ACCOUNTING FOR GEOSPATIAL UNCERTAINTIES IN AN ENERGY - AIR QUALITY DECISION SUPPORT TOOL
This paper presents a methodology to account for spatial uncertainties associated to geospatially allocated emissions of primary air pollutants in Luxembourg. We apply it to an integrated energy-air quality decision support tool for the urban and regional scale. Aggregated emissions of air pollutants are computed by an energy model which minimises the costs of the reference energy system. The model describes the five sectors agriculture, transport, industry, residential and commercial. The sectoral emissions are spatially allocated to obtain hourly to daily emission maps. The air quality model simulates the dispersion of the emitted pollutants and their chemical reactions to produce ozone for typical episodes for each five-year period. Both models are coupled by an optimisation method to find the optimal energy system with a constraint on ozone concentrations.
Dis-aggregation from national to urban scale requires accounting for uncertainties and their storage in data bases for further geospatial modelling or decision making. The spatial uncertainty can be modelled by spatial stochastic simulation assuming a semi-variogram with local and regional scale variability. A Monte Carlo approach is used to simulate a set of spatially correlated uncertain realisations. Finally, the average dis-aggregated emission value and its corresponding ``local'' uncertainty can be computed for each grid cell and stored in the geospatial data base with the OGC standard uncertML. UncertML ensures exchange of geospatial uncertainties throughout the whole modelling tool and supports decision making under uncertainty.
Ulrich Leopold - Public Research Centre Henri Tudor
Christian Braun - Public Research Centre Henri Tudor
Laurent Drouet - Public Research Centre Henri Tudor
Daniel S. Zachary - Public Research Centre Henri Tudor