DatSciTrain_bushfire_specific_pm25_for_locations_2019

Cassandra Yuen and Ivan Hanigan

This repository contains training material for the CARDAT hackyhour Bushfire Smoke V1.3 PM2.5 data and coding with generative AI, introducing the Bushfire Smoke PM2.5 V1.3 dataset with a sample subset and demonstrating code development with the aid of generative AI.

Setup

Install R and RStudio

You will need to install and download R and RStudio to run the code. Instructions are available at https://cardat.github.io/DatSciTrain_set_up_R_and_friends/, as well as a recommended layout for the RStudio interface.

Download the code and associated data

Download this repository by clicking the green <> Code button at the top of this GitHub repository file listing, then select Download ZIP. Extract the contents of the downloaded ZIP to your desired location.

(If you are familiar with Git, you can clone this repository instead.)

Bushfire Smoke PM2.5 data will be made available to hackyhour participants via Cloud CARDAT. Please add these files to the data_provided/ directory alongside the location_crds.csv file. Note that the Bushfire Smoke PM2.5 data are provided for training purposes only, and may not be on-shared or used for any other purpose* beyond this hackyhour.

Installing required R packages

1. Open the DatSciTrain_bushfire_specific_pm25_for_locations_2019.Rproj project file in RStudio, then open main.R.
2. If prompted by RStudio, install the required R packages. Alternatively you can install them with the install.packages function (e.g. install.packages("terra")) in the RStudio console.

Set up access to a generative AI

For this workshop, code development with generative AI will be demonstrated using Claude. To access Claude, go to the website and create an account - a Personal, Free tier account is sufficient.

Also check if you have institutional access to a generative AI (e.g. Microsoft Copilot). You may use this to compare and contrast responses from different generative AIs.

Running the code

You can step through the code line-by-line in RStudio by clicking the "Run" button at the top-right of the code (Source) panel, or using the Ctrl + Enter shortcut.

Data sources

PM2.5 estimates, STL decomposition and selected flags subset from Bushfire Smoke PM2.5 V1.3 dataset. The development and use of this dataset is detailed in the paper:

Borchers-Arriagada, N., Morgan, G.G., Van Buskirk, J., Gopi, K., Yuen, C., Johnston, F.H., Guo, Y., Cope, M. and Hanigan, I.C. (2024) ‘Daily PM2.5 and Seasonal-Trend Decomposition to Identify Extreme Air Pollution Events from 2001 to 2020 for Continental Australia Using a Random Forest Model’, Atmosphere, 15(1341). Available at: https://doi.org/10.3390/atmos15111341.

Access to this dataset for research purposes may be requested via the CARDAT access request form.

Study location coordinates derived from ABS Urban Centres and Localities, 2016