Use of real-time remote sensing data along with a mesoscale model to forecast air pollution due to wildland fires

In this study a mesoscale model coupled with near real-time remote sensing data, has been applied to forecast air pollution due to wildland fires. Operational Multiscale Environment model with Grid Adaptivity (OMEGA) developed by SAIC (Science Applications International Corporation) is used in our current study. Satellite images have been used along with the NFDRS (National Fire Danger Rating) fuel load data to estimate the current fuel load available for burning. Emission from the fire has been calculated by estimating the area burned by the fire using real-time satellite data, and using emission factors given by EPA (Environmental Protection Agency). We have concentrated our efforts on estimating the emission of PM2.5 and Carbon Monoxide due to wildland fires. A forest fire in the Eastern United States has been taken as a case study and the accuracy and efficiency of the model to run on real time basis has been shown. The whole processing is done using a sixteen node parallel cluster, so as to speed up the processing time for the model. A framework has been proposed to use mesoscale model along with real-time remote sending data to automatically detect fire pixels, run the model and generate the output in GIS (Geographic Information Systems) format to be distributed on the web. This will facilitate rapid distribution of forecast result which will be of immense help to persons involved in disaster management of wildland fires.