Comparing Air Quality Index and Cities: Finding Patterns in Data | SubjectToClimate
Keywords: climate change, air pollution, air quality, patterns, data analysis, air quality index, scientific thinking
Formats: Google Docs, Google Sheets
In this 2-4 hour mini-unit, students design methods for identifying patterns in data. Using a real-time air quality index, students explore patterns in the air quality of cities across the globe. This lesson in data analysis has students ask questions about factors that impact global temperatures so students can analyze how these factors might impact air quality. Teachers will assess how students use scientific practices related to data analysis through a "Claim-Evidence-Reasoning" activity.
Step 1 - Inquire: Students explore the World's Air Pollution: Real-time Air Quality Index and discuss variables.
Step 2 - Investigate: Students investigate data on air quality variables and analyze data.
Step 3 - Inspire: Students discuss patterns found in data and inspire others to be "Air Aware."
Student Data Collection Sheet [Google Sheets]
How to Analyze Data Video [Google Drive Video]
Example Air Aware Poster [Google Docs]
Students explore the World's Air Pollution: Real-time Air Quality Index. (15 minutes)
Students open the website and take some time to click around to see what they notice. Teacher can ask students:
What indicators (numbers) are on the website?
Do you notice any patterns in the AQI when you click around different cities?
What do you wonder about when you see this data?
Students share responses to these questions in small groups or with the whole class.
Teacher explains the following answers to the above questions:
The website is a live AQI site. AQI is a universal measure of air quality and it uses several different measures to create one standard number for air quality. According to the Environmental Protection Agency, "The AQI is calculated for four major air pollutants regulated by the Clean Air Act: ground level ozone, particle pollution, carbon monoxide, and sulfur dioxide. For each of these pollutants, the EPA has established national air quality standards to protect public health." [More information can be found at Air Quality Index: A Guide to Air Quality and Your Health.]
Other indicators besides AQI include CO2, PM2.5, PM10, O3, NO2, SO2, CO, temperature, pressure, humidity, and wind. It is important to note that CO2 and O3 are direct greenhouse gases. Additionally, NO2 and SO2 are indirect greenhouse gases.
Answers will vary. During this part of the discussion the teacher invites the students to use the World's Air Pollution: Real-time Air Quality Index find a city that has hazardous AQI levels. Teacher asks, "What do you think is going on in these cities where the AQI is hazardous? How is this impacting health? What communities are most impacted?" [This conversation relates to another StC lesson plan called A Look at Cancer Alley, Louisiana.]
Answers will vary. Teachers listen to the wonderings and use those to help transition to the next step where students decide which cities to study.
Students discuss and decide. (15 minutes)
Teacher explains that students will be comparing cities' AQI. Teacher explains that students need to select cities to compare something. Examples include:
Urban and rural cities.
Cities on different continents.
Cities with varying levels of electricity generated by renewable energy.
Cities with robust public transportation infrastructure and cities that do not.
Students decide their cities. Students should select 6-10 cities.
Option A: Short-Term Data Review (10 minutes)
Students review data.
Teacher shares a dataset with the students. Teacher can collect their own data or use the data in the Student Data Collection Sheet.
Students look at the data and try to find a pattern. Students share what notice. The goal here is for students to see how difficult it is to find patterns in large datasets without the help of a computer. Most students will say they can't find a pattern or relationship among the data.
Teacher explains the difficulty of finding patterns in large datasets without the help of computer analysis. This transitions into students analyzing data.
Option B: Long-Term Data Collection (100 minutes)
Students record data.
Students spend 10 minutes each school day recording data from the World's Air Pollution: Real-time Air Quality Index. Students use the Student Data Collection Sheet to record the data. This activity is great for the start of class, end of class, or for homework. Recording data properly in a spreadsheet is an important foundational skill for all scientists. In most cases instruments will send this data directly to the computer. However, for the purpose of this activity it helps the students understand data because they are able to see where the data is coming from.
As the data is being collected, students try to find a pattern. Students share what they might have noticed. The goal here is for students to see how difficult it is to find patterns in large datasets without the help of a computer. Most students will say they can't find a pattern or relationship among the data.
For both options, students complete this next step. (45 minutes)
Watch this video (20 minutes, 58 seconds) for a detailed explanation of these steps.
Select at least two variables to compare for analysis (e.g., AQI and temperature, AQI and O3, AQI and wind, AQI and location, etc.)
Copy and paste variables on a new sheet.
Insert "chart."
Students look at the graph to find patterns in the data, specifically focusing on describing the relationship among the variables. Examples include:
"As the O3 increases, the AQI increases."
"As the PM2.5 decreases, the AQI decreases."
"There is no relationship between temperature and AQI."
Students can share their findings with each other or with the whole class.
It is really important to note the difference between correlation and causation. Just because we notice a pattern in the data does not mean one variable caused another. We must guide the students to think past this. If the students notice a pattern or relationship in the data we simply call it correlation. It means we need to study that pattern even more!
Teacher asks, "What do we do with this information now?" The students have spent time finding patterns and analyzing their data but must understand there is always a next step. Awareness is normally the next step. Once awareness is built, most people take action to reduce negative health impacts from poor air quality or try to reduce or eliminate pollution that leads to poor air quality.
Students create and share an "Air Aware" poster. This poster will have two main areas. (Example poster)
First Area: Students include a graph and a brief Claim-Evidence-Reasoning statement. (Rubric for CER) Students will need to do some research to find the source of the pollution. For example, if they chose to analyze O3 in Houston, they must research the main causes of O3 in Houston.
Second Area: Students create "Air Aware" messaging that must include one suggestion for health such as "When the AQI is high, reduce outdoor activities."
Students can share these posters on the school website or appropriately on social media. Students could print these posters and post them at school or even share with community members outside of school.
This is a great introduction to data collection and analysis.
It shows students how to navigate data and not feel overwhelmed.
This can be a short-term or long-term option, depending on your preferences.
This is a great lesson for math and science collaboration.
This lesson connects well with the StC lessons A Look at Cancer Alley, Louisiana and Air Pollution and You.
Students should have a basic understanding of air pollution and climate change.
Some proficiency in Google Sheets is recommended.
Students of different abilities in your class can either complete Option A or Option B during the "Investigate" phase.
Students with varying technology skills can be grouped together so they can help each other navigate Google Sheets.
Students can complete this project individually or in groups.