Inderscience Publishers

Comparative investigation of Source Term Estimation algorithms using FUSION field trial 2007 data: linear regression analysis

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Given a warning based on detections at a few sensors, it should be useful to rapidly provide an estimate of the location, time of release and amount of material released. Such information could lead to refined predictions of the hazardous area and support follow–on actions to investigate the cause and nature of the hazardous release. In September 2007, a short–range test designed to collect data to support development of prototype Source Term Estimation (STE) algorithms was conducted. A total of eight different STE algorithm developers participated in this exercise and submitted fourteen sets of full and partial predictions. Linear regression analysis considered several variables that might influence results including the number of sensors, the release type, the time of the release, meteorological inputs and the number of sources. The results of these analyses are used to ascertain trends among different sets of STE predictions and are presented here.

Keywords: STE, source term estimation, sensor data fusion, pollutant dispersion, backtracking, air pollution, hazardous release, dispersion modelling

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