Understanding Plastic Waste Recycling Patterns in The U.S. For Infrastructure Planning
Work on an NSF-funded project on data science for research that addresses critical sustainability challenges. Experience or basic skills in coding (e.g., Python, R) are preferred.
Project is Not Currently Available
This project is not being offered for the current term. Please check back next semester for updates.
Project description
This project will collect, clean, and format spatial data on plastic recycling and existing recycling infrastructure (e.g., materials recovery facilities and recycling plants) across the U.S. from publicly available sources (for instance, the EPA Recycling Infrastructure and Market Opportunities Map). The student will use the data to conduct different statistical analysis, such as regression modeling, to assess how recycling rates vary with other variables, including infrastructure distribution and capacity, landfill tipping fees, bottle bills, and relevant economic indicators. The intern will summarize the interpretation of the data and other results in a final report and, ideally, in a scientific publication. The findings will inform data-driven planning for increasing plastic recycling rates. We will publish our datasets and analysis scripts in an open-source repository (e.g., GitHub).
Project outcome
In this project, the student will gain experience in employing data science for research that addresses critical sustainability challenges. Specifically, they will deploy skills related to data collection, cleaning, formatting, analysis, and interpretation. They will also gain insights into plastic recycling infrastructure and policy approaches to increase recycling across the U.S. The student will improve their writing and communication skills by developing reports and delivering presentations on their research (in one-on-one meetings with the supervisor and group meetings). They will summarize the interpretation of the data and other results in a final report and, ideally, in a scientific publication. They will also get experience publishing our datasets and analysis scripts in an open-source repository (e.g., GitHub).
Project details
| Length of commitment | less than a semester; 0-2 months |
| Start time | Summer (May/June): Projects are expected to begin by Monday, June 9 and end by Friday, August 15 |
| In-person, remote, or hybrid? | Hybrid Project |
| Level of collaboration | Individual student project |
| Benefits | Stipend |
| Who is eligible | All undergraduate students who have taken or are currently enrolled in relevant courses to the project (e.g., EAS 230 or 240, CSE 113 or 115) are preferred. Students who have experience or basic skills in coding (e.g., Python, R) are preferred. |
Project mentor
Aurora del Carmen Munguia-Lopez
Assistant Professor
Department of Chemical and Biological Engineering
Phone: (716) 645-8650
Email: amunguia@
Start the project
- Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
- After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.)
Preparation activities
Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase.
Review the recent publications on our lab website:
https://sites.google.com/view/sustainablesystemsengineering/publications
Expectations for Student:
• Contribute at least 350 hours of effort over a 10-week span during the summer of 2025
• Attend workshops/seminars organized by the NSF grant team
• Submission of project forms/reports (e.g., periodic reflections, end-of-project report)
• Consider completion of the Data Science in Engineering Micro-Credential
Keywords
Chemical and Biological Engineering, systems engineering, computational tools, data science, plastics recycling, sustainability