Optimization of Plume Model Calculations and Measurement Network with a Kalman Filter Approach

In many industrial regions there is a strong demand for accurate monitoring of the air pollution and its sources. The Rijnmond area around Rotterdam in the Netherlands is an example of an industrial area affected by air pollution through many industrial and traffic activities as well as shipping emissions. In the area the air quality is traditionally modelled based on a Gaussian plume model using local emissions. To estimate the background concentration due to transport from non-local sources, the average difference between the model calculations and observations at three stations is taken. However, in case of local high emission events, this difference cannot be pointed to the background and the simple approach leads to false estimates of the background resulting in over or under estimation of the concentrations in the rest of the area. In this study we have developed a modeling system with a Kalman filter approach to optimize plume model concentrations using actual observations. This system is capable to adapt modelled concentrations based on the originating source of the concentrations, more accurately than using simple background estimates. We will present the system set-up and results for a testcase in the Rijnmond area for NOx. For this testcase we have predefined the 'normal' concentrations for different meteorological situations with a Gaussian plume model. Those model calculations are put in a Kalman filter system and assimilated with actual observations. In case of a measured difference of concentration compared to the model, the system will adapt the most likely sources and in addition provide an uncertainty range of the calculation. The results show the system is much better able to represent the NOx concentrations than previous system. Finally we will show how the system can be used to optimise the monitoring network through minimization of the uncertainty of the model results.