On artificial dilution of point source mercury emissions in a regional atmospheric model

Previously, we developed and applied a regional atmospheric mercury model to a domain covering most of North America at a horizontal grid resolution of 100 km. The implication of using this coarse resolution is that point sources of mercury emissions are instantaneously spread over a grid volume of horizontal dimensions 100 x 100 km2 and a vertical dimension equal to the depth of the grid cell where the point source emissions are released. Since point sources comprise a significant majority of a regional mercury emissions inventory, it is important to understand what effect this artificial dilution may have on calculated mercury concentrations and deposition fluxes. To understand this effect, we conducted model simulations using a finer grid, embedded within the original coarse grid, over a sub-domain that includes over 50% of the largest mercury point sources in the north-eastern United States. The horizontal resolution of the fine grid is 20 km, i.e. it is five times smaller than that of the coarse grid. We compared short-term (daily) and long-term (annual) averaged mercury concentrations, and deposition (wet and dry) fluxes on the coarse and fine grids. As expected, the effect of grid resolution is more clearly seen in close proximity to point sources than at remote locations. For short-term averages near major point sources, the peak concentrations and dry deposition fluxes of mercury from the fine grid are almost a factor of two greater than the corresponding estimates from the coarse grid. At remote locations, however, the concentrations and dry deposition peaks estimated by the two model grid resolutions are more comparable. For total wet deposition of mercury, the distinction between the fine and the coarse grid model results is less significant, regardless of the location. This could be due to the redistribution of precipitation fields or the effect of mercury aqueous chemistry. The effect of grid resolution is more important when model estimates are averaged over short time periods, e.g. daily, as opposed to over long periods, e.g. seasonally and annually.