Development and application of an updated geospatial distribution model for gridding 2015 global mercury emissions

Abstract

Mercury is a global pollutant that poses threats to ecosystems and to human health. Due to its global transport, mercury contamination is found in regions of the Earth that are remote from major emissions areas, including the Polar regions. Global anthropogenic emission inventories identify important sectors and industries responsible for emissions at a national level; however, to be useful for air transport modelling, more precise information on the locations of emission is required. This paper describes the methodology applied, and the results of work that was conducted to geospatially distribute anthropogenic mercury emissions as part of the global anthropogenic mercury emissions inventory for 2015 prepared by AMAP/UNEP for the Global Mercury Assessment (GMA) 2018 (UN Environment, 2019). This work includes the identification and use of emission point sources as well as distributing diffuse emissions for 21 emission (industry) sectors. The basic approach involves assigning emission estimates to geo-located point sources, using reported emissions information where available, and otherwise assigning a modelled emission to the point. Emissions which cannot be assigned to point sources are distributed using sector-specific proxies. Mercury speciation highly depends on industry processes and air pollution control technology. Different Hg speciation ratios are therefore applied per sector and country technology level. The resulting global emission datasets include total mercury (HgT), gaseous elemental mercury (Hg0), divalent mercury (Hg2+) and particulate mercury (HgP) in kg per year per raster cell (kg/a). The spatial resolution is flexible and the vertical resolution of the data is based on a set of predefined height classes. The resolution of the GMA2018 emission data is 0.25° × 0.25° with three (physical emission) height classes (0–50, 50–150 and > 150m). A comparison with the EMEP European mercury emission was made based on spatial correlation between the two datasets. Suggested improvements for future work include the further development of proxy data and the implementation of a more structured reporting of emissions by countries.