The usefulness of ground based air quality monitoring data for diagnostics of uncertainties in gridded emission
inventories is examined. A general probabilistic procedure for comparison of levels of uncertainties in different emission
datasets is developed. It implies the evaluation of the agreement between modeling results obtained with these emission
datasets and corresponding measurements. This procedure is applied to the evaluation of different datasets for European
gridded nitrogen oxide (NOx) emissions by using the AirBase monitoring data and the CHIMERE chemistry-transport
model. Numerical experiments are performed for two different types of spatial distributions of emission uncertainties and
five different types of monitors. The results are also generalized for various levels of uncertainties in simulated and measured
data. It is found, in particular, that most informative, from the point of view of diagnostics of NOx emission uncertainties,
are the measurements of NO2 at rural background sites and measurements of ozone at suburban sites situated in
the vicinity of intensive sources of emissions. A more precise conclusion regarding the relative accuracy of two emission
datasets can be drawn with a larger number of monitors in a network and a higher accuracy of the model and measurements.
For example, with a network of 50 rural background NO2 monitors, the probability of choosing the more certain
emission data set is more than 90 percent, if differences in uncertainty of two sets are more than 50 percent. Practical recommendations
for designing or evolving surface measurement networks, in light of the study results, are given.