Utilities for working with air quality monitoring data with a focus on small particulates (PM2.5) generated by wildfire smoke. Functions are provided for downloading available data from the United States 'EPA' < https://www.epa.gov/outdoor-air-quality-data> and it's 'AirNow' air quality site < https://www.airnow.gov>. Additional sources of PM2.5 data made accessible by the package include: 'AIRSIS' (password protected) < https://www.oceaneering.com/data-management/> and 'WRCC' < https://wrcc.dri.edu/cgi-bin/smoke.pl>. Pregenerated data compilations are provided by 'PWFSL' < https://www.fs.fed.us/pnw/pwfsl/>.
Utilities for Working with Air Quality Monitoring Data
The USFS Pacific Wildland Fire Sciences Lab AirFire team works to model wildland fire emissions and has created the BlueSky Modeling Framework. This system integrates a wide collection of models along a smoke modeling pipeline (fire information, fuel loadings, consumption modeling, emissions modeling, time rate of emissions modeling, plume height estimations, and smoke trajectory and dispersion modeling). The resulting model output has been integrated into many different smoke prediction systems and scientific modeling efforts.
The PWFSLSmoke R package is being developed for PWFSL to help modelers and scientists more easily work with PM2.5 data from monitoring locations across North America.
The package makes it easier to obtain data, perform analyses and generate reports. It includes functionality to:
Users will want to install the devtools package to have access to the latest version of the package from Github.
The following packages should be installed by typing the following at the RStudio console:
# Note that vignettes require knitr and rmarkdown install.packages('knitr') install.packages('rmarkdown') install.packages('MazamaSpatialUtils') devtools::install_github('mazamascience/PWFSLSmoke', build_vignettes=TRUE)
Any work with spatial data, e.g. assigning countries, states and timezones, will require installation of required spatial datasets. To get these datasets you should type the following at the RStudio console:
library(MazamaSpatialUtils) dir.create('~/Data/Spatial', recursive=TRUE) setSpatialDataDir('~/Data/Spatial') installSpatialData()
Additional R Notebooks that demonstrate the functionality of the package can be found in the localNotebooks directory on github. These notebooks are not part of the package because they require installation of the MazamaSpatialUtils datasets.
To run them you should: