Istituto di Scienza e Tecnologie dell'Informazione     
Cesari R., Paradisi P., Allegrini P. Source identification by a statistical analysis of backward trajectories based on peak pollution events. In: International Journal of Environment and Pollution, vol. 55 (1-4) pp. 94 - 103. Inderscience Publishers, 2014.
Back-trajectory techniques are extensively used to identify the most probable source locations, starting from the known pollutants concentration data at some receptor sites. In this paper, we review the trajectory statistical methods (TSMs) that are most used in literature for source identification, which are essentially based on the concept of residence time (RT), and we introduce a novel statistical method. To validate this method, artificial receptor data at two receptor sites are derived from numerical simulations with a given aerial source, using the Lagrangian dispersion model (LSM) FLEXPART in forward mode. Then the RTs are computed using again the model FLEXPART, but in backward mode. Then, the new statistical methodology, which is based on the use of peak concentration events, is applied to reconstruct the spatial distribution of emission sources. Our approach requires simulation times shorter than those required in other methods and could overcome the problem of ghost sources.
URL: http://www.inderscience.com/info/inarticle.php?artid=65909
DOI: 10.1504/IJEP.2014.065909
Subject Trajectory-based statistical methods
Backward trajectories
Lagrangian dispersion models
Environmental pollution
Source identification
I.6.4 SIMULATION AND MODELING. Model Validation and Analysis
86A10 Meteorology and atmospheric physics

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