The Real Burnout: The Effects of Climate Change and Particulate Air Matter Pollution on K-12 Education

Abstract

As global warming rises, environmental factors pose new challenges for young individuals; the detrimental effects of pollution exposure extend into health, social well-being, and schooling. This paper introduces a novel method to utilize remote-sensing data to study pollution, specifically PM2.5 (a particulate matter air pollutant that comes from sources such as exhaust and natural fires). To fill in these knowledge gaps from EPA pollution monitors and develop an alternative source of reliable air quality data, I fine-tune a neural model to detect PM2.5 levels from high-resolution satellite images. I construct a data set of approximately 2,500 satellite images taken before, during, and after large wildfires in California, Oregon, and Colorado. I label the images by their corresponding PM2.5 pollution levels, as reported by the nearest EPA air quality monitor. After optimizing hyper parameters, the testing accuracy is just below 90 percent for ViT, while slightly above 90 percent for ResNet-50 and Swin Transformer. The model distinguishes well between very poor and good air quality, with most ambiguities and mistakes at intermediate levels. After completing this analysis, I examine the effects of pollution exposure on student academic performance. I combine pollution data from the EPA with school-level standardized testing data in California, creating a panel that spans 16 years. I find that air pollution exposure has a statistically significant negative effect on the percentage of students who meet or exceed standards on statewide standardized tests, with more severe effects in male, Black, and economically disadvantaged students.