TOPS-T SCHOOL Module 2: Air Quality

NOTE: This entry includes the previewable elements from the repository along with the entire GIT repository in the .zip file Welcome SCHOOL Module 2: Air Quality Welcome to the second module of the SCHOOL curriculum! The Science Core Heuristics for Open Science Outcomes in Learning (SCHOOL) is part of the NASA Transform to Open Science (TOPS) Training (TOPST) initiative, designed to teach the data science lifecycle using data from the NASA Earth Sciences division and to foster an inclusive culture of open science. You can learn more about the SCHOOL Project and other modules on the SCHOOL Project home page. This TOPS SCHOOL module on the theme of “Air Quality and Health” provides a foundational understanding of the complex interplay between air quality, public health, and environmental justice. We will explore how we can use spatial data for the study of air pollution, the sources and types of pollutants, and their impacts on vulnerable communities. You will gain critical insights into how environmental factors shape health outcomes. As you engage with the lessons, you will develop a comprehensive understanding of key concepts, including the composition of air, the significance of social vulnerability, and the tools available for analyzing environmental data. Ultimately, these lessons will empower you to contribute meaningfully to discussions and actions pertaining to air quality and environmental justice. The following lessons examine how poor air quality impacts human health. Each lesson uses one or more datasets to walk users through accessing and analyzing data, and further adapting the code to perform their analyses including: data cleaning, processing to subset to an area of interest, and creating visualizations to share what they have learned with their communities, explore the relationships with variables such as extreme temperatures, demographic and socioeconomic factors, environmental injustice, and environmental disasters. This module is designed to provide undergraduate students and early-career researchers with an introduction to the data science “life cycle” and sample code describing how Open Science principles can be effectively applied to earth sciences, particularly in the context of air pollution and its impact on health. Teaching earth science topics and coding skills in depth is beyond the scope of the SCHOOL Modules. Instead, the SCHOOL Project aims to provide users with the skills to adapt the skills learned in SCHOOL lessons to the users’ own Open Science workflow. To learn more about Open Science, explore NASA’s TOPS Open Science 101 Curriculum. To explore other themes in the SCHOOL project, visit our Modules Page. Introduction to Air Quality and Module Lessons Introduction to Air Quality: This primer contains background on air quality and the way it impacts human populations. Introduction to Air Quality Comparing Air Quality Measures in New York City: Sensors on the Ground and from Space: This lesson will compare data from different air quality monitoring methods, including low-cost sensors deployed by community members in New York City and satellite-derived estimates of air pollution. Participants will learn how to access, process, and visualize these datasets to understand spatial and temporal variations in air quality across the city. Lesson 1: Comparing Air Quality Measures in New York City: Sensors on the Ground and from Space Exploring Air Quality, Social Vulnerability, and Health Outcomes in Metro Detroit: Participants will investigate how the Social Vulnerability Index (SVI) highlights areas at greater risk for adverse health outcomes due to environmental hazards. This lesson will examine the connections between socioeconomic status, health disparities, and environmental injustice. Lesson 2: Exploring Air Quality, Social Vulnerability, and Health Outcomes in Metro Detroit Environmental Justice Screening and Mapping Tool (EJScreen): Explore the environmental and demographic characteristics of a geographic area using the Environmental Protection Agency’s environmental justice screening and mapping tool. Lesson 3: Exploring EJScreen: Environmental Justice Screening and Mapping Tool Particulate Matter Across Socioeconomic Strata of Countries: Analyzing the Global Gridded Relative Deprivation Index Version 1 (GRDIv1) and annual PM2.5 grids to explore global relationships between socioeconomic vulnerability and particulate matter concentrations over time. This lesson will provide insights into how air quality varies across different regions and its implications for public health, focusing on both local and global contexts. Lesson 4: Particulate Matter Across Socioeconomic Strata of Countries This course was made possible thanks to the work of our NASA Transform to Open Science (TOPS) team, our SCHOOL Open Science team, open science Subject Matter Experts (SMEs), and the SCHOOL Development team! Open Science Team Member Contributors Eldar Imanbekov, Undergraduate Student, National Louis University Muhammad Kamboh, Undergraduate Student, National Louis University Brandon Ortiz, Undergraduate Student, National Louis University Lara Pistolesi, Masters Student, University of Derby Open Science Subject Matter Experts Shristi Adhikari, Phd Student, Kentucky State University Yoonjung Ahn, Assistant Professor, University of Kansas Anne-Lise Boyer, Phd Student, University of Arizona Hazem Mahmoud, Scientist, NASA ASDC Alamin Molla, Phd Student, Arizona State University Akshay Mestry, Adjunct Professor, National Louis University Dhruvil Prajapati, Technical Analyst, National Louis University Antonio Tovar, Professor, National Louis University Module Development Team Elaine Famutimi, Technical Analyst, New York City DOT Deborah Balk, Director, CUNY Baruch Josh Brinks, Research Scientist, ISCIENCES LLC Camilla Green, Technical Analyst, Columbia University Kytt MacManus, Research Scientist, Columbia University Juan Martinez, Technical Analyst, Columbia University Thomas Parris, Director, ISCIENCES LLC Linda Pistolesi, Technical Analyst, Columbia University Eulalia Santin, Financial Analyst, Columbia University Sri Vinay, Technical Analyst, NASA ASDC Greg Yetman, Research Scientist, Columbia University Tracy Wen, Financial Analyst, Columbia University