Youth Innovation in Air Quality

• In this podcast, employees from Oregon's Department of Environmental Quality interview a high school student who created a tool to predict poor air quality days, which can vary drastically in different weather and wildfire conditions.
• The student does a great job of summarizing how he approached this project, what he learned from it, and the next steps for using this tool.

Positives

• This podcast shows how youth can innovate and create solutions related to major environmental issues.
• The air quality prediction tool is a great example of the interdisciplinary application of computer science, math, and science to address an environmental problem.
• The podcast page also features text describing the interview, a checklist for wildfire preparedness, and a link to a blog written by the student with a diagram to show his software engineering process.

Additional Prerequisites

• Students should be familiar with some computer science/programming terms such as machine learning, algorithm, software, and application.
• A basic understanding of statistical data analysis would help students understand the significance of the tool.
  

Differentiation

• Before listening to the podcast, ask students how they think youth can use computers and software to solve environmental problems.
• Studying the engineering design process before or after listening to this podcast could help learners understand how tools can be created, iterated, and refined to solve problems.
• Have students think about a problem in their lives or communities that could be helped by aggregating data and creating a prediction tool or evaluating the data.
• Explore government websites that provide data on weather, pollution, and environmental conditions such as the EPA website, NOAA, USGS, etc., and have students enroll in alerts for hazardous conditions or use the data to research trends using linear regression or other statistical tests.