A common approach to estimate the spatial–temporal distribution of atmospheric species properties is data assimilation. This comprises methods to combine information from different sources for obtaining the best estimate of a system state. Data assimilation is based on minimizing the error in the estimate (optimal interpolation and Kalman filtering methods) or on minimizing the cost function (variational methods). Under certain conditions, variational methods turn out to be equivalent to optimal interpolation or Kalman filtering. All data assimilation techniques require an understanding of data error statistics. Optimal interpolation is a relatively simple and computationally cheap non-sequential method. In the optimal interpolation method, error correlations can be modeled with analytical functions on the base of the gathered data. In the present work, we investigate the effect of the form of the error correlation functions on the uncertainty in the estimate when using the spatial–temporal optimal interpolation (STOI) technique. We apply STOI to the estimation of aerosol distribution over Europe. To perform STOI, we use results of the chemical transport model GEOS-Chem simulations, as well as observations from a ground-based radiometric network AERONET that provides data on aerosol properties with low uncertainty. We show that the results of the STOI estimation are very tentative to the form of the error correlation functions. We perform some tuning of the correlation function parameters to improve the accuracy of the estimation.