Date of Award

January 2021

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Kai Chen

Abstract

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reduce exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1×1 km2. The ensemble model incorporated four base models, including a generalized additive model, a generalized additive mixed model, and two machine learning models (random forest and extreme gradient boosting), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Among base models, the two machine learning models outperformed the two regression models. In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, Normalized Difference Vegetation Index, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment and reduce exposure misclassification in future epidemiological studies.

Comments

This thesis is restricted to Yale network users only. This thesis is permanently embargoed from public release.

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