An Approach to Temporally Disaggregate Benzo(a)pyrene Emissions and Their Application to a 3D Eulerian Atmospheric Chemistry Transport Model

Abstract

To simulate the atmospheric fate of air pollutants, it is first necessary to know the emission rates that describe the release of pollutants into ambient air. For benzo(a)pyrene emission data are currently only available as yearly bulk emissions while the simulation models typically require temporally resolved emissions (e.g. hourly). Because residential heating is by far the most important source for benzo(a)pyrene, we developed a method to temporally disaggregate these bulk emission data using the linear dependency of benzo(a)pyrene emission rates stemming from residential combustion on ambient temperature. The resulting time-dependent hourly emission rates have been used in a chemical transport model to simulate concentrations and deposition fluxes of benzo(a)pyrene in the year 2000. The same simulations were repeated with constant emission rates and emission rates that varied only seasonally. By comparing the modeling results of the three emission cases with monthly measurements of air concentrations, the characteristic and the benefit of our disaggregation approach is illustrated. The simulations with disaggregated emissions fitted best to the measurements. At the same time the spatial distribution as well as the yearly total deposition was notably different with each emission case even though the yearly total emissions were kept constant.