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NASA SEES students continue their research with the GME team

During the pandemic, we have collaborated with the NASA STEM Enhancement for Earth Science (SEES) high school internship program hosted by the University of Texas and started the urban heat island group and Air Quality Initiative group to engage high school students in scientific studies. We hosted the program virtually. We had 6 urban heat island students in 2021, and around 20 students in urban heat island and air quality groups in the following two years. 

Each year, we have a few students show their interest in continuing their research with our team. In the first year, a student named Moneel Patel from the urban heat island group developed his research into a research paper in the following year. The paper is titled Analyzing the Relationship Between Particulate Matter 2.5, Land Surface Temperature, and Normalized Difference Vegetation Index: A Case Study of Austin, Texas. He is a sophomore at the University of Texas-Austin this year. 

In 2022, Audrey Kuo from the air quality group continued her research with us. Her research is the proximity analysis of multiple air quality sensor networks, including EPA sensors, PurpleAir sensors, and Davis sensors. She aims to find the sensors within a certain distance and to compare the results from different types of sensors. She is a freshman this year and still continues her research with us. 

This year, ten SEES interns participated in the group and another three student interns (two with NASA Langley Research Center and one with the University of Toledo) participated in the five-week virtual program this year. Small sensors, e.g. PurpleAir sensors are used in most students’ projects. The topic covers ER visits due to asthma, economic factors, visibility and airline cancellation, PM 2.5 levels in urban and suburban areas, PM2.5 in coastal and mainland areas, indoor and outdoor PM 2.5 comparison, as well as intercomparing different types of small sensors. 

Hudson Kim and Riya Gupta continued their research about wildfire modeling and are preparing their manuscript titled A Geospatial Approach to Wildfire Risk Modeling using Machine Learning and Remote Sensing Data for publishing.