How does ADASHI handle NOAA's ALOHA aerial dispersion model?

Q: What is the difference between ADASHI and other programs in their use of NOAA’s ALOHA aerial dispersion model?


A: ADASHI is the only company to completely automate NOAA’s ALOHA model’s operation. Rather than linking to the ALOHA program and making an operating system call to run the program separately, ADASHI completely encapsulates the ALOHA algorithm and databases, thus automating data entry and significantly improving the stability of the ALOHA code (which, when run without ADASHI, can be unstable and crash).


When it comes to generation of ALOHA aerial dispersion models, the main difference between other programs and ADASHI is that in most other programs, you need to open yet another separate program to process an evaluation of the scene, providing inputs and performing a time-consuming series of data inquires whereas in ADASHI, the calculation is fully automatic, generating the aerial dispersion model instantly.


When a hazmat incident is initiated, ADASHI’s algorithm generates a worst case scenario dispersion model based on source characterization supported by the US Army’s Edgewood Chemical and Biological Center (ECBC), terrain features of the map’s location and real-time weather (of which you can set default/prioritize/select different input, for example if you have a mobile MET station you want to use). In addition, any information you have entered in the natural course of program use such as identifying a specific chemical agent or the release method are automatically updated and reflected in the aerial dispersion model. For example, when choosing a rail car type or gas canister size, they are identifiable using ADASHI’s push button graphical entry methods.


Using ADASHI’s intuitive hazmat ID tools, you can incorporate new information such as the size of the hole in the leaking tanker, or in the case of a terrorist threat, identify the size of the bomb – car, suitcase, truck etc. Once you do so, ADASHI recalculates the aerial dispersion model instantaneously adjusting from worst-case scenario to reflect the new information.


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