Global Health Map: Coupling EMAC and KM-SUB-ELF to estimate air pollution health effects using accurate iron soluble fractions

Large-scale atmospheric chemistry-climate models such as the ECHAM/MESSy Atmospheric Chemistry model (EMAC) are capable of accurately describing the composition and distribution of air pollutants on a global scale. On the other hand, small-scale multiphase models are developed to investigate health-related effects of air pollutants. The kinetic multi-layer model for surface and bulk chemistry in the epithelial lining fluid (KM-SUB-ELF) simulates chemical reactions and mass transport in the human lung, allowing for accurate estimations of the production and persistence of reactive oxygen species (ROS), hydroxyl radicals (OH) and damage to biomolecules. In recent publications, KM-SUB-ELF has been extended to consider endogenous production and transport of ROS through membranes in the lung, opening the avenue of mechanistically investigating the effects of air pollution on various diseases that have been linked to particulate matter exposure in epidemiological studies.

In this work, we present a multi-scale modelling approach to link large-scale atmospheric chemistry-climate models with small-scale multiphase kinetic models to derive a global health map. We use the chemistry-climate model EMAC to derive air pollutant distributions with various time resolutions. As studies suggested that metals capable of redox cycling (especially iron and copper) play a dominant role in the exogenous production of ROS and thus particulate matter toxicity, we focus on an accurate distribution of compounds containing iron.

In this work, we bring together the latest scientific developments in both global climate and multiphase chemical kinetics modelling, enabling a state-of-the-art evaluation of the global health burden of air pollution. Our multi-scale modelling approach yields air pollutant health effect simulations with accurate resolution in both space and time, contributing to the unravelling of the complex association of air pollutant emission profiles with epidemiological observations.  The non-linearity of KM-SUB-ELF permits an evaluation of averaging effects over space and time, a common practice in the association of air pollutant profiles with epidemiological observations.