Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment

The combined use of air quality monitoring data and state-of-the art dispersion models provides a more realistic representation of the spatial distribution of pollutants and allows a reduction in the uncertainties involved in the assessment of the exposure in epidemiological studies. Data assimilation is a method which combines such information to produce an optimal representation of the state of the atmosphere. In this work, we tested two approaches to merge these information sets: the successive corrections method (SCM) and the statistical optimal interpolation (OI). These methods have been extended in order to take into account the spatial representativeness of measurements. PM 10 , NO 2 , and O 3 concentration fields produced by an air quality modeling system, run with two nested domains covering much of Central Italy and the Rome urban area, have been used to identify the optimal values for the horizontal and vertical scaling distances that are key parameters for the SCM and OI methods. A statistical analysis of the results obtained from the application of these methods demonstrated that lower RMSE values resulted from the use of the OI method. Further, PM 2.5 modeling results over the Rome urban area and additional measurements collected during experimental campaigns, performed within the population exposure to polycyclic aromatic hydrocarbons (EXPAH) LIFE+ Project, allowed the evaluation of this approach in reconstructing PM 2.5 levels at EXPAH monitoring sites, which were not used in the data assimilation process. The results confirmed the potential of these methods to improve the estimation of modeled concentrations, by taking into account local phenomena not resolved by the model, but clear from the observations, and also in providing more reliable data to be used in exposure studies.