Air quality is a major crisis globally, leading to about 5 million premature deaths every year. In sub-Saharan Africa, there is little air pollution data available to characterize the problem, and a lack of focus on solutions. Using output from a high spatiotemporal resolution atmospheric chemistry transport model over Africa simulated by Dr. Westervelt and his group, the student will characterize levels of pollution and validate model results by comparing observed data to model output. The student will also analyze results from sensitivity simulations in which sources of air pollution have been artificially “turned off” in the model. Comparison between the two simulations will allow for source attribution of air pollution, which is important for developing satisfactory mitigation strategies to improve air quality.

The student will be working with large amounts (~1 TB) of spatial air quality data, doing simple descriptive statistics, data manipulation, and data visualization. Python or R are the preferred tools.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Daniel M. Westervelt
  • Department/School: Lamont-Doherty Earth Observatory
  • Location: Oceanography 306B (Lamont campus)
  • We work on characterizing aerosol and gas-phase air pollution and their impacts on climate using models, observations, and satellite data. With a focus in the Global South.

Project Timeline

  • Earliest starting date: 3/1/2021
  • End date: 8/31/2021
  • Number of hours per week of research expected during Spring 2021: ~8
  • Number of hours per week of research expected during Summer 2021: ~up to 20

Candidate requirements

  • Skill sets: Fluency with data manipulation and visualization tools such as NumPy, SciPy, and matplotlib. Use of R or other data visualization language is also acceptable.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes