><td>49.5</td><td>30.8</td><td>NA</td><td>51.9</td><td>NA</td></tr><tr><td>Cement production</td><td>187.0</td><td>152.4</td><td>151.9</td><td>233.0</td><td>206.3</td></tr><tr><td>Cremation</td><td>4.9</td><td>NA</td><td>NA</td><td>3.8</td><td>NA</td></tr><tr><td>Chlor-alkali production (mercury process)</td><td>21</td><td>9.2</td><td>14.8</td><td>15.2</td><td>20.1</td></tr><tr><td>NFM (Al, Cu, Pb, Zn) production</td><td>151</td><td>NA</td><td>NA</td><td>228</td><td>NA</td></tr><tr><td>Copper smelting</td><td>151</td><td>0.4</td><td>59.7</td><td>228</td><td>70.0</td></tr><tr><td>Lead smelting</td><td>151</td><td>4.1</td><td>25.2</td><td>228</td><td>25.6</td></tr><tr><td>Zinc smelting</td><td>151</td><td>61.3</td><td>96.4</td><td>228</td><td>103.6</td></tr><tr><td>Large-scale gold production</td><td>73.1</td><td>67.1</td><td>87.1</td><td>84.5</td><td>112.1</td></tr><tr><td>Mercury production</td><td>12.2</td><td>0.4</td><td>22.6</td><td>13.8</td><td>33.0</td></tr><tr><td>Pig iron and steel production (primary)</td><td>26.7</td><td>72.1</td><td>48.1</td><td>29.8</td><td>55.1</td></tr><tr><td>Secondary steel production</td><td>9.7</td><td>NA</td><td>NA</td><td>10.1</td><td>NA</td></tr><tr><td>Coal-fired power plants</td><td>268.0</td><td>351.9</td><td>538.2</td><td>292.0</td><td>558.3</td></tr><tr><td>Coal-fired industrial boilers</td><td>123.0</td><td>101.3</td><td>538.2</td><td>126.0</td><td>558.3</td></tr><tr><td>Coal-fired other (domestic/residential, transportation)</td><td>54.4</td><td>32.2</td><td>538.2</td><td>55.8</td><td>558.3</td></tr><tr><td>Oil combustion – power plants</td><td>2.4</td><td>3.9</td><td>14.4</td><td>2.5</td><td>14.3</td></tr><tr><td>Oil combustion – industry</td><td>3.1</td><td>3.6</td><td>14.4</td><td>1.4</td><td>14.3</td></tr><tr><td>Oil combustion – other</td><td>2.6</td><td>2.2</td><td>14.4</td><td>2.7</td><td>14.3</td></tr><tr><td>Natural gas combustion – power plants</td><td>0.3</td><td>4.7</td><td>NA</td><td>0.3</td><td>NA</td></tr><tr><td>Natural gas combustion – industry</td><td>0.1</td><td>5.5</td><td>NA</td><td>0.1</td><td>NA</td></tr><tr><td>Natural gas combustion – other</td><td>0.2</td><td>2.7</td><td>NA</td><td>0.2</td><td>NA</td></tr><tr><td>Oil refining</td><td>13.1</td><td>NA</td><td>NA</td><td>14.4</td><td>NA</td></tr><tr><td>Waste (other waste)</td><td>115</td><td>32.8</td><td>NA</td><td>147.0</td><td>NA</td></tr></tbody></table>
<table><caption>**Table E2. Continued.**</caption><thead><tr><th rowspan="2">Sources</th><th colspan="3">Emissions in 2010</th><th colspan="2">Emissions in 2015</th></tr><tr><th>AMAP/GMA</th><th>EDGAR</th><th>STREETS</th><th>AMAP/GMA</th><th>STREETS</th></tr></thead><tbody><tr><td>Waste incineration (controlled burning, including waste to energy)</td><td>15.4</td><td>32.8</td><td>NA</td><td>15.0</td><td>NA</td></tr><tr><td>Municipal waste</td><td>NA</td><td>NA</td><td>124.3</td><td>NA</td><td>140.6</td></tr><tr><td>Other waste burning</td><td>NA</td><td>NA</td><td>149.3</td><td>NA</td><td>165.6</td></tr><tr><td>Electrical equipment</td><td>NA</td><td>NA</td><td>81.1</td><td>NA</td><td>62.0</td></tr><tr><td>Agricultural waste burning</td><td>NA</td><td>89.6</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Chemicals</td><td>NA</td><td>NA</td><td>26.4</td><td>NA</td><td>31.8</td></tr><tr><td>Dental</td><td>NA</td><td>NA</td><td>21.5</td><td>NA</td><td>16.1</td></tr><tr><td>Transport</td><td>*</td><td>17.4</td><td>NA</td><td>*</td><td>NA</td></tr><tr><td>Vinyl chloride monomer</td><td>NA</td><td>NA</td><td>NA</td><td>58.3</td><td>NA</td></tr></tbody></table>

Note: NA – Not available; * – included in coal/oil/gas combustion “other”.

<table><caption>**Table E3.** Spatial point source and proxy rasters applied in different anthropogenic Hg emission inventories. Minamata Convention target sectors are shown in bold.</caption><thead><tr><th>Source sector</th><th>Emission dataset</th><th>Spatial proxy raster</th><th>Sector emissions spatially distributed using spatial proxy (%)</th><th>Sector emissions spatially distributed using point source information (%)</th><th>References</th></tr></thead><tbody><tr><td rowspan="4">Coal-fired power plants</td><td>EDGARv4 1970–2012</td><td>NA (point sources: CARMA coal)</td><td>0.0</td><td>100.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>CARMA_Power plants</td><td>3</td><td>3</td><td>Pacyna et al. (2003, 2010)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA_coal</td><td>1.8<sup>4</sup></td><td>98.2<sup>4.5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="2">Gas-fired power plants</td><td>EDGARv4 1970–2012</td><td>Point sources: CARMA gas and in-house EDGAR population proxy</td><td>2.6</td><td>97.4</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA oil/gas</td><td>2.1<sup>4</sup></td><td>97.9<sup>4.5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="2">Oil burning power plants</td><td>EDGARv4 1970–2012</td><td>Point sources: CARMA oil and in-house EDGAR population proxy</td><td>0.5</td><td>99.5</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA oil/gas</td><td>6.0<sup>4</sup></td><td>94.0<sup>4.5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Auto-producers power plants</td><td>EDGARv4 1970–2012</td><td>Point sources: CARMA auto-producers and in-house EDGAR population proxy</td><td>0.5</td><td>99.5</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr></tbody></table>
<table><caption>Table E3. Continued.</caption><thead><tr><th>Source sector</th><th>Emission dataset</th><th>Spatial proxy raster</th><th>Sector emissions spatially distributed using spatial proxy (%)</th><th>Sector emissions spatially distributed using point source information (%)</th><th>References</th></tr></thead><tbody><tr><td>Biomass power plants<sup>1</sup></td><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>100<sup>4</sup></td><td>0.0<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Agricultural waste burning</td><td>EDGARv4 1970–2012</td><td>Crops</td><td>100</td><td>0.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td rowspan="4">Cement production</td><td>EDGARv4 1970–2012</td><td>NA</td><td>0.1</td><td>99.9</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td></td></tr><tr><td>AMAP 1995–2005</td><td>Urban population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>0.7<sup>4</sup></td><td>99.3<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="4">Chlor-alkali production</td><td>EDGARv4 1970–2012</td><td>NA</td><td>0.0</td><td>100.0</td><td>Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>1990 (CGEIC)<br>Urban population</td><td>3</td><td>3</td><td>AMAP/UNEP (2008); Pacyna et al. (2010)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>0.5<sup>4</sup></td><td>99.5<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="4">Nonferrous metal production</td><td>EDGARv4 1970–2012 (Zinc, Lead, copper)</td><td>NA</td><td>0.0</td><td>100.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Urban population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>0.3<sup>4</sup></td><td>99.7<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="3">Gold, ASGM</td><td>EDGARv4 1970–2012</td><td>Point sources: Global gold mines and in-house EDGAR population proxy</td><td>2.7</td><td>97.3</td><td>Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>ASGM proxy</td><td>100.0<sup>4</sup></td><td>0.0<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>AMAP 1995–2005</td><td>Gold deposits</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr></tbody></table>
<table><caption>Table E3. Continued.</caption><thead><tr><th>Source sector</th><th>Emission dataset</th><th>Spatial proxy raster</th><th>Sector emissions spatially distributed using spatial proxy (%)</th><th>Sector emissions spatially distributed using point source information (%)</th><th>References</th></tr></thead><tbody><tr><td rowspan="4">Gold, large-scale</td><td>EDGARv4 1970–2012</td><td>Point sources: Global gold mines and in-house EDGAR population proxy)</td><td>2.7</td><td>97.3</td><td>Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>Gold (mines)</td><td>73.2<sup>4</sup></td><td>26.8<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Gold deposits</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td rowspan="4">Mercury production</td><td>EDGARv4 1970–2012</td><td>Point sources: Global mercury mines and in-house EDGAR population proxy)</td><td>0.5</td><td>99.5</td><td>Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Urban population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>96.6<sup>4</sup></td><td>0.4<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="3">Oil refining</td><td>EDGARv4 1970–2012</td><td>NA</td><td>0.5</td><td>99.5</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>9.9<sup>4</sup></td><td>90.1<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td rowspan="5">Iron and steel production</td><td>AMAP 1995–2005</td><td>Urban population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>EDGARv4 1970–2012 (Crude steel production)</td><td>NA</td><td>0.4</td><td>99.6</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>EDGARv4 1970–2012 (Sinter production)</td><td>NA</td><td>0.5</td><td>99.5</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>EDGARv4 1970–2012 (pig iron production)</td><td>NA</td><td>0.2</td><td>99.8</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>0.2<sup>4</sup></td><td>99.8<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Secondary iron and steel production</td><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>92.6<sup>4</sup></td><td>0.4<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Vinyl chloride monomer production</td><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>100.0<sup>4</sup></td><td>0.0<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Aluminum production</td><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>4.3<sup>4</sup></td><td>95.7<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr></tbody></table>
<table><caption>Table E3. Continued.</caption><thead><tr><th>Source sector</th><th>Emission dataset</th><th>Spatial proxy raster</th><th>Sector emissions spatially distributed using spatial proxy (%)</th><th>Sector emissions spatially distributed using point source information (%)</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Cremation</td><td>AMAP/UN Environnement 2010, 2015</td><td>Population (2015)</td><td>96.9<sup>4</sup></td><td>3.1<sup>4</sup></td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td rowspan="4">Stationary combustion (domestic or residential)</td><td>EDGARv4 1970–2012</td><td>In-house EDGAR population proxy</td><td>100.0</td><td>0.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>100.0<sup>4</sup></td><td>0.0<sup>4.5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td rowspan="4">Stationary combustion for industrial</td><td>EDGARv4 1970–2012</td><td>Point sources and in-house EDGAR population proxy</td><td>55.9</td><td>44.1</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>AMAP/UN Environnement 2010, 2015</td><td>CARMA generic industry</td><td>100.0<sup>4</sup></td><td>0.0<sup>4.5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>Fuel production: transformation industry</td><td>EDGARv4 1970–2012</td><td>Point sources and in-house EDGAR population proxy</td><td>10.9</td><td>89.1</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>Road transport</td><td>EDGARv4 1970–2012</td><td>Line sources: in-house EDGAR proxy based on OpenStreetMap</td><td>100.0</td><td>0.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td>Shipping</td><td>EDGARv4 1970–2012</td><td>Line sources: in-house EDGAR proxy based on Long Range Identification and Tracking (LRIT) and Wang et al. (2007)</td><td>100.0</td><td>0.0</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr></tbody></table>
<table><caption>Table E3. Continued.</caption><thead><tr><th>Source sector</th><th>Emission dataset</th><th>Spatial proxy raster</th><th>Sector emissions spatially distributed using spatial proxy (%)</th><th>Sector emissions spatially distributed using point source information (%)</th><th>References</th></tr></thead><tbody><tr><td rowspan="4">Municipal solid waste incineration</td><td>AMAP/UN Environnement 2010, 2015</td><td>Urban population 2015</td><td>8.6<sup>4</sup></td><td>91.4<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>AMAP 1990</td><td>Global population 1990 (CGEIC)</td><td>2</td><td>2</td><td>AMAP (1998)</td></tr><tr><td>AMAP 1995–2005</td><td>Population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr><tr><td>EDGARv4 1970–2012</td><td>Point sources (in-house EDGAR proxy based on EPRTR) and in-house EDGAR population proxy</td><td>94.5</td><td>5.5</td><td>Janssens-Maenhout et al. (2019); Muntean et al. (2014, 2018)</td></tr><tr><td rowspan="2">Hazardous wastes incineration</td><td>AMAP/UN Environnement 2010, 2015</td><td>Population (2015)</td><td>99.8<sup>4</sup></td><td>0.2<sup>4,5</sup></td><td>Steenhuisen and Wilson (2019); AMAP/UN Environnement (2019)</td></tr><tr><td>AMAP 1995–2005</td><td>Population</td><td>3</td><td>3</td><td>Pacyna et al. (2010, 2003)</td></tr></tbody></table>

Notes: ¹ In EDGARv4 1970–2012, the mercury emissions from biomass combustion in power generation are distributed using the proxies of coal, gas, oil, and auto-producers⁷, ² AMAP 1990 was distributed using the 1990 global population and a limited number of large emission point sources. ³ AMAP 1995–2005 was distributed using an increasing number of point sources over time. The remaining (diffuse) emissions were distributed over several different proxies. A percentage of point source and spatial proxy distribution would only be valid for a specific year and therefore cannot be given for the 1995–2005 period. ⁴ Percentages for the 2015 emission distribution. ⁵ AMAP/UN Environnement utilizes point source emission information derived from national emission inventories and from additional (open) sources as described in Steenhuisen and Wilson (2015, 2019). ⁶ Combustion emissions from fuel use during the manufacture of secondary and tertiary products from solid fuels, including production of charcoal (IPCC, 2006, classification 1.A.1.c). ⁷ Activities which generate electricity or heat wholly or partly for their own use, as an activity that supports their primary activity (EDGAR 1.A.1.a). When all emissions (100 %) of a given sector are distributed using point source information, no proxy raster has been used. Hence, the proxy raster is listed as NA. Minamata target sectors (in bold): coal-fired power plants; coal-fired industrial boilers; nonferrous metal smelting (zinc, lead, copper, and large-scale gold production); waste incineration; cement clinker production; ASGM; and mercury (primary) production.

<table><caption>Table E4. Speciation profile used in the AMAP/GMA, WHET, and EDGAR (%) global Hg emission inventories. Bold is the generic speciation profile.</caption><thead><tr><th rowspan="2">Sectors/fuel</th><th colspan="3">WHET (AMAP/UNEP, 2008)</th><th colspan="3">EDGAR (Zhang et al., 2016b)</th><th colspan="3">AMAP/GMA (2015) (Muntean et al., 2018)</th><th colspan="3">(Steenhuisen and Wilson, 2019)</th></tr><tr><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th></tr></thead><tbody><tr><td>Solid biomass</td><td>50</td><td>40</td><td>10</td><td>80</td><td>15</td><td>5</td><td>74.4</td><td>4.8</td><td>20.8</td><td>75</td><td>5</td><td>20</td></tr><tr><td>Liquid and gas</td><td>50</td><td>40</td><td>10</td><td>80</td><td>15</td><td>5</td><td>50</td><td>40</td><td>10</td><td>50</td><td>40</td><td>10</td></tr><tr><td>Coal, CFPP, bituminous</td><td>50</td><td>40</td><td>10</td><td>80</td><td>15</td><td>5</td><td>NA</td><td>NA</td><td>NA</td><td>80</td><td>5</td><td>15</td></tr><tr><td>– ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>84.99</td><td>14.76</td><td>0.25</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>78</td><td>21.1</td><td>0.9</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF + dryFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>78</td><td>21.1</td><td>0.9</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– ESP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>62.5</td><td>37</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>50</td><td>49.5</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– SNCR + ESP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>64.4</td><td>34</td><td>1.6</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– SCR + ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>71</td><td>28.9</td><td>0.1</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– no EoP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>56</td><td>34</td><td>10</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>
<table><caption>Table E4. Continued.</caption><thead><tr><th rowspan="3">Sectors/fuel</th><th colspan="3">WHET<br>(AMAP/UNEP, 2008)</th><th colspan="3">EDGAR<br>(Zhang et al., 2016b)</th><th colspan="3">AMAP/GMA (2015)<br>(Muntean et al., 2018)</th><th colspan="3">(Steenhuisen and Wilson, 2019)</th></tr><tr><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th><th>Hg(0)</th><th>Hg(II)<sub>g</sub></th><th>Hg(II)<sub>p</sub></th></tr><tr><th>50</th><th>40</th><th>10</th><th>80</th><th>15</th><th>5</th><th>NA</th><th>NA</th><th>NA</th><th>80</th><th>5</th><th>15</th></tr></thead><tbody><tr><td><strong>Coal, CFPP, lignite</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>– ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>84.99</td><td>14.76</td><td>0.25</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– ESP + dryFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>84.99</td><td>14.76</td><td>0.25</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF + ESP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>50</td><td>49.5</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– ESP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>62.5</td><td>37</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– SCR + ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>71</td><td>28.9</td><td>0.1</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– no EoP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>56</td><td>34</td><td>10</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td><strong>Coal, CFPP, sub-bituminous</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>– ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>84.99</td><td>14.76</td><td>0.25</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF + dryFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>78</td><td>21.1</td><td>0.9</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– ESP + dryFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>84.99</td><td>14.76</td><td>0.25</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– FF</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>50</td><td>49.5</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– ESP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>62.5</td><td>37</td><td>0.5</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– SCR + ESP + wetFGD</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>71</td><td>28.9</td><td>0.1</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– no EoP</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>56</td><td>34</td><td>10</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td><strong>Combustion in residential</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>– coal</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>83.1</td><td>6.9</td><td>10</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– solid biomass</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>74.4</td><td>4.8</td><td>20.8</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– liquid/gas</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>50</td><td>40</td><td>10</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td><strong>Cement production</strong></td><td>80</td><td>15</td><td>5</td><td>80</td><td>15</td><td>5</td><td>67.6</td><td>28.4</td><td>4</td><td>35</td><td>65</td><td>0</td></tr><tr><td><strong>Iron and steel</strong></td><td>80</td><td>15</td><td>5</td><td>80</td><td>15</td><td>5</td><td>32.1</td><td>62.9</td><td>5</td><td>35</td><td>65</td><td>0</td></tr><tr><td><strong>Copper smelting</strong></td><td>80</td><td>15</td><td>5</td><td>80</td><td>15</td><td>5</td><td>44.5</td><td>50.5</td><td>5</td><td>45</td><td>50</td><td>5</td></tr><tr><td><strong>Lead smelting</strong></td><td>80</td><td>15</td><td>5</td><td>80</td><td>15</td><td>5</td><td>49.7</td><td>45.3</td><td>5</td><td>45</td><td>50</td><td>5</td></tr><tr><td><strong>Zinc smelting</strong></td><td>80</td><td>15</td><td>5</td><td>80</td><td>15</td><td>5</td><td>37.7</td><td>57.4</td><td>5</td><td>45</td><td>50</td><td>5</td></tr><tr><td><strong>Waste incineration</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>– agricultural</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>96</td><td>0</td><td>4</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– municipal</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>17.7</td><td>82</td><td>0.3</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>– industrial</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2.8</td><td>97.2</td><td>0</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td><strong>Mercury production</strong></td><td>80</td><td>20</td><td>0</td><td>80</td><td>20</td><td>0</td><td>80</td><td>20</td><td>0</td><td>80</td><td>15</td><td>0</td></tr><tr><td><strong>Artisanal and small-scale<br>gold production</strong></td><td>100</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td></tr><tr><td><strong>Chlor-alkali industry</strong></td><td>70</td><td>30</td><td>0</td><td>70</td><td>30</td><td>0</td><td>70</td><td>30</td><td>0</td><td>100</td><td>0</td><td>0</td></tr></tbody></table>

NA: not available.

## E2 Future anthropogenic emissions

<table><caption>Table E5. Abbreviations used in future emission tables.</caption><tr><td>ASGM</td><td>Artisanal and small-scale mining</td></tr><tr><td>BAS</td><td>Baseline: business as usual for energy and climate policy</td></tr><tr><td>BAU</td><td>Business as usual: continuation of current air pollution and climate policies</td></tr><tr><td>CLE</td><td>Current legislation</td></tr><tr><td>CLIM</td><td>Climate mitigation scenario for energy and activities</td></tr><tr><td>EC</td><td>Emission control</td></tr><tr><td>ETP</td><td>Energy Technology Perspectives</td></tr><tr><td>GMA'18</td><td>Global Mercury Assessment 2018 (AMAP/UN Environnement, 2019)</td></tr><tr><td>GMA'13</td><td>Global Mercury Assessment 2013 (AMAP/UNEP, 2013)</td></tr><tr><td>GHG</td><td>Greenhouse gases</td></tr><tr><td>MFR</td><td>Maximum (technologically) feasible reduction</td></tr><tr><td>MINA</td><td>Minamata policy scenario</td></tr><tr><td>NAP</td><td>National Action Plan</td></tr><tr><td>NFME</td><td>Nonferrous metals</td></tr></table>
<table><caption>**Table E5. Continued.**</caption><tr><td>NP</td><td>No policy</td></tr><tr><td>PM</td><td>Particulate matter</td></tr><tr><td>RED/BAN</td><td>Reduction or ban of Hg use in activity</td></tr><tr><td>SRES</td><td>Special Report on Emission Scenarios</td></tr><tr><td>VCM</td><td>Vinyl chloride monomer</td></tr><tr><td>WEO</td><td>World Energy Outlook</td></tr></table>

<table><caption>**Table E6.** Scenario design for future Hg emissions: considerations and resulting scenarios.</caption><thead><tr><th></th><th>Considerations</th><th>Scenario options</th></tr></thead><tbody><tr><td>Energy projections</td><td><ul><li>Energy scenarios</li><li>Represent future development of energy systems</li><li>Take into account energy and climate policy</li><li>Usually also include projections of (energy-intensive) industries</li></ul></td><td><ul><li>BAS: baseline scenario assuming current energy and climate policy into the future</li><li>CLIM: more ambitious climate scenarios, e.g., taking into consideration net-zero pathways</li></ul></td></tr><tr><td>Air quality policy</td><td><ul><li>Assumptions about PM, SO<sub>2</sub>, and NO<sub>x</sub> control further influence future Hg emission levels and speciation through co-benefits</li><li>Assumptions about Hg-specific policy, e.g., implementation of the Minamata Convention</li></ul></td><td><ul><li>NOC: No policy scenario provides a baseline of emission without any assumed interventions</li><li>CLE: current legislation scenario reflecting current Hg, PM, SO<sub>2</sub> and NO<sub>x</sub> policy</li><li>MINA: Minamata policy scenario; reflects full adoption of Minamata Hg reduction targets in relevant sectors, including National Action Plans (NAPs) for ASGM</li><li>COFR: Maximum (technologically) feasible reduction scenario; reflects full application of the most efficient air pollution control methods, regardless of cost</li><li>HGMFR: Maximum (technologically) feasible reduction of Hg using Hg-specific technologies, regardless of cost</li></ul></td></tr></tbody></table>

<table><caption>**Table E7.** Data sources and other considerations for building future Hg emission scenarios.</caption><thead><tr><th></th><th>Considerations</th><th>Options</th></tr></thead><tbody><tr><td>Base year emission inventory</td><td><ul><li>Gridding of emissions (i.e., spatial distribution)</li><li>Definition of sectors must be consistent with available activity projections (e.g., separation of emissions from fuels and other input materials in industrial processes)</li><li>Calibration to existing inventories and statistics</li></ul></td><td><ul><li>EDGAR4vtox2 (this inventory will be used for future projections by TAPS model)</li><li>AMAP/UN Environment 2013 (GAINS v3, Rafaj et al., 2013 is calibrated to this inventory)</li><li>GMA'18 (UNEP, 2019) (GAINS v4 is calibrated to this inventory)</li><li>STREETS</li></ul></td></tr><tr><td>Source of activity projections</td><td><ul><li>Global or regional consistency</li><li>Granularity</li><li>Time steps</li><li>Sectoral coverage, e.g., availability of projections for Hg-specific sectors like ASGM, generated waste, or VCM production.</li><li>Timing of climate policies, projections of population growth, poverty, etc.</li></ul></td><td><ul><li>IEA World Energy Outlook</li><li>MIT's EPPA model</li><li>Other integrated assessment models</li><li>Regional energy models</li><li>Own projections</li></ul></td></tr><tr><td>Choice of projected sectors</td><td><ul><li>Sector alignment with baseline emission inventory</li></ul></td><td><ul><li>Maximum number of available sectors</li><li>Focus on sectors explicitly covered by the Minamata Convention (see Table E10).</li></ul></td></tr></tbody></table>
<table><caption>**Table E7. Continued.**</caption><thead><tr><th></th><th>Considerations</th><th>Options</th></tr></thead><tbody><tr><td>Representation of air pollutant control technologies</td><td>– Implicit – e.g., represented as variable emission factors for a selected activity that might lower over time, as technology or efficiency improvements are made<br/>– Explicit – e.g., represented as a distinct choice of control options per modeling region, which leads to a lowering of the unabated emission factor of an activity to a final abated emission factor</td><td>– Global: e.g., TAPS model (not yet implemented)<br/>– Global: GAINS model (mirrors the approach in GMA'18)<br/>– Regional: CAME model (China)</td></tr><tr><td>Time steps</td><td>– Present only “end point” of a scenario or equally spaced time steps?</td><td>– Yearly steps up to 2050<br/>– 5-year steps up to 2050 (e.g., GAINS model)<br/>– Only present endpoint (e.g., Pacyna et al., 2016)</td></tr></tbody></table>

<table><caption>**Table E8.** Global projections of mercury emissions in the literature. BAS – baseline energy or activity scenario; CLIM – climate mitigation scenario for energy and activities; CLE – current legislation for Hg pollution control; MFR – maximum feasible Hg reduction; SRES – Special Report on Emission Scenarios; WEO – World Energy Outlook; ETP – Energy Technology Perspectives.</caption><thead><tr><th>Scale</th><th>Reference</th><th>Base year</th><th>Projection year</th><th>Inventory used</th><th>Policy scenarios – activities/Energy/ Hg control</th><th>Specification</th><th>Emissions gridded?</th></tr></thead><tbody><tr><td>Global</td><td>Streets et al. (2009)</td><td>2006</td><td>2050</td><td>STREETS</td><td>IPCC SRES scenarios:<br/>(1) A1B<br/>(2) A2<br/>(3) B1<br/>(4) B2</td><td>Hg(0)<br/>Hg(II)<sub>g</sub><br/>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>Global</td><td>Rafaj et al. (2013)</td><td>2010</td><td>2050</td><td>Own/calibrated to UNEP/AMAP 2013</td><td>IEA WEO 2012, supplemented by own projections for gold, caustic soda production, other Hg emissions<br/>(1) BAS (a) CLE<br/>(b) MFR<br/>(2) CLIM (a) CLE<br/>(b) MFR</td><td>Hg(0)<br/>Hg(II)<sub>g</sub><br/>Hg(II)<sub>p</sub></td><td>No</td></tr><tr><td>Global</td><td>Pacyna et al. (2010)</td><td>2005</td><td>2020</td><td>Own</td><td>(1) BAS: business as usual<br/>(2) Policy scenario (Hg focus)<br/>(3) Deep Green scenario (Hg focus)</td><td>Hg(0)<br/>Hg(II)<sub>g</sub><br/>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>Global</td><td>Lei et al. (2014)</td><td>2000</td><td>2050</td><td>STREETS, Pacyna et al. (2006a)</td><td>IPCC SRES scenarios:<br/>(1) A1FI<br/>(2) A1B<br/>(3) B1</td><td></td><td>Yes</td></tr><tr><td>Global</td><td>Pacyna et al. (2016)</td><td>2010</td><td>2035</td><td>AMAP/UNEP 2013</td><td>Own, using a variety of data sources for different activities;<br/>(1) CLE<br/>(2) New policies, 450 ppm CO<sub>2</sub><br/>(3) MFR</td><td>Hg(0)<br/>Hg(II)<sub>g</sub><br/>Hg(II)<sub>p</sub></td><td>Yes</td></tr></tbody></table>
<table><caption>**Table E9.** Regional projections of mercury emissions in the literature. APCD – air pollution control device; BAS – baseline energy or activity scenario; BAU – business-as-usual scenario; CCS – carbon capture and storage; CLIM – climate mitigation scenario for energy and activities; CLE – current legislation for Hg pollution control; HgCD – Hg control device; MFR – maximum feasible Hg reduction; SRES – Special Report on Emission Scenarios; WEO – IEA World Energy Outlook.</caption><thead><tr><th>Scale</th><th>Reference</th><th>Base year</th><th>Projection year</th><th>Inventory used</th><th>Policy scenarios – activities and energy Hg control</th><th>Specification</th><th>Emissions gridded?</th></tr></thead><tbody><tr><td>Europe</td><td>Pacyna et al. (2006a)</td><td>2000</td><td>2020</td><td>Own</td><td>Own projections<br>(1) BAU + policy scenario<br>(2) BAU + Deep Green scenario<br>(Hg focus)</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>Poland</td><td>Glodek et al. (2010)</td><td>2005</td><td>2020</td><td>Hlawczika et al. (2006) as reported to EMEP</td><td>Own projections<br>(1) BAU + extended controls<br>(2) BAU + MFR</td><td>THg</td><td>No</td></tr><tr><td>India</td><td>Chakraborty et al. (2015)</td><td>2001</td><td>2020</td><td>Own</td><td>Own projections<br>(1) BAU + phase-out of intentional Hg applications</td><td>THg</td><td>No</td></tr><tr><td>Europe</td><td>Rafaj et al. (2014)</td><td>2010</td><td>2050</td><td>Own (GAINS)</td><td>EU ReMix renewable energy model<br>(1) BAS + CLE<br>max. renewable power + CLE</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>China, India</td><td>Giang et al. (2015)</td><td>2010</td><td>2050</td><td>GMA'13 (UNEP, 2013)</td><td>IPCC SRES scenarios:<br>A1B<br>B1<br>Minamata Flexible<br>Minamata Strict<br>Minamata Flexible<br>Minamata Strict</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>China</td><td>Zhao et al. (2015)</td><td>2012</td><td>2030</td><td>Own</td><td>Own projections + WEO2012<br>(1) No future policy<br>(2) New policy (based on WEO2012)<br>New policy + APCDs</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td><td>Yes</td></tr><tr><td>China</td><td>Ancora et al. (2016)</td><td>2010</td><td>2030</td><td>Own</td><td>Own projections<br>(1) BAU<br>(2) Minamata Low<br>(3) Minamata High<br>(4) Minamata Medium</td><td></td><td>No</td></tr><tr><td>China</td><td>Wu et al. (2018a)</td><td>2015</td><td>2030</td><td>Own</td><td>Own<br>(1) CLE<br>(2) Climate Policy + Minamata</td><td>Technical paths to ELVs (emission limit values) of 15/5/2 µg m<sup>-3</sup></td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td></tr><tr><td>China</td><td>Wu et al. (2018c)</td><td>2015</td><td>2030</td><td>Own</td><td>Own<br>(1) CLE<br>(2) Climate Policy + Minamata</td><td>(a) BAU<br>(b) Extended emission control<br>(c) Accelerated emission control</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td></tr><tr><td>China</td><td>Mulvaney et al. (2020)</td><td>2012</td><td>2030</td><td>EDGAR.v4tox2</td><td>China Regional Energy Model (C-REM)<br>(1) No climate or Hg policy<br>(2)–(4) Climate scenarios</td><td>Minamata APCDs</td><td>Hg(0)<br>Hg(II)<sub>g</sub><br>Hg(II)<sub>p</sub></td></tr><tr><td>China</td><td>Guo et al. (2022)</td><td>2020</td><td>2060</td><td>Own</td><td>(a) coal washing<br>(b) APCD<br>(c) operating hours<br>(d) plant lifetime</td><td></td><td>THg</td></tr><tr><td>8 Copper-producing countries</td><td>Yamamoto et al. (2023)</td><td>2020</td><td>2050</td><td>Own</td><td>(1)–(3) Varying copper stock levels</td><td>(a)–(d) Varying Cu recycling rates and HgCD efficiency</td><td>THg</td></tr><tr><td>India</td><td>Vishwanathan et al. (2023)</td><td>2020(?)</td><td>2070</td><td>Own</td><td>(1) BAS<br>(2) CLIM + CCS<br>(3) CLIM + renewables</td><td>(a) No HgCD<br>(b) cheapest HgCD<br>(c) best HgCD</td><td>THg</td></tr></tbody></table>
<table><caption>**Table E10.** Anthropogenic emission sectors covered by different future global emission scenario modeling efforts in the literature. ASGM – artisanal and small-scale gold mining; EC – emission control; Red/BAN – reduction or ban; VCM – vinyl chloride monomer production.</caption><thead><tr><th rowspan="2">Minamata target →</th><th rowspan="2">Base year</th><th colspan="3">Projection year</th><th colspan="3">Combustion</th><th rowspan="2">Industry Smelting</th><th rowspan="2">ASGM</th><th rowspan="2">Hg containing products</th><th rowspan="2">Waste</th><th rowspan="2">Transport</th></tr><tr><th>EC</th><th>Power</th><th>Coal</th><th>Oil</th><th>Biomass</th></tr></thead><tbody><tr><td>Streets et al. (2009)</td><td>2006</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pacyna et al. (2010)</td><td>2005</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rafaj et al. (2013)</td><td>2010</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lei et al. (2014)</td><td>2010</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pacyna et al. (2016)</td><td>2010</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="13">Options for future Hg emission scenario modeling:</td></tr><tr><td>Option 1: all available</td><td>2015</td><td>2050</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Option 2: Minamata</td><td>2015</td><td>2050</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr></tbody></table>
<table><caption>**Table E11.** Proposed setup for GAINS v4 and TAPS future Hg emission scenarios.</caption><thead><tr><th></th><th>GAINS v4</th><th>TAPS</th></tr></thead><tbody><tr><td>Model name</td><td>Greenhouse Gas and Air Pollution Interaction and Synergies model v4</td><td>Tool for Air Pollution Scenarios</td></tr><tr><td>Associated research group</td><td>Pollution Management Group, International Institute of Applied Systems Analysis (IIASA)</td><td>Selin Group, Massachusetts Institute of Technology (MIT)</td></tr><tr><td>Base year – projection year (time steps)</td><td>2015–2050 (5-year steps)</td><td>2015–2050 (5-year steps)</td></tr><tr><td>Emission inventory</td><td>AMAP/UN-Environment 2018</td><td>EDGAR</td></tr><tr><td>Activity projections</td><td>Energy projections:<br>Wide range of climate and air quality scenarios such as WEO2022<br>Hg-specific:<br>Conservative activity projections for caustic soda production, VCM production and ASGM</td><td>EPPA v7 model</td></tr><tr><td>Sectoral coverage</td><td>Large sectoral coverage, especially of the Convention-relevant sectors (see Table E10)</td><td>EDGAR sectors (with a few aggregations)</td></tr><tr><td>Regional resolution</td><td>183 GAINS regions, presented in 17 aggregated WEO2022 regions</td><td>18 EPPA regions</td></tr><tr><td>Hg policy Scenarios</td><td>Current legislation (CLE):<br>– Reflects current policy for mercury<br>– Reflects current policy PM, SO<sub>2</sub> and greenhouse gases<br>Minamata policy scenario (MINA):<br>– Reflects full adoption of Minamata Hg reduction targets in relevant sectors<br>Maximum (technologically) feasible reduction scenario (MFR):<br>– Reflects minimum anthropogenic emissions, based on full application of the most efficient technologies, regardless of cost constraints.</td><td>Combination of EPPA v7 scenarios (e.g., Paris Forever, Paris 2 °C, Paris 1.5 °C, Accelerated actions) with Hg emission scenarios</td></tr><tr><td>Representation of Hg control options</td><td>Explicit – Hg-specific control technologies have been updated between 2021 and 2022, allowing for combining co-benefits from PM and SO<sub>2</sub> abatement with Hg-specific removal technologies.</td><td>Implicit – e.g., represented as variable emission factors for a selected activity that might lower over time, as technology or efficiency improvements are made</td></tr><tr><td>Gridding</td><td>Under implementation – resolution: 0.5° × 0.5° globally, 0.1° × 0.1° for some urban areas</td><td>0.1° × 0.1°</td></tr></tbody></table>

<table><caption>**Table E12.** Proposed future anthropogenic Hg emission scenarios.</caption><thead><tr><th>Activity projections:<br>Energy, population, climate policy,<br>industry, Hg-specific activities</th><th>No. of<br>regions</th><th>Air pollutant control<br>policy:<br>PM–SO<sub>2</sub> control</th><th>Mercury-specific policy:<br>Hg control</th><th>Scenario ID</th></tr></thead><tbody><tr><td rowspan="3">BAS scenario: current<br>policy</td><td rowspan="3">15<br>world<br>regions</td><td>NOC</td><td>NOC</td><td>00_BAS_NOC</td></tr><tr><td>CLE</td><td>CLE<br>CLE + Minamata NAPs</td><td>01_BAS_CLE<br>02_BAS_MINA</td></tr><tr><td>coMFR</td><td>CLE<br>HgMFR</td><td>03_BAS_coMFR<br>04_BAS_HgMFR</td></tr><tr><td>Announced Policies (CLIM1)<br>Scenario: including announced<br>climate targets</td><td>15<br>world<br>regions</td><td>CLE</td><td>CLE</td><td>05_CLIM1_CLE</td></tr><tr><td>Net-Zero Emissions (CLIM2)</td><td>Global</td><td>CLE</td><td>CLE</td><td>06_CLIM2_CLE</td></tr><tr><td>Scenario</td><td></td><td>coMFR</td><td>HgMFR</td><td>07_CLIM2_HgMFR</td></tr></tbody></table>
## Appendix F: Additional details on the GAINS model

IIASA's GAINS model has been used to evaluate air pollution and greenhouse gas policies for several decades (Amann et al., 2011). As a multi-pollutant model, GAINS includes emission factors and control strategies for SO₂, PM, NH₃, N₂O, NOₓ, CH₄, CO₂, CO, VOC, F-gases and Hg from anthropogenic emission sources. Currently, 55 emission sectors are combined with 15 activity types are associated with Hg emission factors, leading to a nuanced description of Hg emissions. The model relies on exogenous activity inputs from integrated assessment models, extended by additional mercury-specific activity inputs. It contains a bottom-up inventory of pollution control options for each pollutant, on the basis of its 182 regions on the global level, which for many regions are coordinated in consultation with local experts. Mercury co-benefits from existing PM and SO₂ control are calculated automatically for all applicable combustion and industry sectors and additional mercury control strategies can be added, based on current and future policy. Results are presented in 5-year steps up to 2050.

The following paragraphs describe the most current Hg-GAINS scenarios, for which publication is planned in 2023.

### Inventory used and activity projections

The World Energy Outlook scenarios for 2022 are currently implemented in GAINS and have been used as activities for the most recent Hg scenarios. Data on ASGM, caustic soda production, Hg mining, and VCM were derived from the Global Mercury Assessment, USGS mineral yearbooks, and similar sources. ASGM and VCM production are kept constant until 2050, representing a conservative estimate which can be used to illustrate policy changes clearly. The ratio of bodies cremated is kept constant until 2050, but cremation activities are driven by population growth.

### Emission factors

Unabated mercury emission factors in GAINS are derived from different literature sources and the Global Mercury Assessment, aggregated (e.g., the nonferrous metal sector) and disaggregated (e.g., the iron and steel sectors) as appropriate to the model resolution. Emission factors for the combustion sectors are documented in Rafaj et al. (2013), and others have been developed based on information in the GMA'18 (Global Mercury Assessment 2018, 2020). Unabated emission factors are constant through all the model years. Specific emission factors were developed for the waste sector. GAINsV4 was calibrated to the 2015 emissions in GMA'18 (highest transparency), and all changes are documented.

## Air pollution control policies in GAINS

The global mercury scenarios make use of the most up-to-date PM and SO₂ control policies from the recently implemented WEO2022 scenarios, utilizing both the CLE and cost-optimal maximum feasible reduction (MFR) scenarios for PM and SO₂ control. Hg co-benefits for the PM–SO₂ CLE and MFR are calculated within the model for all types of combustion and industry sectors. Where relevant legislation exists, e.g., in the EU, Hg-specific control options are then added to the co-benefit control strategy. Where a Hg-specific control strategy overlaps with existing PM or SO₂, a hierarchy of controls is implemented whereby the most efficient ones are assumed to be implemented in the most technologically advanced installations. Additional Hg-specific controls include, e.g., the EU directive on large combustion plants concerning waste incineration, phase-out of caustic soda production using mercury cells, and all Minamata National Action Plans for ASGM reduction published until December 2022.

Results from the scenarios will be reported following the regional resolution of the IEA World Energy Outlook 2022, which uses 15 world regions: (1) North America excluding the United States; (2) the United States; (3) Central and South America excluding Brazil; (4) Brazil; (5) Europe excluding the European Union; (6) the European Union; (7) Africa excluding South Africa; (8) South Africa; (9) the Middle East; (10) Eurasia; (11) Asia Pacific excluding China, India, Japan, and Southeast Asia; (12) China; (13) India; (14) Japan; and (15) Southeast Asia.
Appendix G: Models, drivers, simulations, and outputs

<table><caption>Table F1. Three-dimensional global and regional atmospheric Hg models.</caption><thead><tr><th>Name</th><th>Model type</th><th>Horizontal resolution</th><th>Vertical resolution</th><th>Gas-phase chemistry</th><th>Aqueous chemistry</th><th>Reference</th></tr></thead><tbody><tr><td colspan="7"><em>Global models</em></td></tr><tr><td>GISS-CTM</td><td>Atmospheric</td><td>8° × 10°</td><td>9 levels, top 10 hPa</td><td>O<sub>3</sub></td><td>Yes</td><td>Shia et al. (1999)</td></tr><tr><td>CAM-Chem</td><td>Multimedia</td><td>0.9° × 1.25°</td><td>32 levels, top 2 hPa</td><td>O<sub>3</sub>, OH</td><td>Yes</td><td>Lei et al. (2013); Zhang and Zhang (2022)</td></tr><tr><td>ECHMERIT</td><td>Atmospheric</td><td>2.8° × 2.8°</td><td>19 levels, top 10 hPa</td><td>O<sub>3</sub>, OH</td><td>Yes</td><td>Jung et al. (2009)</td></tr><tr><td>GEM-MACH-Hg</td><td>Atmospheric (global and regional)</td><td>0.5° × 0.5°</td><td>58 levels, top 7 hPa</td><td>OH, bromine species</td><td>No</td><td>Dastoor et al. (2021); Zhou et al. (2021)</td></tr><tr><td>GEOS-Chem</td><td>Atmospheric</td><td>2° × 2.5°</td><td>47 levels, top 0.01 hPa</td><td>Br, OH, O<sub>3</sub>, Cl, NO<sub>2</sub>, CO</td><td>Yes</td><td>Shah et al. (2021)</td></tr><tr><td>GLEMOS</td><td>Atmospheric</td><td>1° × 1°</td><td>20 levels, top 10 hPa</td><td>O<sub>3</sub>, OH, Br</td><td>Yes</td><td>Travnikov and Ilyin (2009)</td></tr><tr><td>WACCM</td><td>Atmospheric</td><td>1.9° × 2.5°</td><td>88 levels, top 6 × 10<sup>-6</sup> hPa (140 km)</td><td>Cl, OH, Br, O<sub>3</sub></td><td>No</td><td>Saiz-Lopez et al. (2022)</td></tr><tr><td colspan="7"><em>Regional models</em></td></tr><tr><td>CMAQ-newHg-Br</td><td>Atmospheric</td><td>12 × 12 km</td><td>35 vertical layers</td><td>O<sub>3</sub>, OH, H<sub>2</sub>O<sub>2</sub>, Cl<sub>2</sub>, Cl, Br</td><td>Yes</td><td>Ye et al. (2018a); Wu et al. (2024)</td></tr><tr><td>WRF-Chem</td><td>Atmospheric</td><td>100 × 100 km</td><td>72 levels up to 50 hPa</td><td>Br, Cl, OH</td><td>No</td><td>Gencarelli et al. (2014); Ahmed et al. (2023)</td></tr><tr><td>GEOS-Chem nested</td><td>Atmospheric</td><td>0.5 × 0.625° or 0.25 × 0.3125°</td><td>47 levels, top 0.01 hPa</td><td>Br, OH, O<sub>3</sub></td><td>Yes</td><td>Wang et al. (2014a); Zhang et al. (2012)</td></tr><tr><td>WRF-GC-Hg</td><td>Atmospheric</td><td>50 × 50 km or 25 × 25 km</td><td>47 levels, top 0.01 hPa</td><td>Br, OH, O<sub>3</sub></td><td>Yes</td><td>Xu et al. (2022)</td></tr></tbody></table>
<table><caption>**Table F2.** Three-dimensional global and regional ocean Hg models.</caption><thead><tr><th>Name</th><th>Model type</th><th>Ocean</th><th>Biogeochemistry</th><th>Horizontal resolution</th><th>Vertical resolution</th><th>Hg species</th><th>Chemistry scheme</th><th>Reference</th></tr></thead><tbody><tr><td>FATE-Hg</td><td>Global</td><td>FATE</td><td>NA</td><td>1.0°</td><td>55</td><td>Hg(0), Hg(II), MMHg, DMHg</td><td>Kawai et al. (2020)</td><td>Kawai et al. (2020)</td></tr><tr><td>OGSTM-BFM-Hg</td><td>Regional</td><td>NEMO</td><td>OGSTM-BFM</td><td>1/16°</td><td>70</td><td>Hg(0), Hg(II), MMHg, DMHg, 4 phytoplankton MMHg, 4 zooplankton MMHg</td><td>Rosati et al. (2022)</td><td>Rosati et al. (2022)</td></tr><tr><td>MITgcm</td><td>Global</td><td>MITgcm</td><td>Darwin</td><td>LLC90 grid with a nominal resolution 1° × 1°</td><td>50 levels with depths 10–500 m of</td><td>Hg(0), Hg(II), Hg(II)<sub>p</sub>, MMHg, DMHg, and MMHgP in water; MMHg in 6 phytoplankton and 2 zooplankton</td><td>*</td><td>Zhang et al. (2019c)</td></tr><tr><td>MERCY</td><td>Global</td><td>ICON-O</td><td>ECOSMO</td><td>7 to 148 km unstructured triangular grid</td><td>z- or z*- levels with depths of 15–200 m</td><td>14 Hg species in water; 21 in biota (3 phytoplankton, 2 zooplankton, 1 macrobenthos, 2 fish)</td><td>MERCY</td><td>Bieser et al. (2023)</td></tr></tbody></table>

<table><caption>**Table F3.** Multimedia mass balance models. The numbers in parentheses give the number of boxes per media.</caption><thead><tr><th>Name</th><th>Number of compartments</th><th>Media</th><th>Mercury species</th><th>Spatial scope</th><th>References</th></tr></thead><tbody><tr><td>Global Biogeochemical Box Model (GBC)</td><td>7</td><td>Atmosphere (1)<br/>Ocean (3)<br/>Terrestrial (3)</td><td>THg</td><td>Global</td><td>Amos et al. (2013, 2014, 2015)</td></tr><tr><td>Six Box Biogeochemical Cycle Model</td><td>6</td><td>Atmosphere (2)<br/>Ocean (3)<br/>Terrestrial (2)</td><td>THg</td><td>Global</td><td>Selin (2014)</td></tr><tr><td>Atm-Hg_3boxmodel</td><td>3</td><td>Atmosphere (3)</td><td>Hg(0),<br/>Hg(II)</td><td>Hemispheric</td><td>Feinberg et al. (2022)</td></tr><tr><td>Arctic Ocean Hg and MeHg Mass Budget Model</td><td>2</td><td>Ocean (2)</td><td>Hg(0),<br/>Hg(II),<br/>MMHg,<br/>DMHg</td><td>Regional (Arctic<br/>Ocean)</td><td>Soerensen et al. (2016a);<br/>Schartup et al. (2022)</td></tr><tr><td>WorM³</td><td>5</td><td>Atmosphere (1)<br/>Soil (1)<br/>Vegetation (1)<br/>Ocean (2)</td><td>Hg(0),<br/>Hg(II)<sub>g</sub>,<br/>Hg(II)<sub>p</sub>,<br/>Hg(II)<sub>aq</sub></td><td>Global</td><td>Qureshi et al. (2011)</td></tr></tbody></table>

<footnote>* The inorganic Hg chemistry in MITgcm-Hg includes photochemical and biological redox conversions between dissolved Hg(0) and Hg(II) in the oceanic mixed layer and biologically mediated redox reactions in the subsurface ocean. The dark oxidation of dissolved Hg(0) in the mixed layer is also included. The formation rates of methylated Hg are parameterized according to microbial remineralization of organic carbon. The photochemical and dark demethylation processes are included. The rate constants of photochemical reactions are scaled by the shortwave radiation intensity attenuated by ocean pigments (chlorophyll and dissolved organic carbon). The dark demethylation rate is calculated as a function of seawater temperature. CH₃Hg uptake by phytoplankton is simulated with volume concentration factors as a function of cell size and seawater DOC concentration. The trophic transfer of CH₃Hg to zooplankton is modeled with the processes including zooplankton grazing, excretion, and mortality. NA is not applicable.</footnote>
<table><caption>**Table F4.** Exposure-risk models.</caption><thead><tr><th>Research scope</th><th>Types of sources</th><th>Exposure pathways</th><th>Data sources of Hg in food</th><th>Risk endpoint</th><th>References</th></tr></thead><tbody><tr><td>Region</td><td>Coal-fired power plant</td><td>Fish ingestion</td><td>Survey data</td><td>Adult paresthesia</td><td>Fthenakis et al. (1995)</td></tr><tr><td>Region (nearby the plant)</td><td>MSW gasification plant</td><td>Inhalation, Soil ingestion, Dermal contact and Diet</td><td>X</td><td>Hazard Index (HI)</td><td>Lonati and Zanoni (2013)</td></tr><tr><td>Region (China)</td><td>China Atmospheric Hg Emission</td><td>Diet</td><td>Obtained from Previous Studies by Authoritative Scientific Research</td><td>IQ and Fatal Heart Attacks (FHA)</td><td>Chen et al. (2019)</td></tr><tr><td>Region (China)</td><td>Coal-fired power plants retrofitting measures</td><td>Diet (10 kinds of food)</td><td>Obtained from previous studies</td><td>IQ and Fatal Heart Attacks</td><td>Li et al. (2020b)</td></tr><tr><td>Global</td><td>Artisanal and small-scale gold mining (ASGM)</td><td>Seafood, freshwater fish, and rice</td><td>Food Intake Inventory and Literature-collected</td><td>IQ and Fatal Heart Attacks and Monetized Impact</td><td>Pang et al. (2022)</td></tr><tr><td>Global</td><td>Global Atmospheric Mercury Emissions</td><td>Seafood, freshwater fish and rice</td><td>Food Intake Inventory and Literature-Collected</td><td>IQ and Fatal Heart Attacks and Monetized global Impact</td><td>Zhang et al. (2021b)</td></tr><tr><td>Region (Pacific Ocean)</td><td>Anthropogenic Mercury Release</td><td></td><td>X</td><td>Pacific Ocean Tuna MeHg Concentration</td><td>Médieu et al. (2022)</td></tr><tr><td>Global</td><td></td><td>Rice</td><td>FAO data and Peer-reviewed Publications</td><td>IQ and Fatal Heart Attacks</td><td>Liu et al. (2019b)</td></tr></tbody></table>
<table><caption>**Table F5.** Global atmospheric model drivers. External and internal atmospheric model drivers. Different options for harmonizing drivers for the multimodel exercise are given in the last column, with the option for the baseline simulation being highlighted in bold.</caption><thead><tr><th>Drivers</th><th>GEOS-Chem</th><th>GEM-MACH-Hg</th><th>GLEMOS</th><th>ECHMERIT</th><th>CAM-Chem/Hg</th><th>Driver options for simulations</th></tr></thead><tbody><tr><td colspan="7"><strong>Meteorological</strong></td></tr><tr><td>Meteorological data</td><td>Offline. MERRA-2 or GEOS-FP reanalysis</td><td>Online. Global Environmental Multi-scale (GEM) model</td><td>Offline. WRF driven by the operational analysis data from ECMWF</td><td>Online. Fifth-generation Atmospheric General Circulation Model ECHAM5</td><td>Offline. NCEP/DOE AMIP II reanalysis</td><td>(1) Model-specific<br>(2) Using the same base reanalysis (when possible)</td></tr><tr><td colspan="7"><strong>Chemical</strong></td></tr><tr><td>Oxidant concentrations</td><td>Offline. Archived oxidant concentrations (Wang et al., 2021b)</td><td>Offline. Time-varying oxidant concentrations are from GEM-MACH (Makar et al., 2018); the Br concentrations are derived from satellite observations.</td><td>Offline. The Br, O<sub>3</sub>, and OH concentrations are imported from GEOS-Chem (Zaveri and Peters, 1999).</td><td>Online. The gas phase chemical mechanism is based on the CBMZ mechanism<br>The GEOS-Chem Hg aqueous phase mechanism is derived from MECCA (Sander et al., 2005)</td><td>Online for ozone, OH, sulfate, and chlorine based on the internal model; Monthly averaged dataset (CAM-Chem) derived from model.</td><td>(1) Model-specific<br>(2) Harmonized dataset<br>(GEOS-Chem)<br>(3) Harmonized</td></tr><tr><td>Aerosol data for Hg(II) partitioning</td><td>Offline. Archived aerosol concentrations</td><td>Offline. Time-varying concentrations from GEM-MACH (Makar et al., 2018)</td><td>Offline. Imported from GEOS-Chem</td><td>None</td><td>Online but assume 50/50 partitioning of Hg(II) (Holmes et al., 2010).</td><td>(1) Model-specific<br>(2) Harmonized dataset<br>(GEOS-Chem)</td></tr><tr><td>J values</td><td>Fast-JX v7.0a implemented in GEOS-Chem (Eastham et al., 2014)</td><td>JVAL14-MESSy based formulation (Sander et al., 2014)</td><td>Hg(II)<sub>g</sub> photo-reduction rates are pre-calculated by CAM-Chem (Saiz-Lopez et al., 2018, 2020)</td><td>Fast-J photolysis mechanism (Wild et al., 2000)</td><td>A combined (online-lookup table) approach (Lamarque et al., 2012)</td><td>(1) Model-specific</td></tr><tr><td colspan="7"><strong>Geophysical</strong></td></tr><tr><td>Land cover type</td><td>Olson 2001 land map (73 land use categories) with LAI data from MODIS</td><td>15 land use categories</td><td>17 land use categories (MODIS)</td><td>Land use characteristics are directly transferred from ECHAM5</td><td>Community Land Model (Oleson et al., 2010)</td><td>(1) Model-specific<br>(2) MODIS land cover dataset (MCD12Q1, https://doi.org/10.5067/ (Friedl and Sulla-Menashe, 2019)<br>(3) MODIS LAI (GEOS-Chem)</td></tr><tr><td colspan="7"><strong>Emissions</strong></td></tr><tr><td>Anthropogenic Hg emissions</td><td>Various</td><td>Various</td><td>Various</td><td>Various</td><td>Various</td><td>E2010/2020*</td></tr><tr><td>Geogenic Hg emissions</td><td>Geogenic emissions distributed according to the locations of mercury mines (Selin et al., 2007)</td><td></td><td></td><td></td><td></td><td>(1) Harmonized geogenic emissions inventory (Sect. 3.1)<br>(2) GEOS-Chem emission files</td></tr></tbody></table>
<table><caption>Table F5. Continued.</caption><thead><tr><th>Drivers</th><th>GEOS-Chem</th><th>GEM-MACH-Hg</th><th>GLEMOS</th><th>ECHMERIT</th><th>CAM-Chem/Hg</th><th>Driver options for simulations</th></tr></thead><tbody><tr><td colspan="7"><em>Emissions</em></td></tr><tr><td>Secondary Hg emissions (ocean)</td><td>Air-sea exchange of prescribed ocean Hg(0) concentration field (Horowitz et al., 2017)</td><td>Ocean emissions are determined by air-sea Hg(0) exchange scheme</td><td>Prescribed fluxes from seawater proportional to the primary production in seawater and based on global estimates (Lamborg et al., 2002; Mason, 2009)</td><td>Hg(0) ocean fluxes are calculated using the two-layer gas exchange model (Liss and Slater, 1974).</td><td>Ocean emissions are determined by a simplified air-sea exchange scheme</td><td>(1) Time-varying (2010–2020) sea surface Hg concentrations from multimodel ocean Hg average; harmonized air-sea exchange scheme (Appendix B)<br>(2) Model-specific<br>(3) Mass balance models for scaling legacy emissions magnitude and trend</td></tr><tr><td>Secondary Hg emissions (land)</td><td>The method of Selin et al. (2008)</td><td>Natural sources and re-emissions of previously deposited Hg from land are based on formulations by Gbor et al. (2007) and Shetty et al. (2008).</td><td>Prescribed fluxes from soil based on global estimates (Lamborg et al., 2002; Mason, 2009). Online calculated prompt re-emission from snow.</td><td>Soils and vegetation emissions are calculated offline and derived from the EDGAR/POET emission inventory (Granier et al., 2005)</td><td>Land sources include emissions from soil and vegetation, plus rapid re-emissions of deposited mercury</td><td>(1) Time-varying soil re-emissions based on global soil Hg concentration dataset and air-soil re-emissions modeling (Sect. 3.4)<br>(2) Model-specific<br>(3) Mass balance models for scaling legacy emissions magnitude and trend</td></tr><tr><td>Wildfire Hg emissions</td><td>Hg emissions factors are applied to an emission inventory, including: Global Fire Emissions Database (GFED) v4.1s (van der Werf et al., 2017), Quick Fire Emissions Dataset (QFEDv2), or Fire Inventory from NCAR (FINN)</td><td>Fire INventory from NCAR (FINN), and Hg emission factors (Fraser et al., 2018)</td><td>Fire INventory from NCAR (FINN). Hg emission factors from Andreae (2019)</td><td>Various</td><td>From the monthly means from the IPCC estimate of biomass burned and the IMAGE projection of managed forests</td><td>(1) Harmonized Wildfire emissions inventory (Sect. 3.3)<br>(2) GFED based emissions<br>(3) FINN based emissions</td></tr></tbody></table>

<footnote>* E2010/2020: Updated spatially distributed global anthropogenic emission inventories from 2010 to 2020 as available for the study (see Sect. 3.2.1).</footnote>
<table><caption>**Table F6.** Ocean model drivers. External and internal marine model environmental drivers that could be the cause of trends in regional or global ocean Hg concentration during the past and coming decades. References are focused on prior model studies on the impact of these drivers (regional to global context).</caption><thead><tr><th>Drivers</th><th>MERCY</th><th>MITgcm</th><th>Possibility of harmonizing drivers for simulations</th><th>References</th></tr></thead><tbody><tr><td colspan="5"><em>Atmospheric ensemble input</em></td></tr><tr><td>Atmospheric Hg(0) concentration</td><td>CMAQ-Hg/ICON-ART</td><td>GEOS-Chem</td><td>Atmospheric model ensemble</td><td>Schröter et al. (2018)</td></tr><tr><td>Hg deposition</td><td>CMAQ-Hg/ICON-ART</td><td>GEOS-Chem</td><td>Atmospheric model ensemble</td><td>Schröter et al. (2018)</td></tr><tr><td colspan="5"><em>Rivers</em></td></tr><tr><td>River freshwater discharge</td><td>HD</td><td>Liu et al. (2021b)</td><td>Liu et al. (2021b)</td><td>Hagemann and Dümenil (1997); Liu et al. (2021b)</td></tr><tr><td>Hg in river discharge</td><td>HydroPy</td><td>Liu et al. (2021b)</td><td>Liu et al. (2021b)</td><td>Stacke and Hagemann (2021); Liu et al. (2021b)</td></tr><tr><td>Nutrients in river discharge</td><td>HydroPy</td><td>NEWS</td><td>NEWS</td><td>Stacke and Hagemann (2021); Mayorga et al. (2010)</td></tr><tr><td>TOC in river discharge</td><td>GRDC</td><td>NEWS</td><td>NEWS</td><td>Mayorga et al. (2010)</td></tr><tr><td colspan="5"><em>Atmosphere</em></td></tr><tr><td>Precipitation</td><td>ERA5</td><td>ERA5</td><td>ERA5</td><td>Hersbach et al. (2020)</td></tr><tr><td>Wind speed</td><td>ERA5</td><td>ERA5</td><td>ERA5</td><td>Hersbach et al. (2020)</td></tr><tr><td>Surface shortwave radiation</td><td>ERA5</td><td>ERA5</td><td>ERA5</td><td>Hersbach et al. (2020)</td></tr><tr><td>Atmospheric N deposition</td><td>CAMS</td><td>n/a</td><td>CAMS</td><td>Inness et al. (2019); Schröter et al. (2018)</td></tr><tr><td>Atmospheric Fe deposition</td><td>CAMS</td><td>Mahowald et al. (2009)</td><td>CAMS</td><td>Inness et al. (2019); Schröter et al. (2018)</td></tr><tr><td>Air-sea exchange</td><td>Modeled variable (MERCY)</td><td>Modeled variable</td><td>Nightingale et al. (2000a)</td><td>Kuss et al. (2009), Kuss (2014); Nightingale et al. (2000a); Andersson et al. (2008a); Schwarzenbach et al. (2003); Liss (1973); Liss and Slater (1974)</td></tr><tr><td colspan="5"><em>Ocean</em></td></tr><tr><td>Bathymetry</td><td>GEBCO</td><td>ECCO v4</td><td>GEBCO</td><td>GEBCO Bathymetric Compilation Group (2022)</td></tr><tr><td>Coastlines</td><td>GEBCO</td><td>ECCO v4</td><td>GEBCO</td><td>GEBCO Bathymetric Compilation Group (2022)</td></tr><tr><td>Salinity</td><td>Modeled variable (ICON-O)</td><td>Modeled variable (MITgcm)</td><td>WOA</td><td>Boyer et al. (2018)</td></tr><tr><td>Seawater temperature</td><td>Modeled variable (ICON-O)</td><td>Modeled variable (MITgcm)</td><td>WOA</td><td>Boyer et al. (2018)</td></tr><tr><td>Nutrients in water (N, P, Si, Fe)</td><td>Modeled variable (ECOMSO)</td><td>Modeled variable (MITgcm)</td><td>n/a</td><td>Boyer et al. (2018)</td></tr><tr><td>Sea ice extent</td><td>Modeled variable (ICON-O)</td><td>Modeled variable (MITgcm)</td><td>n/a</td><td>Korn et al. (2022)</td></tr><tr><td>Tides</td><td>Modeled variable (ICON-O)</td><td>n/a</td><td>n/a</td><td>Egbert and Erofeeva (2002)</td></tr></tbody></table>
<table><caption>**Table F6. Continued.**</caption><thead><tr><th>Drivers</th><th>MERCY</th><th>MITgcm</th><th>Possibility of harmonizing drivers for simulations</th><th>References</th></tr></thead><tbody><tr><td><em>Ocean</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary production</td><td>Modeled variable (ECOSMO)</td><td>Darwin</td><td>n/a</td><td>Daewel and Schrum (2013); Daewel et al. (2019)</td></tr><tr><td>Biota growth, feeding, and mortality rates</td><td>Modeled variable (ECOSMO)</td><td>Darwin</td><td>n/a</td><td>Daewel and Schrum (2013); Daewel et al. (2019)</td></tr><tr><td>Light attenuation</td><td>Modeled variable (MERCY)</td><td>Darwin</td><td>n/a</td><td>Bieser et al. (2023)</td></tr><tr><td>Particle settling</td><td>Modeled variable (ICON-O)</td><td>Darwin</td><td>n/a</td><td>Schröter et al. (2018)</td></tr><tr><td>Bioaccumulation</td><td>Modeled variable (MERCY)</td><td>Modeled variable</td><td>n/a</td><td>Bieser et al. (2023)</td></tr><tr><td>Sediments and resuspension</td><td>Modeled variable (MERCY)</td><td>n/a</td><td>n/a</td><td>Bieser et al. (2023)</td></tr></tbody></table>

“n/a” stands for not applicable.

<table><caption>**Table F7.** Multimedia mass balance box model drivers.</caption><thead><tr><th colspan="2">Drivers</th></tr></thead><tbody><tr><td colspan="2"><em>External: Hg emissions and releases</em></td></tr><tr><td>Natural</td><td>Geogenic</td></tr><tr><td>Anthropogenic</td><td>Emissions to the air<br>Releases to land and water</td></tr><tr><td colspan="2"><em>Internal: time-invariant rate coefficients of Hg exchange between compartments</em></td></tr><tr><td>Atmosphere</td><td>Hg(II) deposition to the ocean<br>Hg(0) deposition to the ocean<br>Hg(II) deposition to land<br>Hg(0) deposition to land</td></tr><tr><td>Surface ocean</td><td>Hg(0) ocean evasion<br>Particle settling to the subsurface ocean<br>Water transfer to the subsurface ocean</td></tr><tr><td>Subsurface ocean</td><td>Particle settling to the deep ocean<br>Water transfer to the surface ocean<br>Water transfer to the deep ocean</td></tr><tr><td>Deep ocean</td><td>Burial in deep sediments<br>Water transfer to the subsurface ocean</td></tr><tr><td>Fast terrestrial pool</td><td>Evasion due to respiration of organic carbon<br>Photochemical re-emission of deposited Hg<br>Biomass burning<br>Transfer to the slow pool<br>Transfer to the armored pool<br>River runoff to the surface ocean</td></tr><tr><td>Slow soil pool</td><td>Evasion due to respiration of organic carbon<br>Biomass burning<br>Transfer to the fast pool<br>River runoff to the surface ocean</td></tr><tr><td>Armored soil pool</td><td>Evasion due to respiration of organic carbon<br>Biomass burning<br>Transfer to the fast pool<br>River runoff to the surface ocean</td></tr></tbody></table>
<table><caption>Table F8. Atmospheric multimodel ensemble simulations.</caption><thead><tr><th>Simulation</th><th>Purpose</th><th>Description</th><th>Driver option</th><th>Time range</th><th>Priority<sup>a</sup></th></tr></thead><tbody><tr><td colspan="6"><strong>Baseline</strong></td></tr><tr><td>A1</td><td>Spatial and temporal trends of Hg levels (baseline)</td><td>Global-scale simulations of Hg atmospheric dispersion using the state-of-the-art model configuration and unified emission inventories</td><td>Defaults (bold in Table F5)</td><td>2010–2020</td><td>1</td></tr><tr><td colspan="6"><strong>Source contributions<sup>1</sup></strong></td></tr><tr><td>A2.1</td><td>Relative contribution of anthropogenic emissions</td><td>Perturbation simulation with no anthropogenic emissions</td><td>Ant. = 0</td><td>2010–2020</td><td>1</td></tr><tr><td>A2.2</td><td>Relative contribution of geogenic emissions</td><td>Perturbation simulation with no geogenic emissions</td><td>Geo. = 0</td><td>2010–2020</td><td>1</td></tr><tr><td>A2.3</td><td>Relative contribution of land legacy emissions</td><td>Perturbation simulation with no legacy emissions from soil</td><td>L.land = 0</td><td>2010–2020</td><td>1</td></tr><tr><td>A2.4</td><td>Relative contribution of wildfire emissions</td><td>Perturbation simulation with no emissions from wildfires</td><td>BB = 0</td><td>2010–2020</td><td>1</td></tr><tr><td>A2.5</td><td>Relative contributions of ASGM emissions</td><td>Perturbation simulation with no ASGM emissions</td><td>ASGM = 0</td><td>2010–2020</td><td>1</td></tr><tr><td>A2.6</td><td>Relative contributions of MC Annex D sources</td><td>Perturbation simulation with no emissions from MC Annex D sources</td><td>Annex D = 0</td><td>2010–2020</td><td>1</td></tr><tr><td colspan="6"><strong>Trend analysis – identifying drivers of trends</strong></td></tr><tr><td colspan="6"><strong>(a) Contributions of various drivers to trends<sup>2</sup></strong></td></tr><tr><td>A3a.1 (Nature Simulation)</td><td>Contributions of anthropogenic emissions and environmental drivers to changes in Hg levels</td><td>Perturbation simulation with fixed 2010 anthropogenic emissions</td><td>E2010/2020 = 2010<br>All others: varying</td><td>2010–2020</td><td>1</td></tr><tr><td>A3a.2</td><td>Contribution of ASGM to changes in Hg levels</td><td>Perturbation simulation with ASGM emissions fixed at 2010</td><td>ASGM = 2010<br>All others: varying</td><td>2010–2020</td><td>1</td></tr><tr><td>A3a.3</td><td>Contribution of Annex D on changing Hg levels</td><td>Perturbation simulation with MC Annex D emissions fixed at 2010</td><td>Annex D = 2010<br>All others: varying</td><td>2010–2020</td><td>1</td></tr><tr><td>A3a.4</td><td>Contribution of geogenic emissions to changes in Hg levels</td><td>Perturbation simulation with fixed 2010 geogenic emissions</td><td>Geogenic: 2010<br>All others: varying</td><td>2010–2020</td><td>1</td></tr><tr><td colspan="6"><strong>(b) Effects of changes in various drivers to trends</strong></td></tr><tr><td>A3b.1</td><td>Effect of transport on changes in Hg levels (inert tracer experiment)</td><td>Baseline simulation with an inert tracer emitted with the same spatial distribution as Hg, but all chemistry and deposition turned off (exponential decay imposed with 6-month lifetime)</td><td>Meteorology = varying<br>All emission fields fixed with 2010 spatial distribution (multimodel mean from A1)</td><td>2010–2020</td><td>2</td></tr><tr><td>A3b.2</td><td>Effect of wildfire emissions to changes in Hg levels</td><td>Perturbation simulation with varying wildfire emissions and all other drivers fixed</td><td>Wildfire emissions = varying<br>All others: 2010</td><td>2010–2020</td><td>2</td></tr><tr><td>A3b.3</td><td>Effect of terrestrial legacy emissions on changes in Hg levels</td><td>Perturbation simulation with varying terrestrial legacy emissions and all other drivers fixed</td><td>Land re-emissions = varying<br>All others: 2010</td><td>2010–2020</td><td>2</td></tr><tr><td>A3b.4</td><td>Effect of ocean concentrations on changes in Hg levels</td><td>Perturbation simulation with varying oceanic concentrations and all other drivers fixed</td><td>Ocean re-emission = varying<br>All others: 2010</td><td>2010–2020</td><td>2</td></tr><tr><td>A3b.5</td><td>Effect of land surface conditions to changes in Hg levels</td><td>Perturbation simulation with varying land surface conditions (land cover, LAI) and all other drivers fixed</td><td>LAI/land cover = varying<br>All others: 2010</td><td>2010–2020</td><td>2</td></tr><tr><td>A3b.6</td><td>Effect of chemical reactants on changes in Hg levels</td><td>Perturbation simulation with varying air concentrations of major chemical reactants (Br, OH, Cl, O<sub>3</sub>,...) and PM<sub>2.5</sub> and all other drivers fixed</td><td>Reactants = varying<br>All others: 2010</td><td>2010–2020</td><td>2</td></tr></tbody></table>