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Department of Geological and Mining Engineering and Sciences; Department of Civil, Environmental, and Geospatial Engineering


Lagrangian particle dispersion models (LPDMs) are powerful and popular tools used for the analysis of atmospheric trace gas measurements. However, it can be difficult to determine the transport pathway of emissions from their source to a receptor using the standard gridded model output, particularly during complex meteorological scenarios. In this paper we present a method to clearly and easily identify the pathway taken by only those emissions that arrive at a receptor at a particular time, by combining the standard gridded output from forward (e.g., concentration) and backward (e.g., residence time) LPDM simulations. By comparing the pathway determined from this method with particle trajectories from both the forward and backward models, we show that this method successfully restores much of the Lagrangian information that is lost when the data are gridded. A sample analysis is presented, demonstrating that the sourceto-receptor pathway determined from this method is more accurate and easier to use than existing methods using standard LPDM products (gridded fields of, e.g., concentrations and residence time). As demonstrated in an evaluation and an example application, the method requires agreement between the transport described by the forward and backward simulations and thus provides a means to assess the quality and reversibility of the simulation. Finally, we discuss the potential for combining the backward LPDM simulation with gridded data from other sources (e.g., chemical transport models) to obtain a Lagrangian sampling of the air that will eventually arrive at a receptor. Based on the advantages presented here, this new method can complement or even replace many of the standard uses of backward LPDM simulations.

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© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License. Publisher’s version of record:

Publication Title

Atmospheric Chemistry and Physics

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.


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