**Conference**: Standards and Quality**Presentation**: Spatial uncertainty contributions for emissions monitoring based on EN 15259**Presentation Time**: Day2 - 11:25

Available soon

Obtaining a representative sample of flue gas is critically important for both the Automated Measuring System (AMS) and the Standard Reference Method (SRM) that is used to calibrate the AMS. The spatial deviation in pollutant concentration across a measurement plane must be taken into account when assessing the uncertainty of a measurement. Deviation at the AMS sampling location is corrected by the QAL2 calibration established under EN 14181, provided that the SRM sampling location is fully representative. Deviation at the SRM sampling location depends on the SRM employed. If the SRM specifies a grid sampling approach, as is the case for most wet chemical methods and gravimetric dust determinations, it can be assumed that this spatial variation is accounted for. However, when the SRM sample is extracted from a single point, the uncertainty related to the SRM location must then be accounted for.

EN 15259 defines procedures for establishing sample representativeness and also specifies how the residual positional uncertainty can be evaluated. However, the statistical tests within EN 15259 assume that the spatial concentration variation across a sampling grid is large when compared with the temporal variation at a fixed reference point. This is not always the case and it is not then clear how the positional uncertainty can be evaluated. Surrogates are also often employed when evaluating sampling planes, e.g., the O2 or CO2 variation is assumed to describe the behaviour of other pollutants, such as SO2 and CO, but this is not always appropriate. This presentation explores alternative approaches for evaluating positional uncertainty, using real world examples, and gives typical values of positional uncertainty for different power generation pollutants and processes.

This contribution is submitted on behalf of the VGB Technical Group: Emissions Monitoring.