The quantification of NO_<i>x</i> and SO₂ point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study

Abstract

Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50 m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO2 flux (for NOx this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4 m s−1. (3) The remaining errors by [NOx] ∕ [NO2] ratio correction can be significant when measuring very close. To minimize the [NOx] ∕ [NO2] ratio correction error, we recommend minimum distances from the source, at which 5 % of the NO2 maximum reaction rate is reached and thus NOx steady state can be assumed. (4) Our study suggests that emission rates < 30 g s−1 for NOx and < 50 g s−1 for SO2 are not recommended for mobile DOAS measurements. Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.