knitr::opts_chunk$set(echo = TRUE, fig.width = 7, fig.height = 5)
Install from CRAN with:
install.packages('AirMonitor')
Install the latest version from GitHub with:
devtools::install_github('mazamascience/AirMonitor')
The USFS AirFire group regularly processes monitoring data in support of their various operational tools. Pre-processed, harmonized and QC'ed data files can be loaded with the following functions:
~_load()
-- load data based on a start- and end-time~loadAnnual()
-- load a year's worth of data~loadDaily()
-- load the most recent 45 days of data (updated once per day)~loadLatest()
-- load the most recent 10 days of data (updated every hour)Data archives go back to 2014 or earlier depending on the data source.
We encourage people to embrace "recipe" style coding as enabled by
dplyr and related packages. The special %>%
operator uses the output
of one function as the first argument of the next function, thus allowing for
easy "chaining" of results to create a step-by-step recipe.
With only a few exceptions, all the monitor_
functions accept a mts_monitor
object as their first argument and generate a mts_monitor object as a result
so they can be chained together.
suppressPackageStartupMessages({ library(AirMonitor) Camp_Fire <- Camp_Fire })
Let's say we are interested in the impact of smoke from the 2018 Camp Fire in the Sacramento area.
We would begin by creating a Camp_Fire
object that has all the
monitors in California for the period of interest. The recipe for creating Camp_Fire
has four steps: 1) load annual data; 2) filter for monitors in
California; 3) restrict the date range to Camp Fire dates; 4) remove any monitors
with no valid data in this range.
# create the Camp_Fire 'mts_monitor' object Camp_Fire <- # 1) load annual data monitor_loadAnnual(2018) %>% # 2) filter for California monitor_filter(stateCode == 'CA') %>% # 3) restrict date range monitor_filterDate( startdate = 20181108, enddate = 20181123, timezone = "America/Los_Angeles" ) %>% # 4) remove monitors with no valid data monitor_dropEmpty()
We can use the monitor_leaflet()
function to display these monitors (colored
by maximum PM2.5 value) in an interactive map. This map allows us to
zoom in and click on the monitor in downtown Sacramento to get it's
deviceDeploymentID
-- "127e996697f9731c_840060670010".
monitor_leaflet(Camp_Fire)
We can use this deviceDeploymentID
to create a mts_monitor object for this
single monitor and take a look at a time series plot. Day-night shading and AQI
decorations create a publication-ready plot:
# create single-monitor Sacramento Sacramento <- # 1) start with Camp_Fire Camp_Fire %>% # 2) select a specific device-deployment monitor_select("127e996697f9731c_840060670010") # review timeseries plot Sacramento %>% monitor_timeseriesPlot( shadedNight = TRUE, addAQI = TRUE, main = "Hourly PM2.5 Concentration in Sacramento" ) # add the AQI legend addAQILegend(cex = 0.8)
Next, we can use this specific location to create a mts_monitor object containing all monitors within 50 kilometers (31 miles) of Sacramento.
Sacramento_area <- # 1) start with Camp_Fire Camp_Fire %>% # 2) find all monitors within 50km of Sacramento monitor_filterByDistance( longitude = Sacramento$meta$longitude, latitude = Sacramento$meta$latitude, radius = 50000 ) monitor_leaflet(Sacramento_area)
We can use the same monitor_timeseriesPlot()
function to display the hourly
data for all the monitors in the Sacramento area in a single plot. This gives
a sense of the range of values within the area at any given hour.
Sacramento_area %>% monitor_timeseriesPlot( shadedNight = TRUE, addAQI = TRUE, main = "Wildfire Smoke within 30 miles of Sacramento" ) addAQILegend(lwd = 1, pch = NA, bg = "white", cex = 0.8)
Now we can average together all the monitors and create a local-time, daily average for the Sacramento area.
# 1) start with Sacramento_area Sacramento_area %>% # 2) average together all timeseries hour-by-hour monitor_collapse( deviceID = "Sacramento_area" ) %>% # 3) calculate the local-time daily average (default) monitor_dailyStatistic() %>% # 4) pull out the $data dataframe monitor_getData()
Alternatively, we can plot the daily averages.
# 1) start with Sacramento_area Sacramento_area %>% # 2) average together all timeseries hour-by-hour monitor_collapse() %>% # 3) create daily barplot monitor_dailyBarplot( main = "Daily Average PM2.5 in the Sacramento Area" ) # add the AQI legend addAQILegend(pch = 15, bg = "white", cex = 0.8)
Best of luck analyzing your local air quality data!
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