Infrastructure and transportation are having a moment in media coverage and political efforts. These umbrella terms mostly bring ports, bridges, and roads to mind, but they also include something in Air Sciences’ wheelhouse: MOVES, the MOtor Vehicle Emission Simulator, developed and funded by the United States Environmental Protection Agency (EPA). Now in its third iteration, MOVES provides a snapshot of tailpipe emissions for on- and off-road motor vehicles. The model encompasses six common air pollutants (ground-level ozone, particulate matter, carbon monoxide, lead, sulfur dioxide, and carbon dioxide, known collectively as criteria air pollutants), greenhouse gases, and hazardous air pollutants.
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.