The resulting dataset contains 23 million values. Some of the 23 pollutants simulated reflect the many organic gasses capable of reacting with NO and NO2 tp produce ozone. But we also want to look at all the other chemicals that are having an influence on what the model is doing. These include nitrogen oxide, nitrogen dioxide, peroxyacl nitrate, nitric acid, and the different hydrocarbons that go into the production of ozone. Once we have this information, it helps us understand the reasons ozone is being formed and which air pollutants it would be most effective to reduce.
Lighter colors are higher concentrations.
Testing UAM with X DataSlice Wagner's team tests the model by comparing it with experimental data from a three-day, multimillion dollar air test taken during adverse meteorological conditions in 1987. Using NCSA's X DataSlice software, Wagner's team can dissect the model vertically and horizontally, rotate it, animate it, and use color coding to represent changes in chemical concentration. And it lets us look at inputs as well as outputs. For instance, let's say we color code our emissions, and our model shows an air pollution source out in the desert where we know there isn't one. We can spot this problem much easier with the visualization than we could by looking through pages and pages of numbers.