EISSA Output

EISSA can be deployed for large and small regions, operates at the level of detail and resolution determined by the user and it allows the user to decide on the sources and substances to be included in the system. EISSA can save data for different moments in time, hence it can also calculate and visualize trends in emissions.

EISSA can be deployed for large and small regions and operates at the level of detail and resolution determined by the user. For example, EISSA can be applied for an industrial or rural complex at a resolution of 10s of meters, for the entire territory of Flanders (1.350.000 hectares) for at a level of detail of one hectare (1ha) or for an extensive air quality hotspot region such as the area ‘southern Poland, the Czech republic and Slovakia’ (about 32.000.000 hectares) for which a spatial resolution in agreement with air quality models should be chosen, e.g. 2500 ha. Essentially, the area and the resolution of an EISSA application can be chosen according to the quality of the available data, the specific requirements and, of course, the processing speed expected during use of the system.

EISSA allows the user to decide on the sources and substances to be included in the system. For example, the EISSA application developed for Persistent Organic Pollutants in Flanders comprises all reported point sources and about 70 diffuse sources for POP’s for the years 1990, 1995, 2000, 2005 and 2010. A total of 28 substances are covered. The EISSA application which is currently being developed for Eastern Europe will only deal with the residential sector, however, several subsectors corresponding to combinations of fuel and heating installation types will be defined.

For all sources and substances comprised in the system, user-customized output can be generated. The following two figures illustrate the user interface of the EISSA system with its analytical capabilities. The first figure presents the different EISSA inputs, the second reveals typical emission output and analysis thereof.

  • Input data comprise the user-customized list of pollutants (top-left, ‘substances’ tab), the user-customized list of sources (top-right, ‘sources’ tab, tree structure of sectors, subsectors and sources), (expert) definitions of emission explaining variables (bottom-left, ‘emission explanatory variables’) and visualization thereof (bottom-right)

Overview of inputs in the EISSA user interface: list of pollutants (top-left, ‘substances’ tab), the list of sources (top-right, ‘sources’ tab), definitions of emission explaining variables (bottom-left, ‘emission explanatory variables’ tab) and visualization thereof (bottom-right).

  • Output and analysis results are shown in the form of tables, graphs and maps. Apart from a total map across all sectors, the spatial distribution for each individual sector can be viewed. The user can zoom in on a specific municipality or province. Administrative boundaries can be overlaid on the maps to facilitate orientation.

Overview of the output and analysis results in the EISSA user interface: computed emission map at initial raster resolution for chosen pollutant (top), aggregated emission map at city level (bottom-left) and analysis of the emission data in table and graphs format (bottom-right).

EISSA can save data for different moments in time, hence it can also calculate and visualize trends in emissions.

The option to study the evolution of emissions in time, in combination with the flexibility to select and customize input data (e.g. emission factors and/or activity data) and algorithms defining the emission explaining variables (EEV’s), makes EISSA highly optimized to quantify the effectiveness of measures. To this effect, it supports scenario analysis and "what if" exercises. For example, the question “What impact does the implementation of a Low Emission Zone (LEZ) have on the emissions within the LEZ?” can be answered by comparing a reference run (no LEZ) with a scenario run (LEZ implemented) in which the input data are adapted in agreement with the LEZ regulations, i.e. a lowering of averaged emission factors due to the shift towards cleaner cars. Algorithms will remain unchanged as the same type of data sources (car fleet and road network) still can be used. The question “What impact does banning of wood burning have on the residential emissions in Flanders?” can also be answered by EISSA by comparing a reference and a scenario run. As compared to the reference, most probably both, the input data and the algorithms, will have to be adapted in agreement with the banning measure. Indeed, the wood ban most probably will cause a drastic shift in the heating installation landscape, consequently data sources used for the reference run will no longer be suitable. This in turn will lead to the need for other algorithms.