README.md
In MazamaScience/PWFSLSmoke: Utilities for Working with Air Quality Monitoring Data

PWFSLSmoke R Package

Utilities for Working with PM2.5 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 > 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:

download and easily work with regulatory PM2.5 data from the EPA and AirNow
download and quality control raw monitoring data from AIRSIS and WRCC
convert between UTC and local timezones
apply various algorithms to the data (nowcast, rolling means, aggregation, etc.)
provide interactive timeseries and maps through RStudio’s Viewer pane
create a variety of publication ready maps and timeseries plots

This package is designed to be used with R (>= 3.3) and RStudio so make sure you have those installed first.

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: