Project Description:

Poor air quality, including particulate matter, is the most deadly and costly environmental problem in the world. Outdoor air pollution could cause 6 to 9 million premature deaths a year by 2060 and cost 1% of global GDP, around 2.6 trillion USD annually, as a result of sick days, medical bills and reduced agricultural output (OECD, 2022). In order to mediate and control air pollution we must first understand the sources of pollutants and the chemistry that transforms gaseous pollutants (NOx, SOx) into particulate matter. One approach to gain understanding and to predict pollution episodes is to use sophisticated computer models to predict pollutant concentrations and compare them to observations. Such validation gives scientist confidence that the computer models are valid enables them to be use for predictive and remediation strategies. The Michalski research group is incorporating stable isotopes into these models as a new way of validation. His group is seeking and Computer Science Major to incorporate isotopes into MUSICA, the National Center for Atmospheric Research (NCAR) new Earth System Model. Students would formulate and test new chemical isotopic mechanisms using MUSICBOX, the chemical scheme used within MUSICA. This requires the development and use of PYTHON coding and command line scripting. The data would then be visualized and analyzed using R. Once completed MUSICA will be installed and tested on the super-computer clusters maintained by Purdue’s Rosen Center for Advanced Computing (RCAC). For this, LINUX scripting is very useful. Students would then incorporate isotopes into the surface emission model withing MUSCIA and add the isotope enabled MUSICBOX. Students would work with graduate students to add meteorology data and conducted coupled simulations of air pollution in the United States and Mexico and analyze output (NetCDF) and compare to observations.

Requirements:

Computer Programing: Python, Data Science R programing, command line scripting/shell script, LINUX, NetCDF. General Chemistry

Bonus for FORTRAN and cluster computing

Project Description:

Seismic waves travel through Earth's layers and contain information about the underground physical properties. Seismic waves are constantly induced by road traffic such as cars and trucks. By measuring the vibration amplitudes emitted from the motor vehicles, we estimate local road roughness and seismic attenuation. In this project, the student is expected to use seismic vibrations recorded by the road-side communication fibers to extract road roughness and subsurface attenuation information. The student will be supervised by a graduate student and the professor to perform signal processing and statistical analysis.

Requirements:

Students with a strong math, physics, geophysics, and programing background are highly encouraged to apply.