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Thesis

Repository for Computational Science Masters thesis: Citizen-Data-Driven Validation and Acceleration of HYSPLIT Air Pollution Simulations with Physics-Guided Machine Learning

Instructions for use

This repository contains code for the following tasks, in the order that the folders are in:

  • Processing of citizen reported data of percieved odour from 2016-2023.
  • Results of HYSPLIT and application of DCGAN deep learning model to generate more data for these HYSPLIT simulations.
  • Supercomputer (Snellius) implementation of HYSPLIT simulations for Pittsburgh.

The hysplit folder also contains the citizen-data-driven validation scheme for air pollution models. Please see READme pages within each folder for more information.