UROP Openings

Have a UROP opening you would like to submit?

Please fill out the form.

Submit your UROP opening

Measuring indoor air quality in an existing building retrofit for energy efficiency and electrification




11: Urban Studies and Planning

Faculty Supervisor:

David Hsu

Faculty email:


Apply by:



David Hsu, email: ydh@mit.edu

Project Description

This is a continuing project to measure changes in air quality in a residential building that is being retrofitted for full electrification and passive house standards. The importance of this project stems from the fact in many cities, buildings are the largest portion of energy use and greenhouse gas emissions. Many of the proposed pathways for reducing carbon emissions in buildings require that buildings do two things: first, buildings must become much more energy efficient, mostly by tightening and insulating building envelopes, and second, buildings need to switch their use of fuels to electricity, an energy-carrying medium that can be most easily decarbonized in the future. Significant changes to the envelope and energy systems, however, may also change the exposure of occupants to both indoor and outdoor air pollution, a major public health concern. Working with an affordable housing community partnership in New York City, we have installed air quality and gas sensors in a low-income, four-story residential building before, during, and after a retrofit to full electrification and passive house standards. We expect to see changes in indoor air pollutants, un-combusted fuels and combustion products, based on changes to the building systems and envelope. Work will consist of: 1. analyzing and visualizing indoor air quality data from the installed sensors. 2. developing a health impact methodology, to be deployed by social workers that we are collaborating with. You will work with a professor, graduate student, and possibly other undergraduates.


Ability to learn, code, and apply new statistical and data analysis technique. Experience with statistical analysis, data analysis, and data cleaning. Must be able to use Python and R.