A Geospatial and Machine Learning Framework for Forecasting Ground Level 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.

Recommended citation: Keenan, William J., "A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution" (2024). Electronic Theses and Dissertations. 2398. https://digitalcommons.du.edu/etd/2398 https://digitalcommons.du.edu/etd/2398