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Publication Date
2025-5
First Advisor
Scott LaCombe
Document Type
Honors Project
Degree Name
Bachelor of Arts
Department
Statistical and Data Sciences
Keywords
air pollution, wildfires, deep learning, Auto Encoder, Neural Network, singular value decomposition
Abstract
As the planet warms, wildfires are expected to become a major contribution to air pollution levels, specifically across the western United States (Calvin et al. 2023; Abatzoglou and Williams 2016). The burning of vegetation releases particulate matter–a mixture of solid particles and liquid droplets that hang in the air and can be inhaled (US EPA 2016). Around 90% of this particulate matter is less than 2.5 microns in diameter, referred to as PM2.5 (Vicente et al. 2013). Unfortunately, studies attempting to investigate associations between such health outcomes and wildfiresmoke-derived PM2.5 are plagued by low statistical power and data with poor spatial resolution (Reid et al. 2016). This thesis explores a recently proposed, Deep Learning-enabled interpolation method (Amato et al. 2020) to predict ground-level smoke-derived PM2.5 (SPM2.5) concentrations. I use a AutoEncoder to allow the inclusion of important meteorological covariates, expanding on the previously proposed methodology. The AutoEncoder shows a promising ability to reconstruct SPM2.5, with a post-reconstruction unnormalized MSE of 0.295. However, the interpolation proposed by (Amato et al. 2020) does not handle discontinuous data well, suggesting that further study of machine learning on discontinuous and non-differentiable data is warranted.
Rights
©2025 Matilda Slosser. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Language
English
Recommended Citation
Slosser, Matilda, "Spatiotemporal Interpolation of Wildfire Smoke-Derived PM2.5 Using Deep Neural Networks" (2025). Honors Project, Smith College, Northampton, MA.
https://scholarworks.smith.edu/theses/2675
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Comments
73 pages : color illustrations. Includes bibliographical references (pages 62-73).