Date of Award

6-15-2024

Document Type

Masters Thesis

Degree Name

M.A. in Geography

Organizational Unit

College of Natural Science and Mathematics, Geography and the Environment

First Advisor

Jing Li

Second Advisor

Michael Keables

Third Advisor

Kris Kuzera

Keywords

Air pollution, Air quality, Geographic information science (GIS), Long-short-term-memory (LSTM), Machine learning, Ozone pollution

Abstract

The major detrimental health effects of ground-level ozone (GLO) pollution make it imperative that both policy makers and ordinary citizens have access to high accuracy, high-resolution forecasts of their local area. Recently, advancements in computing power have made it possible to apply artificial intelligence (AI) techniques to a variety of big data modelling problems, including GLO forecasting and estimation. Of these AI methods, deep neural networks (DNN) have demonstrated the highest accuracy due to their ability extract non-linear relationships from high dimensional, noisy data inputs.

This research effort uses novel data sources, namely NOAA’s High Resolution Rapid Refresh (HRRR) meteorology model, and a long-short-term-memory (LSTM) neural network to forecast and interpolate ozone values at high spatiotemporal resolution of 1 hour and 3 km. The accuracies of the LSTM models are analyzed using lagged ozone at various forecast horizons and across the varying geographies of eleven ground sensors. I use Denver, Colorado as my study area due to its long-standing GLO pollution problem and relatively high density of EPA ozone monitoring stations.

Copyright Date

6-2024

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

William J. Keenan

Provenance

Received from ProQuest

File Format

application/pdf

Language

English (eng)

Extent

56 pgs

File Size

1.5 MB

Download

Available for download on Friday, January 31, 2025



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