Parametrization of temporal emission variability for greenhouse gases and air pollutants

Abstract

The basis for modelling greenhouse gases and air pollutants is an explicit spatially and temporally resolved specification of the anthropogenic emissions. Although a lot effort has been put in to improve the spatial allocation for emission inventories, the temporal variability for many sectors is not aligned with real-world conditions and often prescribed using static or constant time profiles. We have been developing a modelling system to predict the spatial-temporal behaviour of anthropogenic emissions of air pollutants and greenhouse gases in which we aim to also include the influence of e.g. meteorological conditions on activities and emission factors. As such, we are replacing the static emission profiles with parametrizations one by one. Here, we present the approaches we are taking for 3 sectors. First, for large power plants the point source metadata of the CAMS-REG inventory have been matched to those from the ENTSO-E database to link the hourly production statistics to the emission inventory. Next, we tried to group the production time series of the different kinds of power plants, but this turned out not to be possible as they show very specific profiles. It was affordable to include for each power plant its individual emission time profile into the emission model. Second, for small stationary combustion the temporal variability is calculated for two building types, i.e. office and residential buildings, using the heating degree day method for oil and gas. For residential wood and coal combustion additional constraints with respect to heating behaviour have been applied. Third, for methane emissions from landfills we based our parametrization on investigations showing a significant pressure dependency of the emission flux. The original (constant) emission profile is exchanged with a profile which is spatially and temporally dependent on the change of atmospheric pressure, which leads to a variability of a factor 4 around the annual mean flux. Given the impact of synoptic variability the behaviour shows comparable patterns across large regions. At the meeting we will also show our planned approach to detail the emission variability of methane and PM from the agricultural sector (enteric fermentation, livestock housing and land management).