An integrated atmospheric dispersion and exposure assessment model

General Information

EcoSense is a Europe-wide integrated atmospheric dispersion and exposure assessment model which calculates external costs related to the exposure to airborne pollutants with a focus on impacts on human health, following the Impact Pathway approach. To allow for a fast computation time, EcoSense uses so-called source-receptor matrices instead of a full atmospheric dispersion model. Source-receptor matrices link the change in emissions in one country to a change in concentration in a specific area/grid cell. EcoSense considers impacts of classical air pollutants, such as SO2, NOx, Particulate Matter, NMVOCs and NH3.

Using impact functions, which combine concentration-response relationships with the background rate of different diseases and the affected population, it is possible to identify and assess the health impacts due to changes in pollutant concentrations. The different impacts can then be translated into a common metric (disability adjusted life years, DALY), which includes changes in both quality of life and life expectancy, and monetized accordingly. Currently, EcoSense assesses impacts due to long-term exposure to particulate matter (PM), nitrogen dioxide (NO2), and ozone (SOMO35).

Application and further development

The main area of application is to support the assessment of different air pollution mitigation strategies by providing information about their effectiveness and cost efficiency. The (avoided) external costs can be used, for example, in cost-benefit analyses to identify cost-efficient measures.

Furthermore, EcoSense has been applied for the calculation of different cost rates in the Methodological Convention for the Assessment of Enviromental Costs of the German Federal Environmental Agency.

Currently, the underlying methodology is being further developed to distinguish the exposure of different socio-economic groups. The aim is to simulate the (potential) lifetime exposure of different, representative groups in order to better consider the long-term effects of different measures in their assessment.

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