When working with geospatial data on Google Earth (for example, from our AEREarth tool), you might need the elevations for some coordinates. If it’s only a handful, they’re easily found with Google Earth’s interface. But let’s say you want elevations for dozens, hundreds, or even thousands of locations.
We recently wrote a post about a handy Excel workbook you can use to query elevations for a set of coordinates in the Google Maps Application Programming Interface (API). Here, we’ll go over how to do the same thing using Python.
One Air Sciences’ team member’s graduate research at Portland State University (Oregon) clocked a lot of time with a tabletop ultraviolet (UV)-visible spectrometer. This equipment measures how much a chemical substance absorbs light. You see, Matt had painstakingly prepared hundreds of passive air pollution monitoring devices to conduct high-density measurements of nitrogen dioxide (NO2) in east Portland. To “extract” the adsorbed NO2 from the devices, an aqueous solution was prepared with spectral properties that changed with the amount of NO2 present. Perfect, tedious work for a grad student, but it ultimately produced some gratifying results.
In our last post on this topic we left off asking the question, “given how much wildland fires change year to year, how do we build an emissions inventory (EI) that is representative of a multi-year period, or a future period?” This is a confounding problem not only for the Regional Haze planning process but for any air quality planning exercise that a regulatory agency engages with.