Students will engage in a “Zoom In Inquiry” learning routine to understand the symbols on a world map that shows population-weighted concentrations of PM_2.5. They will reflect on how their perception of the image changed as they saw more of the image.

Students will engage in a “Zoom In Inquiry” learning routine to understand a world map that shows changes in PM_2.5-attributable mortality per 100,000 population (Bondie, 2013).

In this 5E’s lesson, students observe maps that show smoke and AOD levels surrounding Fresno, California at the time when the 2020 Creek Fire was burning. Students construct a claim that identifies a relationship between fire-related data and air quality data.

Students review a video that models the global impact of smoke from fires to develop an understanding of how models can be used to interpret and forecast phenomena in the Earth System. 

In this 5Es lesson, students will uncover how changes in global air quality have impacted human health in cities between 2000 and 2019.

In this lesson, students will explore the effect of aerosols on sky color and visibility by using an interactive virtual model. 

Learn about volcanic ash and watch a visualization of the Calbuco volcano to see how ash travels around the world. 

This mini lesson engages students in watching a NASA video related to accumulated dust that makes the trans-Atlantic journey from the Sahara Desert to the Amazon rainforest using NASA's CALIPSO satellite. Students will examine a model and answer questions related to dust transport and the introduction of phosphorus to the soils of the Amazon.

Students will synthesize information from maps that show population, concentrations of PM_2.5, and PM_2.5-attributable mortality across the globe in order to draw conclusions about the relationship between particulate pollution and human health. 

Students analyze two North Pole orthographic data visualizations produced from soil moisture data. After describing trends in the seasonal thaw of land surfaces, students demonstrate their understanding of Earth’s energy budget by explaining relationships and make predictions about the dataset.