Package 'AirMonitor'

Description

This function is a convenience wrapper around graphics::legend(). It will show the AQI colors and names by default if col and legend are not specified.

AQI categories are arranged with lower levels at the bottom of the legend to match the arrangement in the plot. This is different from the default "reading order" so you may wish to reverse the order of user supplied arguments with rev() .

Usage

addAQILegend(
  x = "topright",
  y = NULL,
  pollutant = c("PM2.5", "CO", "OZONE", "PM10", "AQI"),
  palette = c("EPA", "subdued", "deuteranopia"),
  languageCode = c("eng", "spa"),
  NAAQS = c("PM2.5_2024", "PM2.5"),
  ...
)

Arguments

Value

A list with components rect and text is returned invisbly. (See legend.)

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Description

Draws AQI lines across a plot at the levels appropriate for The monitor_timeseriesPlot function uses this function internally when specifying addAQI = TRUE. pollutant.

Usage

addAQILines(
  pollutant = c("PM2.5", "CO", "OZONE", "PM10", "AQI"),
  palette = c("EPA", "subdued", "deuteranopia"),
  NAAQS = c("PM2.5_2024", "PM2.5"),
  ...
)

Arguments

Value

No return value, called to add lines to a time series plot.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Description

Draws a stacked bar indicating AQI levels on one side of a plot The monitor_timeseriesPlot function uses this function internally when specifying addAQI = TRUE.

Usage

addAQIStackedBar(
  pollutant = c("PM2.5", "CO", "OZONE", "PM10", "AQI"),
  palette = c("EPA", "subdued", "deuteranopia"),
  width = 0.01,
  height = 1,
  pos = c("left", "right"),
  NAAQS = c("PM2.5_2024", "PM2.5")
)

Arguments

Value

No return value, called to add color bars to a time series plot.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Description

Draw shading rectangles on a plot to indicate nighttime hours. The monitor_timeseriesPlot function uses this function internally when specifying shadedNight = TRUE.

Usage

addShadedNight(timeInfo, col = adjustcolor("black", 0.1))

Arguments

Value

No return value, called to add day/night shading to a timeseries plot.

Description

The US Forest Service AirFire group maintains an archive of processed monitoring data. The base URL for this archive is used as the default in all ~_load() functions.

"https://airfire-data-exports.s3.us-west-2.amazonaws.com/monitoring/v2"

Usage

AirFire_S3_archiveBaseUrl

Format

A url

Details

AirFire_S3_archiveBaseUrl

Description

Loads pre-generated .rda files containing hourly AirNow data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function contain a single year's worth of data

For the most recent data in the last 10 days, use airnow_loadLatest().

For daily updates covering the most recent 45 days, use airnow_loadDaily().

For archival data for a specific month, use airnow_loadMonthly().

Pre-processed AirNow exists for the following parameters:

1. PM2.5

Usage

airnow_loadAnnual(
  year = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  parameterName = "PM2.5"
)

Arguments

Value

A mts_monitor object with AirNow data. (A list with meta and data dataframes.)

See Also

airnow_loadDaily

airnow_loadLatest

airnow_loadMonthly

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

# See https://en.wikipedia.org/wiki/2017_Montana_wildfires

# Daily Barplot of Montana wildfires
airnow_loadAnnual(2017) \
  monitor_filter(stateCode == "MT") \
  monitor_filterDate(20170701, 20170930, timezone = "America/Denver") \
  monitor_dailyStatistic() \
  monitor_timeseriesPlot(
    ylim = c(0, 300),
    xpd = NA,
    addAQI = TRUE,
    main = "Montana 2017 -- AirNow Daily Average PM2.5"
  )

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing hourly AirNow data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated once per day and contain data for the previous 45 days.

For the most recent data in the last 10 days, use airnow_loadLatest().

For data extended more than 45 days into the past, use airnow_loadAnnual().

Pre-processed AirNow exists for the following parameters:

1. PM2.5

2. PM2.5_nowcast

Usage

airnow_loadDaily(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  parameterName = "PM2.5"
)

Arguments

Value

A mts_monitor object with AirNow data. (A list with meta and data dataframes.)

See Also

airnow_loadAnnual

airnow_loadLatest

airnow_loadMonthly

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

airnow_loadDaily() \
  monitor_filter(stateCode == "WA") \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing the most recent AirNow data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated multiple times an hour and contain data for the previous 10 days.

For daily updates covering the most recent 45 days, use airnow_loadDaily().

For data extended more than 45 days into the past, use airnow_loadAnnual().

Pre-processed AirNow exists for the following parameters:

1. PM2.5

2. PM2.5_nowcast

Usage

airnow_loadLatest(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  parameterName = "PM2.5"
)

Arguments

Value

A mts_monitor object with AirNow data. (A list with meta and data dataframes.)

See Also

airnow_loadAnnual

airnow_loadDaily

airnow_loadMonthly

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

airnow_loadLatest() \
  monitor_filter(stateCode == "WA") \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing hourly AirNow data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function contain a single month's worth of data

For the most recent data in the last 10 days, use airnow_loadLatest().

For daily updates covering the most recent 45 days, use airnow_loadDaily().

For data extended more than 45 days into the past, use airnow_loadAnnual().

Pre-processed AirNow exists for the following parameters:

1. PM2.5

Usage

airnow_loadMonthly(
  monthStamp = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  parameterName = "PM2.5"
)

Arguments

Value

A mts_monitor object with AirNow data. (A list with meta and data dataframes.)

Description

Loads pre-generated .rda files containing annual AIRSIS data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

Current year files loaded by this function are updated once per week.

For the most recent data in the last 10 days, use airsis_loadLatest().

For daily updates covering the most recent 45 days, use airsis_loadDaily().

Usage

airsis_loadAnnual(
  year = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with AIRSIS data. (A list with meta and data dataframes.)

Note

Some older AIRSIS timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

airsis_loadDaily

airsis_loadLatest

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

# See https://en.wikipedia.org/wiki/Camp_Fire_(2018)

# AIRSIS monitors during the Camp Fire
airsis_loadAnnual(2018) \
  monitor_filter(stateCode == "CA") \
  monitor_filterDate(20181101, 20181201) \
  monitor_dropEmpty() \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing daily AIRSIS data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated once per day and contain data for the previous 45 days.

For the most recent data in the last 10 days, use airsis_loadLatest().

For data extended more than 45 days into the past, use airsis_loadAnnual().

Usage

airsis_loadDaily(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with AIRSIS data. (A list with meta and data dataframes.)

Note

Some older AIRSIS timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

airsis_loadAnnual

airsis_loadLatest

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

airsis_loadDaily()\ %>\
  monitor_filter(stateCode == "CA") \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing the most recent AIRSIS data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated multiple times an hour and contain data for the previous 10 days.

For daily updates covering the most recent 45 days, use airsis_loadDaily().

For data extended more than 45 days into the past, use airsis_loadAnnual().

Usage

airsis_loadLatest(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with AIRSIS data. (A list with meta and data dataframes.)

Note

Some older AIRSIS timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

airsis_loadAnnual

airsis_loadDaily

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

airsis_loadLatest()\ %>\
  monitor_filter(stateCode == "CA") \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

This function converts hourly PM2.5 measurements into AQI category levels. These levels can then be converted to colors or names using the arrays found in US_AQI.

Usage

aqiCategories(
  x,
  pollutant = c("PM2.5", "AQI", "CO", "NO", "OZONE", "PM10", "SO2"),
  NAAQS = c("PM2.5_2024", "PM2.5"),
  conversionArray = NULL
)

Arguments

Details

By default, return values will be integers in the range 1:6 or NA. The conversionArray parameter can be used to convert these integers into whatever is specified in the first six elements of conversionArray. A typical usage would be: conversionArray = US_AQI$names_eng.

Value

A vector or matrix of AQI category indices in the range 1:6.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

See Also

aqiColors

Examples

library(AirMonitor)

# Lane County, Oregon AQSIDs all begin with "41039"
LaneCounty <-
  NW_Megafires %>%
  monitor_filter(stringr::str_detect(AQSID, '^41039')) %>%
  monitor_filterDate(20150822, 20150823)

LaneCounty %>%
  aqiCategories()

LaneCounty %>%
  aqiCategories(conversionArray = US_AQI$names_eng)

Description

This function uses the leaflet::colorBin() function to return a vector or matrix of colors derived from data values.

Usage

aqiColors(
  x,
  pollutant = c("PM2.5", "AQI", "CO", "NO", "OZONE", "PM10", "SO2"),
  palette = c("EPA", "subdued", "deuteranopia"),
  na.color = NA,
  NAAQS = c("PM2.5_2024", "PM2.5")
)

Arguments

Value

A vector or matrix of AQI colors to be used in maps and plots.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

See Also

aqiCategories

Examples

library(AirMonitor)

# Fancy plot based on pm2.5 values
pm2.5 <- Carmel_Valley$data[,2]
Carmel_Valley %>%
  monitor_timeseriesPlot(
    shadedNight = TRUE,
    pch = 16,
    cex = pmax(pm2.5 / 100, 0.5),
    col = aqiColors(pm2.5),
    opacity = 0.8
  )

Description

The Camp_Fire dataset provides a quickly loadable version of a mts_monitor object for practicing and code examples.

Usage

Camp_Fire

Format

A mts_monitor object with 360 rows and 134 columns of data.

Details

The 2018 Camp Fire was the deadliest and most destructive wildfire in California's history, and the most expensive natural disaster in the world in 2018 in terms of insured losses. The fire caused at least 85 civilian fatalities and injured 12 civilians and five firefighters. It covered an area of 153,336 acres and destroyed more than 18,000 structures, most with the first 4 hours. Smoke from the fire resulted in the worst air pollution ever for the San Francisco Bay Area and Sacramento Valley.

This dataset was was generated on 2022-10-12 by running:

library(AirMonitor)

Camp_Fire <-
  monitor_loadAnnual(2018) %>%
  monitor_filter(stateCode == 'CA') %>%
  monitor_filterDate(
    startdate = 20181108,
    enddate = 20181123,
    timezone = "America/Los_Angeles"
 ) %>%
 monitor_dropEmpty()

save(Camp_Fire, file = "data/Camp_Fire.rda")

Description

The Carmel_Valley dataset provides a quickly loadable version of a mts_monitor object for practicing and code examples.

Usage

Carmel_Valley

Format

A mts_monitor object with 576 rows and 2 columns of data.

Details

In August of 2016, the Soberanes fire in California burned along the Big Sur coast. At the time, it was the most expensive wildfire in US history. This dataset contains PM2.5 monitoring data for the monitor in Carmel Valley which shows heavy smoke as well as strong diurnal cycles associated with sea breezes. Data are stored as a mts_monitor object and are used in some examples in the package documentation.

This dataset was generated on 2022-10-12 by running:

library(AirMonitor)

Carmel_Valley <-
  airnow_loadAnnual(2016) %>%
  monitor_filterMeta(deviceDeploymentID == "a9572a904a4ed46d_840060530002") %>%
  monitor_filterDate(20160722, 20160815)

save(Carmel_Valley, file = "data/Carmel_Valley.rda")

Description

State codes for the 48 contiguous states +DC that make up the CONtinental US.

CONUS <- c( "AL","AZ","AR","CA","CO","CT","DE","FL","GA", "ID","IL","IN","IA","KS","KY","LA","ME","MD", "MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ", "NM","NY","NC","ND","OH","OK","OR","PA","RI","SC", "SD","TN","TX","UT","VT","VA","WA","WV","WI","WY", "DC" )

Usage

CONUS

Format

A vector with 49 elements

Details

CONUS state codes

Description

Vector of names of the required monitor$meta columns. These represent metadata columns that must exist in every valid mts_monitor object. Any number of additional columns may also be present.

Usage

coreMetadataNames

Format

A vector of character strings

Details

coreMetadataNames

Examples

print(coreMetadataNames, width = 80)

Description

Loads pre-generated .rda files containing hourly AirNow data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function contain a single year's worth of data.

Pre-processed AirNow exists for the following parameter codes:

1. 88101 – PM2.5 FRM/FEM Mass

2. 88502 – PM2.5 non FRM/FEM Mass

Specifying parameterCode = "PM2.5" will merge records from both sources.

Usage

epa_aqs_loadAnnual(
  year = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  parameterCode = c("PM2.5", "88101", "88502")
)

Arguments

Value

A mts_monitor object with EPA AQS data. (A list with meta and data dataframes.)

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

# See https://en.wikipedia.org/wiki/2017_Montana_wildfires

# Daily Barplot of Montana wildfires
epa_aqs_loadAnnual(2015) \
  monitor_filter(stateCode == "WA") \
  monitor_filterDate(20150724, 20150907) \
  monitor_dailyStatistic() \
  monitor_timeseriesPlot(
    main = "Washington 2015 -- AirNow Daily Average PM2.5"
  )

}, silent = FALSE)

## End(Not run)

Description

Nowcast and AQI algorithms are applied to the data in the monitor object. A modified mts_monitor object is returned whre values have been replaced with their Air Quality Index equivalents. See monitor_nowcast.

Usage

monitor_aqi(
  monitor,
  version = c("pm", "pmAsian", "ozone"),
  includeShortTerm = FALSE,
  NAAQS = c("PM2.5_2024", "PM2.5")
)

Arguments

Value

A modified mts_monitor object containing AQI values. (A list with meta and data dataframes.)

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

References

https://en.wikipedia.org/wiki/Nowcast_(Air_Quality_Index)

https://www.airnow.gov/aqi/aqi-basics/

Description

The variable(s) in ... are used to specify columns of monitor$meta to use for ordering. Under the hood, this function uses arrange on monitor$meta and then reorders monitor$data to match.

Usage

monitor_arrange(monitor, ...)

Arguments

Value

A reorderd version of the incoming mts time series object. (A list with meta and data dataframes.)

See Also

monitor_select

Examples

library(AirMonitor)

Camp_Fire$meta$elevation[1:10]

byElevation <-
  Camp_Fire %>%
  monitor_arrange(elevation)

byElevation$meta$elevation[1:10]

Description

Evaluates all timezones in monitor and returns the most common one. In the case of a tie, the alphabetically first one is returned.

Usage

monitor_bestTimezone(monitor = NULL)

Arguments

Value

A valid base::OlsonNames() timezone.

Description

Checks on the validity of an mts_monitor object. If any test fails, this function will stop with a warning message.

Usage

monitor_check(monitor)

Arguments

Value

Invisibly returns TRUE if mts_monitor has the correct structure. Stops with an error message otherwise.

Description

Collapses data from all time series in a mts_monitor into a single-time series mts_monitor object using the function provided in the FUN argument. The single-time series result will be located at the mean longitude and latitude unless longitude and latitude parameters are specified.

Any columns of monitor$meta that are constant across all records will be retained in the returned mts_monitor meta dataframe.

The core metadata associated with this location (e.g. countryCode, stateCode, timezone, ...) will be determined from the most common (or average) value found in monitor$meta. This will be a reasonable assumption for the vast majority of intended use cases where data from multiple instruments in close proximity are averaged together.

Usage

monitor_collapse(
  monitor,
  longitude = NULL,
  latitude = NULL,
  deviceID = "generatedID",
  FUN = mean,
  na.rm = TRUE,
  ...
)

Arguments

Value

A mts_monitor object representing a single time series. (A list with meta and data dataframes.)

Note

After FUN is applied, values of +/-Inf and NaN are converted to NA. This is a convenience for the common case where FUN = min/max or FUN = mean and some of the time steps have all missing values. See the R documentation for min for an explanation.

Examples

library(AirMonitor)

# Lane County, Oregon AQSIDs all begin with "41039"
LaneCounty <-
  NW_Megafires %>%
  monitor_filter(stringr::str_detect(AQSID, '^41039')) %>%
  monitor_filterDate(20150821, 20150828)

# Get min/max for all monitors
LaneCounty_min <- monitor_collapse(LaneCounty, deviceID = 'LaneCounty_min', FUN = min)
LaneCounty_max <- monitor_collapse(LaneCounty, deviceID = 'LaneCounty_max', FUN = max)

# Create plot
monitor_timeseriesPlot(
  LaneCounty,
  shadedNight = TRUE,
  main = "Lane County Range of PM2.5 Values"
)

# Add min/max lines
monitor_timeseriesPlot(LaneCounty_max, col = 'red', type = 's', add = TRUE)
monitor_timeseriesPlot(LaneCounty_min, col = 'blue', type = 's', add = TRUE)

Description

Create a combined mts_monitor from any number of mts_monitor objects or from a list of mts_monitor objects. The resulting mts_monitor object with contain all deviceDeploymentIDs found in any incoming mts_monitor and will have a regular time axis covering the the entire range of incoming data.

If incoming time ranges are tempporally non-contiguous, the resulting mts_monitor will have gaps filled with NA values.

An error is generated if the incoming mts_monitor objects have non-identical metadata for the same deviceDeploymentID unless replaceMeta = TRUE.

Usage

monitor_combine(
  ...,
  replaceMeta = FALSE,
  overlapStrategy = c("replace all", "replace na")
)

Arguments

Value

A combined mts_monitor object. (A list with meta and data dataframes.)

Note

Data are combined with a "later is better" sensibility where any data overlaps exist. Incoming mts_monitor objects are ordered based on the time stamp of their last record. Any data records found in a "later" mts_monitor will overwrite data associated with an "earlier" mts_monitor.

With overlapStrategy = "replace all", any data records found in "later" mts_monitor objects are preferentially retained before the "shared" data are finally reordered by ascending datetime.

With overlapStrategy = "replace missing", only missing values in "earlier" mts_monitor objects are replaced with data records from "later" time series.

Examples

library(AirMonitor)

# Two monitors near Pendelton, Oregon
#
# Use the interactive map to get the deviceDeploymentIDs
#   NW_Megafires %>% monitor_leaflet()

Pendleton_West <-
  NW_Megafires %>%
  monitor_select("f187226671d1109a_410590121_03") %>%
  monitor_filterDatetime(2015082300, 2015082305)

Pendleton_East <-
  NW_Megafires %>%
  monitor_select("6c906c6d1cf46b53_410597002_02") %>%
  monitor_filterDatetime(2015082300, 2015082305)

monitor_combine(Pendleton_West, Pendleton_East) %>%
  monitor_getData()

Description

Creates a daily barplot of data from a mts_monitor object.

Reasonable defaults are chosen for annotations and plot characteristics. Users can override any defaults by passing in parameters accepted by graphics::barplot.

Usage

monitor_dailyBarplot(
  monitor = NULL,
  id = NULL,
  add = FALSE,
  addAQI = FALSE,
  palette = c("EPA", "subdued", "deuteranopia"),
  opacity = NULL,
  minHours = 18,
  dayBoundary = c("clock", "LST"),
  NAAQS = c("PM2.5_2024", "PM2.5"),
  ...
)

Arguments

Value

No return value. This function is called to draw an air quality daily average plot on the active graphics device.

Note

The underlying axis for this plot is not a time axis so you cannot use this function to "add" bars on top of a monitor_timeseriesPlot(). See the AirMonitorPlots package for more flexibility in plotting.

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Examples

library(AirMonitor)

layout(matrix(seq(2)))

Carmel_Valley %>% monitor_dailyBarplot()
title("(pre-2024 PM NAAQS)", line = 0)

Carmel_Valley %>% monitor_dailyBarplot(NAAQS = "PM2.5_2024")
title("(updated PM NAAQS)", line = 0)

layout(1)

Description

Daily statstics are calculated for each time series in monitor$data using FUN and any arguments passed in ....

Because the returned mts_monitor object is defined on a daily axis in a specific time zone, it is important that the incoming monitor contain timeseries associated with a single time zone.

Usage

monitor_dailyStatistic(
  monitor = NULL,
  FUN = mean,
  na.rm = TRUE,
  minHours = 18,
  dayBoundary = c("clock", "LST"),
  ...
)

Arguments

Value

A mts_monitor object containing daily statistical summaries. (A list with meta and data dataframes.)

Note

When dayBoundary = "clock", the returned monitor$data$datetime time axis will be defined in the local timezone (not "UTC") with days defined by midnight as it appears on a clock in that timezone. The transition from DST to standard time will result in a 23 hour day and standard to DST in a 25 hour day.

When dayBoundary = "LST", the returned monitor$data$datetime time axis will be defined in "UTC" with times as they appear in standard time in the local timezone. These days will be one hour off from clock time during DST but every day will consist of 24 hours.

Examples

library(AirMonitor)

Carmel_Valley %>%
  monitor_dailyStatistic(max) %>%
  monitor_getData()

Carmel_Valley %>%
  monitor_dailyStatistic(min) %>%
  monitor_getData()

Description

Calculates the number of hours per day each time series in monitor was at or above a given threshold.

Because the returned mts_monitor object is defined on a daily axis in a specific time zone, it is important that the incoming monitor contain only timeseries within a single time zone.

Usage

monitor_dailyThreshold(
  monitor = NULL,
  threshold = NULL,
  na.rm = TRUE,
  minHours = 18,
  dayBoundary = c("clock", "LST"),
  NAAQS = c("PM2.5_2024", "PM2.5")
)

Arguments

Value

A mts_monitor object containing daily counts of hours at or above a threshold value. (A list with meta and data dataframes.)

Note

When dayBoundary = "clock", the returned monitor$data$datetime time axis will be defined in the local timezone (not "UTC") with days defined by midnight as it appears on a clock in that timezone. The transition from DST to standard time will result in a 23 hour day and standard to DST in a 25 hour day.

When dayBoundary = "LST", the returned monitor$data$datetime time axis will be defined in "UTC" with times as they appear in standard time in the local timezone. These days will be one hour off from clock time during DST but every day will consist of 24 hours.

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Examples

library(AirMonitor)

# Hours at MODERATE or above
Carmel_Valley %>%
  monitor_dailyThreshold("Moderate") %>%
  monitor_getData()

# Hours at MODERATE or above with the 2024 updated NAAQS
Carmel_Valley %>%
  monitor_dailyThreshold("Moderate", NAAQS = "PM2.5_2024") %>%
  monitor_getData()

# Hours at UNHEALTHY or above
Carmel_Valley %>%
  monitor_dailyThreshold("Unhealthy") %>%
  monitor_getData()

Description

Two successive steps are used to guarantee that the datetime axis contains no repeated values:

1. remove any duplicate records

2. guarantee that rows are in datetime order

Usage

monitor_distinct(monitor)

Arguments

Value

A mts_monitor object with no duplicated data records. (A list with meta and data dataframes.)

Note

This function is primarily for package-internal use.

Description

The incoming mts_monitor object is subset to retain only time series with valid data.

Usage

monitor_dropEmpty(monitor)

Arguments

Value

A subset of the incoming mts_monitor. (A list with meta and data dataframes.)

Description

This function creates interactive graphs that will be displayed in RStudio's 'Viewer' tab.

Usage

monitor_dygraph(
  monitor,
  title = "title",
  ylab = "PM2.5 Concentration",
  rollPeriod = 1,
  showLegend = TRUE
)

Arguments

Value

Initiates the interactive dygraph plot in RStudio's 'Viewer' tab.

Examples

## Not run: 
library(AirMonitor)

# Multiple monitors
Camp_Fire %>%
  monitor_filter(countyName == "Alameda") %>%
  monitor_dygraph()

## End(Not run)

Description

Filters the monitor argument to include only those time series located within a certain radius of a target location. If no time series fall within the specified radius, an empty mts_monitor object will be returned.

When count is used, a mts_monitor object is created containing up to count time series, ordered by increasing distance from the target location. Note that the number of monitors returned may be less than the specified count value if fewer than count time series are found within the target area.

Usage

monitor_filterByDistance(
  monitor,
  longitude = NULL,
  latitude = NULL,
  radius = 50,
  count = NULL,
  addToMeta = FALSE
)

Arguments

Value

A mts_monitor object with monitors near a location.

Note

The returned mts_monitor will have an extra distance. (A list with meta and data dataframes.)

Examples

library(AirMonitor)

# Walla Walla
longitude <- -118.330278
latitude <- 46.065

Walla_Walla_monitors <-
  NW_Megafires %>%
  monitor_filterByDistance(
    longitude = -118.330,
    latitude = 46.065,
    radius = 50000,     # 50 km
    addToMeta = TRUE
  )

Walla_Walla_monitors %>%
  monitor_getMeta() %>%
  dplyr::select(c("locationName", "distanceFromTarget"))

Description

Subsets a mts_monitor object by date. This function always filters to day-boundaries. For sub-day filtering, use monitor_filterDatetime().

Dates can be anything that is understood by MazamaCoreUtils::parseDatetime() including either of the following recommended formats:

• "YYYYmmdd"

• "YYYY-mm-dd"

If either startdate or enddate is not provided, the start/end of the mts_monitor time axis will be used.

Timezone determination precedence assumes that if you are passing in POSIXct values then you know what you are doing.

1. get timezone from startdate if it is POSIXct

2. use passed in timezone

3. get timezone from mts_monitor

Usage

monitor_filterDate(
  monitor = NULL,
  startdate = NULL,
  enddate = NULL,
  timezone = NULL,
  unit = "sec",
  ceilingStart = FALSE,
  ceilingEnd = FALSE
)

Arguments

Value

A subset of the given mts_monitor object. (A list with meta and data dataframes.)

Note

The returned data will run from the beginning of startdate until the beginning of enddate – i.e. no values associated with enddate will be returned. The exception being when enddate is less than 24 hours after startdate. In that case, a single day is returned.

See Also

monitor_filterDatetime

monitor_filterMeta

Examples

library(AirMonitor)

Camp_Fire %>%
  monitor_timeRange()

# Day boundaries returned in "UTC"
Camp_Fire %>%
  monitor_filterDate(
    "2018-11-15",
    "2018-11-22",
    timezone = "America/Los_Angeles"
  ) %>%
  monitor_timeRange()

# Day boundaries returned in "America/Los_Angeles"
Camp_Fire %>%
  monitor_filterDatetime(
    "20181115",
    "20181122",
    timezone = "America/Los_Angeles"
  ) %>%
  monitor_timeRange(
    timezone = "America/Los_Angeles"
  )

Description

Subsets a mts_monitor object by datetime. This function allows for sub-day filtering as opposed to monitor_filterDate() which always filters to day-boundaries.

Datetimes can be anything that is understood by MazamaCoreUtils::parseDatetime(). For non-POSIXct values, the recommended format is "YYYY-mm-dd HH:MM:SS".

If either startdate or enddate is not provided, the start/end of the mts_monitor time axis will be used.

Timezone determination precedence assumes that if you are passing in POSIXct values then you know what you are doing.

1. get timezone from startdate if it is POSIXct

2. use passed in timezone

3. get timezone from mts_monitor

Usage

monitor_filterDatetime(
  monitor = NULL,
  startdate = NULL,
  enddate = NULL,
  timezone = NULL,
  unit = "sec",
  ceilingStart = FALSE,
  ceilingEnd = FALSE
)

Arguments

Value

A subset of the given mts_monitor object. (A list with meta and data dataframes.)

See Also

monitor_filterDate

monitor_filterMeta

Examples

library(AirMonitor)

Camp_Fire %>%
  monitor_timeRange()

# Reduced time range returned in "UTC"
Camp_Fire %>%
  monitor_filterDatetime(
    "2018-11-15 02:00:00",
    "2018-11-22 06:00:00",
    timezone = "America/Los_Angeles"
  ) %>%
  monitor_timeRange()

# Reduced time range returned in "America/Los_Angeles"
Camp_Fire %>%
  monitor_filterDatetime(
    "2018111502",
    "2018112206",
    timezone = "America/Los_Angeles"
  ) %>%
  monitor_timeRange(
    timezone = "America/Los_Angeles"
  )

Description

A generalized metadata filter for mts_monitor objects to choose cases where conditions are true. Multiple conditions are combined with & or separated by a comma. Only rows where the condition evaluates to TRUE are kept. Rows of monitor$meta where the condition evaluates to NA are dropped. Associated olumns of monitor$data are also dropped for internal consistency in the returned mts_monitor object.

monitor_filter() is an alias for monitor_filterMeta().

Usage

monitor_filterMeta(monitor, ...)

monitor_filter(monitor, ...)

Arguments

Value

A subset of the incoming mts_monitor. (A list with meta and data dataframes.)

Note

Filtering is done on variables in monitor$meta.

See Also

monitor_filterDate

monitor_filterDatetime

Examples

library(AirMonitor)

# Filter based on countyName field
Camp_Fire %>%
  monitor_filter(countyName == "Alameda") %>%
  monitor_timeseriesPlot(main = "All Alameda County Monitors")

# Filter combining two fields
Camp_Fire %>%
  monitor_filter(latitude > 39.5, longitude > -121.5) %>%
  monitor_pull("locationName")

# Filter using string matching
Camp_Fire %>%
  monitor_filter(stringr::str_detect(locationName, "^San")) %>%
  monitor_pull("locationName")

Description

A PWFSLSmoke package ws_monitor object is enhanced and modified so that it becomes a valid mts_monitor object. This is a lossless operation and can be reversed with monitor_toPWFSLSmoke().

Usage

monitor_fromPWFSLSmoke(ws_monitor = NULL)

Arguments

Value

A mts_monitor object.

Description

This function augments monitor$meta with summary information derived from monitor$data reflecting recent measurements.

Usage

monitor_getCurrentStatus(
  monitor,
  enddate = NULL,
  minHours = 18,
  dayBoundary = c("clock", "LST")
)

Arguments

Value

The monitor$meta table augmented with current status information for each time series.

"Last" and "Previous"

The goal of this function is to provide useful information about what happened recently with each time series in the provided mts_monitor object. Devices don't always consistently report data, however, and it is not alwlays useful to have NA's reported when there is recent valid data at earlier times. To address this, monitor_getCurrentStatus() uses last and previous valid times. These are the time when a monitor most recently reported data, and the most recent time of valid data before that, respectively. By reporting on these times, this function ensures that valid data is returned and provides information on how outdated this information is. This information can be used in maps to show AQI colored dots when data is only a few hours old but gray dots when data is older than some threshold.

Calculating latency

According to https://docs.airnowapi.org/docs/HourlyDataFactSheet.pdf a datum assigned to 2pm represents the average of data between 2pm and 3pm. So, if we check at 3:15pm and see that we have a value for 2pm but not 3pm then the data are completely up-to-date with zero latency.

monitor_getCurrentStatus() defines latency as the difference between a time index and the next most recent time index associated with a valid value. If there is no more recent time index, then the difference is measured to the given enddate parameter. Because mts_monitor objects are defined on an hourly axis, these differences have units of hours.

For example, if the recorded values for a monitor are [16.2, 15.8, 16.4, NA, 14.0, 12.5, NA, NA, 13.3, NA], then the last valid value is 13.3 with an index is 9, and the previous valid value is 12.4 with an index of 6. The last latency is then 1 (hour before the end), and the previous latency is 3 (hours before the last valid value).

Summary data

The table created by monitor_getCurrentStatus() includes per-time series summary information calculated from monitor$data. The additional data fields added to monitor$meta are listed below:

currentStatus_processingTime

Time at which this function was run

currentStatus_enddate

Time relative to which "currency" is calculated

last_validIndex

Row index of the last valid mesurement in monitor$data

previous_validIndex

Row index of the previous valid measurement in monitor$data

last_validTime

UTC time associated with last_validIndex

previous_validTime

UTC time associated with previous_validIndex

last_latency

Hours between last_validTime and endtime

previous_latency

Hours between previous_validTime andlast_validTime

last_validLocalTimestamp

Local time representation of last_validTime

previous_validLocalTimestamp

Local time representation of previous_validTime

last_PM2.5

Last valid PM2.5 measurement

previous_PM2.5

Previous valid PM2.5 measurement

last_nowcast

Last valid PM2.5 NowCast value

previous_nowcast

Previous valid PM2.5 NowCast value

yesterday_PM2.5_avg

Daily average PM2.5 for the day prior to enddate

Examples

# Fail gracefully if any resources are not available
try({

library(AirMonitor)

monitor <- airnow_loadLatest()
# TODO:  Needed before rebuilding of v2 database with fullAQSID
monitor$meta$fullAQSID <- paste0("840", monitor$meta$AQSID)

currentStatus <-
  monitor %>%
  monitor_filter(stateCode == "WA") %>%
  monitor_getCurrentStatus()

}, silent = FALSE)

Description

These functions are convenient wrappers for extracting the dataframes that comprise a mts_monitor object. These functions are designed to be useful when manipulating data in a pipeline using %>%.

Below is a table showing equivalent operations for each function.

Usage

monitor_getData(monitor)

monitor_getMeta(monitor)

Arguments

Value

A dataframe from the given mts_monitor object.

Description

This function returns the distances (meters) between monitor locations and a location of interest. These distances can be used to create a mask identifying monitors within a certain radius of the location of interest.

Usage

monitor_getDistance(
  monitor = NULL,
  longitude = NULL,
  latitude = NULL,
  measure = c("geodesic", "haversine", "vincenty", "cheap")
)

Arguments

Value

Named vector of distances (meters) with each distance identified by deviceDeploymentID.

Note

The measure "cheap" may be used to speed things up depending on the spatial scale being considered. Distances calculated with measure = "cheap" will vary by a few meters compared with those calculated using measure = "geodesic".

Examples

library(AirMonitor)

# Walla Walla
longitude <- -118.3302
latitude <- 46.065

distance <- monitor_getDistance(NW_Megafires, longitude, latitude)
closestIndex <- which(distance == min(distance))

# Distance in meters
distance[closestIndex]

# Monitor core metadata
str(NW_Megafires$meta[closestIndex, AirMonitor::coreMetadataNames])

Description

This function returns true under the following conditions:

• no time series: ncol(monitor$data) == 1

• no time series records: nrow(monitor$data) == 0

• all timeseries values are NA

This makes for more readable code in functions that need to test for this.

Usage

monitor_isEmpty(monitor)

Arguments

Value

Invisibly returns TRUE if no data exist in mts_monitor, FALSE otherwise.

Description

The mts_monitor is checked for the presence of core meta and data columns.

Core meta columns include: (TODO: complete this list)

• deviceDeploymentID – unique identifier (see MazmaLocationUtils)

• deviceID – device identifier

• locationID – location identifier (see MazmaLocationUtils)

• locationName – English language name

• longitude – decimal degrees E

• latitude – decimal degrees N

• elevation – elevation of station in m

• countryCode – ISO 3166-1 alpha-2

• stateCode – ISO 3166-2 alpha-2

• timezone – Olson time zone

Core data columns include:

• datetime – measurement time (UTC)

Usage

monitor_isValid(monitor = NULL, verbose = FALSE)

Arguments

Value

Invisibly returns TRUE if mts_monitor has the correct structure, FALSE otherwise.

Description

This function creates interactive maps that will be displayed in RStudio's 'Viewer' tab. The slice argument is used to collapse a mts_monitor timeseries into a single value. If slice is an integer, that row index will be selected from the monitor$data dataframe. If slice is a function (unquoted), that function will be applied to the timeseries with the argument na.rm=TRUE (e.g. max(..., na.rm=TRUE)).

If slice is a user defined function, it will be used with argument na.rm=TRUE to collapse the time dimension. Thus, user defined functions must accept na.rm as an argument.

Usage

monitor_leaflet(
  monitor,
  slice = "max",
  radius = 10,
  opacity = 0.7,
  maptype = "terrain",
  extraVars = NULL,
  jitter = 5e-04,
  NAAQS = c("PM2.5_2024", "PM2.5"),
  ...
)

Arguments

Details

The maptype argument is mapped onto leaflet "ProviderTile" names. Current map types include:

"roadmap"

– "OpenStreetMap"

"satellite"

– "Esri.WorldImagery"

"terrain"

– "Esri.WorldTopoMap"

"toner"

– "Stamen.Toner"

If a character string not listed above is provided, it will be used as the underlying map tile if available. See https://leaflet-extras.github.io/leaflet-providers/ for a list of "provider tiles" to use as the background map.

Value

Invisibly returns a leaflet map of class "leaflet".

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Examples

## Not run: 
library(AirMonitor)
# Fail gracefully if any resources are not available
try({

# Maximum AQI category at each site
monitor_loadLatest() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet()

# Mean AQI category at each site
monitor_loadLatest() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet(
    slice = "mean"
  )

# Mean AQI category at each site using the updated NAAQS
monitor_loadLatest() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet(
    slice = "mean",
    NAAQS = "PM2.5_2024"
  )

}, silent = FALSE)

## End(Not run)

Description

Loads monitoring data for a given time range. Data from AirNow, AIRSIS and WRCC are combined into a single mts_monitor object.

Archival datasets are combined with 'daily' and 'latest' datasets as needed to satisfy the requested date range.

Usage

monitor_load(
  startdate = NULL,
  enddate = NULL,
  timezone = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  epaPreference = c("airnow", "epa_aqs")
)

Arguments

Value

A mts_monitor object with PM2.5 monitoring data. (A list with meta and data dataframes.)

See Also

monitor_loadAnnual

monitor_loadDaily

monitor_loadLatest

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

wa <-
  monitor_load(20210601, 20211001) %>%
  monitor_filter(stateCode == "WA")

monitor_timeseriesPlot(wa)

}, silent = FALSE)

## End(Not run)

Description

Combine annual data from AirNow, AIRSIS and WRCC:

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

Current year files loaded by this function are updated once per week.

For the most recent data in the last 10 days, use monitor_loadLatest().

For daily updates covering the most recent 45 days, use monitor_loadDaily().

For data extended more than 45 days into the past, use monitor_load().

Usage

monitor_loadAnnual(
  year = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  epaPreference = c("airnow", "epa_aqs")
)

Arguments

Value

A mts_monitor object with PM2.5 monitoring data. (A list with meta and data dataframes.)

Note

This function guarantees that only a single time series will be associated with each locationID using the following logic:

1. AirNow data takes precedence over data from AIRSIS or WRCC

2. more recent data takes precedence over older data

This relevant mostly for "temporary" monitors which may be replaced after they are initially deployed. If you want access to all device deployments associated with a specific locationID, you can use the provider specific functions: airnow_loadAnnual, airsis_loadAnnual and wrcc_loadAnnual

See Also

monitor_loadDaily

monitor_loadLatest

Examples

## Not run: 
library(AirMonitor)
# Fail gracefully if any resources are not available
try({

monitor_loadAnnual() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Combine daily data from AirNow, AIRSIS and WRCC:

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated once per day and contain data for the previous 45 days.

For the most recent data in the last 10 days, use monitor_loadLatest().

For data extended more than 45 days into the past, use monitor_load().

Usage

monitor_loadDaily(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore")
)

Arguments

Value

A mts_monitor object with PM2.5 monitoring data. (A list with meta and data dataframes.)

Note

This function guarantees that only a single time series will be associated with each locationID using the following logic:

1. AirNow data takes precedence over data from AIRSIS or WRCC

2. more recent data takes precedence over older data

This relevant mostly for "temporary" monitors which may be replaced after they are initially deployed. If you want access to all device deployments associated with a specific locationID, you can use the provider specific functions: airnow_loadDaily, airsis_loadDaily and wrcc_loadDaily

See Also

monitor_loadAnnual

monitor_loadLatest

Examples

## Not run: 
library(AirMonitor)
# Fail gracefully if any resources are not available
try({

monitor_loadDaily() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Combine recent data from AirNow, AIRSIS and WRCC:

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated multiple times an hour and contain data for the previous 10 days.

For daily updates covering the most recent 45 days, use monitor_loadDaily().

For data extended more than 45 days into the past, use monitor_load().

Usage

monitor_loadLatest(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore")
)

Arguments

Value

A mts_monitor object with PM2.5 monitoring data. (A list with meta and data dataframes.)

Note

This function guarantees that only a single time series will be associated with each locationID using the following logic:

1. AirNow data takes precedence over data from AIRSIS or WRCC

2. more recent data takes precedence over older data

This relevant mostly for "temporary" monitors which may be replaced after they are initially deployed. If you want access to all device deployments associated with a specific locationID, you can use the provider specific functions: airnow_loadLatest, airsis_loadLatest and wrcc_loadLatest

See Also

monitor_loadAnnual

monitor_loadDaily

Examples

## Not run: 
library(AirMonitor)
# Fail gracefully if any resources are not available
try({

monitor_loadLatest() %>%
  monitor_filter(stateCode %in% CONUS) %>%
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

This function works similarly to dplyr::mutate() and applies FUN to each time series found in monitor$data. FUN must be a function that accepts a numeric vector as its first argument and returns a vector of the same length.

Usage

monitor_mutate(monitor = NULL, FUN = NULL, ...)

Arguments

Value

A modified mts_monitor object. (A list with meta and data dataframes.)

Examples

library(AirMonitor)

Carmel_Valley %>%
  monitor_filterDatetime(2016080207, 2016080212) %>%
  monitor_toCSV(includeMeta = FALSE) %>%
  cat()

Carmel_Valley %>%
  monitor_filterDatetime(2016080207, 2016080212) %>%
  monitor_mutate(function(x) { return(x / 2) }) %>%
  monitor_toCSV(includeMeta = FALSE) %>%
  cat()

Description

A NowCast algorithm is applied to the data in in the monitor object. The version argument specifies the minimum weight factor and number of hours to be used in the calculation.

Available versions include:

1. pm: hours = 12, weight = 0.5

2. pmAsian: hours = 3, weight = 0.1

3. ozone: hours = 8, weight = NA

The default, version = "pm", is appropriate for typical usage.

Usage

monitor_nowcast(
  monitor,
  version = c("pm", "pmAsian", "ozone"),
  includeShortTerm = FALSE
)

Arguments

Details

This function calculates each hour's NowCast value based on the value for the given hour and the previous N-1 hours, where N is the number of hours appropriate for the version argument. For example, if version = "pm", the NowCast value for Hour 12 is based on the data from hours 1-12.

The function returns values when at least two of the previous three hours have data. NA's are returned for hours where this condition is not met.

By default, the funtion will not return a valid value until the Nth hour. If includeShortTerm = TRUE, the function will return a valid value after only the 2nd hour (provided, of course, that both hours are valid).

Calculated Nowcast values are truncated to the nearest .1 ug/m3 for 'pm' and nearest .001 ppm for 'ozone' regardless of the precision of the data in the incoming mts_monitor object.

Value

A modified mts_monitor object. (A list with meta and data dataframes.)

References

https://en.wikipedia.org/wiki/Nowcast_(Air_Quality_Index)

AQI Technical Assistance Document

Description

This function acts similarly to pull working on monitor$meta or monitor$data. Data are returned as a simple array. Data are pulled from whichever dataframe contains var.

Usage

monitor_pull(monitor = NULL, var = NULL)

Arguments

Value

An array of values.

Examples

library(AirMonitor)

# Metadata
Camp_Fire %>%
  monitor_pull("deploymentType") %>%
  table()

# Data for a specific ID
Camp_Fire %>%
  monitor_dailyStatistic(mean) %>%
  monitor_pull("6bbab08e3786ef66_840060450006") %>%
  round(0)

# Associated dates
Camp_Fire %>%
  monitor_dailyStatistic(mean) %>%
  monitor_pull("datetime")

Description

Use an R expression to identify values for replacement.

The R expression given in filter is used to identify elements in monitor$data that should be replaced. The datetime column will be retained unmodified. Typical usage would include

1. replacing negative values with 0

2. replacing unreasonably high values with NA

Expressions should use data for the left hand side of the comparison.

Usage

monitor_replaceValues(monitor = NULL, filter = NULL, value = NULL)

Arguments

Value

A modified mts_monitor object. (A list with meta and data dataframes.)

Examples

library(AirMonitor)

wa <- monitor_filterMeta(NW_Megafires, stateCode == 'WA')
any(wa$data < 5, na.rm = TRUE)

wa_zero <- monitor_replaceValues(wa, data < 5, 5)
any(wa_zero$data < 5, na.rm = TRUE)

Description

This function acts similarly to dplyr::select() working on monitor$data. The returned mts_monitor object will contain only those time series identified by id in the order specified.

This can be helpful when using faceted plot functions based on ggplot such as those found in the AirMonitorPlots package.

Usage

monitor_select(monitor, id)

monitor_reorder(monitor, id)

Arguments

Value

A reordered (subset) of the incoming mts_monitor object. (A list with meta and data dataframes.)

See Also

monitor_filterMeta

Description

Subsetting of monitor acts similarly to tidyselect::where() working on monitor$data. The returned mts_monitor object will contain only those time series where FUN applied to the time series data returns TRUE.

Usage

monitor_selectWhere(monitor, FUN)

Arguments

Value

A subset of the incoming mts_monitor object. (A list with meta and data dataframes.)

See Also

monitor_select

Examples

library(AirMonitor)

# Show all Camp_Fire locations
Camp_Fire$meta$locationName

# Use package US_AQI data for HAZARDOUS
name <- US_AQI$names_eng[6]
threshold <- US_AQI$breaks_PM2.5[6]

# Find HAZARDOUS locations
worst_sites <-
  Camp_Fire %>%
  monitor_selectWhere(
    function(x) { any(x >= threshold, na.rm = TRUE) }
  )

# Show the worst locations
worst_sites$meta$locationName

Description

Extends or contracts the time range of an mts_monitor object by adding/removing time steps at the start and end and filling any new time steps with missing values. The resulting time axis is guaranteed to be a regular, hourly axis with no gaps using the same timezone as the incoming mts_monitor object. This is useful when you want to place separate mts_monitor objects on the same time axis for plotting.

If either startdate or enddate is missing, the start or end of the timeseries in monitor will be used.

Usage

monitor_setTimeAxis(
  monitor = NULL,
  startdate = NULL,
  enddate = NULL,
  timezone = NULL
)

Arguments

Value

The incoming mts_monitor time series object defined on a new time axis. (A list with meta and data dataframes.)

Note

If startdate or enddate is a POSIXct value, then timezone will be set to the timezone associated with startdate or enddate. In this common case, you don't need to specify timezone explicitly.

If neither startdate nor enddate is a POSIXct value AND no timezone is supplied, the timezone will be inferred from the most common timezone found in monitor.

Examples

library(AirMonitor)

# Default range
Carmel_Valley %>%
  monitor_timeRange()

# One-sided extend with user specified timezone
Carmel_Valley %>%
  monitor_setTimeAxis(enddate = 20160820, timezone = "UTC") %>%
  monitor_timeRange()

# Two-sided extend with user specified timezone
Carmel_Valley %>%
  monitor_setTimeAxis(20190720, 20190820, timezone = "UTC") %>%
  monitor_timeRange()

# Two-sided extend without timezone (uses monitor$meta$timezone)
Carmel_Valley %>%
  monitor_setTimeAxis(20190720, 20190820) %>%
  monitor_timeRange()

Description

An mts_monitor object is reduced so as to contain only the first or last n timeseries. These functions work similarly to dplyr::slice_head and dplyr::slice_tail but apply to both dataframes in the mts_monitor object.

This is primarily useful when the mts_monitor object has been ordered by a previous call to monitor_arrange or by some other means.

monitor_slice_head() selects the first and monitor_slice_tail() the last timeseries in the object.

Usage

monitor_slice_head(monitor, n = 5)

monitor_slice_tail(monitor, n = 5)

Arguments

Value

A subset of the incoming mts_monitor time series object. (A list with meta and data dataframes.)

Examples

library(AirMonitor)

# Find lowest elevation sites
Camp_Fire %>%
  monitor_filter(!is.na(elevation)) %>%
  monitor_arrange(elevation) %>%
  monitor_slice_head(n = 5) %>%
  monitor_getMeta() %>%
  dplyr::select(elevation, locationName)

# Find highest elevation sites
Camp_Fire %>%
  monitor_filterMeta(!is.na(elevation)) %>%
  monitor_arrange(elevation) %>%
  monitor_slice_tail(n = 5) %>%
  monitor_getMeta() %>%
  dplyr::select(elevation, locationName)

Description

Calculate the local time for a monitor, as well as sunrise, sunset and solar noon times, and create several temporal masks.

The returned dataframe will have as many rows as the length of the incoming UTC time vector and will contain the following columns:

• localStdTime_UTC – UTC representation of local standard time

• daylightSavings – logical mask = TRUE if daylight savings is in effect

• localTime – local clock time

• sunrise – time of sunrise on each localTime day

• sunset – time of sunset on each localTime day

• solarnoon – time of solar noon on each localTime day

• day – logical mask = TRUE between sunrise and sunset

• morning – logical mask = TRUE between sunrise and solarnoon

• afternoon – logical mask = TRUE between solarnoon and sunset

• night – logical mask = opposite of day

Usage

monitor_timeInfo(monitor = NULL, id = NULL)

Arguments

Details

While the lubridate package makes it easy to work in local timezones, there is no easy way in R to work in "Local Standard Time" (LST) (i.e. never shifting to daylight savings) as is often required when working with air quality data. US EPA regulations mandate that daily averages be calculated based on LST.

The localStdTime_UTC is primarily for use internally and provides an important tool for creating LST daily averages and LST axis labeling.

Value

A dataframe with times and masks.

Examples

library(AirMonitor)

carmel <-
  Carmel_Valley %>%
  monitor_filterDate(20160801, 20160810)

# Create timeInfo object for this monitor
ti <- monitor_timeInfo(carmel)

# Subset the data based on day/night masks
data_day <- carmel$data[ti$day,]
data_night <- carmel$data[ti$night,]

# Build two monitor objects
carmel_day <- list(meta = carmel$meta, data = data_day)
carmel_night <- list(meta = carmel$meta, data = data_night)

# Plot them
carmel_day %>%
  monitor_timeseriesPlot(
    pch = 8,
    col = "goldenrod",
    shadedNight = TRUE
  )

carmel_night %>%
  monitor_timeseriesPlot(
    add = TRUE,
    pch = 16,
    col = "darkblue"
  )

Description

This function is a wrapper for range(monitor$data$datetime) and is convenient for use in data pipelines.

Dates will be returned in the timezone associated with monitor$data$datetime which is typically "UTC" unless timezone is specified.

Usage

monitor_timeRange(monitor = NULL, timezone = NULL)

Arguments

Value

A vector containing the minimum and maximum times of a mts_monitor object.

Examples

Carmel_Valley %>%
  monitor_timeRange(timezone = "America/Los_Angeles")

Description

Creates a time series plot of data from a mts_monitor object. By default, points are plotted as semi-transparent squares. All data values are plotted from all monitors found in the mts_monitor object.

Reasonable defaults are chosen for annotations and plot characteristics. Users can override any defaults by passing in parameters accepted by graphics::plot.default.

Usage

monitor_timeseriesPlot(
  monitor = NULL,
  id = NULL,
  shadedNight = FALSE,
  add = FALSE,
  addAQI = FALSE,
  palette = c("EPA", "subdued", "deuteranopia"),
  opacity = NULL,
  NAAQS = c("PM2.5_2024", "PM2.5"),
  ...
)

Arguments

Value

No return value. This function is called to draw an air quality time series plot on the active graphics device.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Examples

library(AirMonitor)

# Single monitor
Carmel_Valley %>%
  monitor_timeseriesPlot()

# Multiple monitors
Camp_Fire %>%
  monitor_filter(countyName == "Alameda") %>%
  monitor_timeseriesPlot(main = "All Alameda County Monitors")

# Standard extras
Carmel_Valley %>%
  monitor_timeseriesPlot(
    shadedNight = TRUE,
    addAQI = TRUE
  )
addAQILegend()

# Standard extras using the updated PM NAAQS
Carmel_Valley %>%
  monitor_timeseriesPlot(
    shadedNight = TRUE,
    addAQI = TRUE,
    NAAQS = "PM2.5_2024"
  )
addAQILegend(NAAQS = "PM2.5_2024")

# Fancy plot based on pm2.5 values
pm2.5 <- Carmel_Valley$data[,2]
Carmel_Valley %>%
  monitor_timeseriesPlot(
    shadedNight = TRUE,
    pch = 16,
    cex = pmax(pm2.5 / 100, 0.5),
    col = aqiColors(pm2.5),
    opacity = 0.8
  )
addAQILegend(pch = 16, cex = 0.6, bg = "white")

Description

Creates a table of AQI category vs monitoring site with a count of the number of times each AQI category was experienced at each site. The count will be a count of hours or days depending on averaging period of the incoming monitor object.

When siteIdentifier is used, the identifiers must be in the same order as monitor$meta.

Usage

monitor_toAQCTable(
  monitor,
  NAAQS = c("PM2.5_2024", "PM2.5"),
  siteIdentifier = "locationName"
)

Arguments

Value

Table of AQI category counts.

Note

On February 7, 2024, EPA strengthened the National Ambient Air Quality Standards for Particulate Matter (PM NAAQS) to protect millions of Americans from harmful and costly health impacts, such as heart attacks and premature death. Particle or soot pollution is one of the most dangerous forms of air pollution, and an extensive body of science links it to a range of serious and sometimes deadly illnesses. EPA is setting the level of the primary (health-based) annual PM2.5 standard at 9.0 micrograms per cubic meter to provide increased public health protection, consistent with the available health science. See PM NAAQS update.

Examples

library(AirMonitor)

# Lane County, Oregon AQSIDs all begin with "41039"
LaneCounty <-
  NW_Megafires %>%
  monitor_filter(stringr::str_detect(AQSID, '^41039')) %>%
  monitor_filterDate(20150801, 20150901)

# Count of hours each site spent in each AQ category in August
LaneCounty %>%
  monitor_toAQCTable()

# Count of days each site spent in each AQ
LaneCounty %>%
  monitor_dailyStatistic(mean) %>%
  monitor_toAQCTable()

# Count of days each site spent in each AQ (simplified names)
siteNames <- c(
  "Eugene 1", "Eugene 2", "Eugene 3",
  "Springfield", "Oakridge", "Cottage Grove"
)
LaneCounty %>%
  monitor_dailyStatistic(mean) %>%
  monitor_toAQCTable(siteIdentifier = siteNames)

# Count of days at each AQ level with the new, 2024 NAAQS
LaneCounty %>%
  monitor_dailyStatistic(mean) %>%
  monitor_toAQCTable(NAAQS = "PM2.5_2024")

Description

Converts the contents of the monitor argument to CSV. By default, the output is a text string with "human readable" CSV that includes both meta and data. When saved as a file, this format is useful for point-and-click spreadsheet users who want to have everything on a single sheet.

To obtain a machine parseable CSV string for just the data, you can use includeMeta = FALSE. To obtain machine parseable metadata, use includeData = FALSE.

Usage

monitor_toCSV(monitor, includeMeta = TRUE, includeData = TRUE)

Arguments

Value

CSV formatted text.

Examples

library(AirMonitor)

monitor <-
  Carmel_Valley %>%
  monitor_filterDate(20160802, 20160803)

monitor_toCSV(monitor) %>% cat()
monitor_toCSV(monitor, includeData = FALSE) %>% cat()
monitor_toCSV(monitor, includeMeta = FALSE) %>% cat()

Description

A mts_monitor object is modified so that it becomes a PWFSLSmoke package ws_monitor object. While some information will be lost, this operation can be reversed with monitor_fromPWFSLSmoke().

Usage

monitor_toPWFSLSmoke(monitor = NULL)

Arguments

Value

A PWFSLSmoke ws_monitor object. (A list with meta and data dataframes.)

Note

In order to avoid duplicated monitorID values in the returned ws_monitor object, the full deviceDeploymentID will be used as the monitorID.

Description

Trims the date range of a mts_monitor object to local time date boundaries which are within the range of data. This has the effect of removing partial-day data records at the start and end of the timeseries and is useful when calculating full-day statistics.

By default, multi-day periods of all-missing data at the beginning and end of the timeseries are removed before trimming to date boundaries. If trimEmptyDays = FALSE all records are retained except for partial days beyond the first and after the last date boundary.

Day boundaries are calculated using the specified timezone or, if NULL, from monitor$meta$timezone.

Usage

monitor_trimDate(monitor = NULL, timezone = NULL, trimEmptyDays = TRUE)

Arguments

Value

A subset of the given mts_monitor object. (A list with meta and data dataframes.)

Examples

library(AirMonitor)

# Non-day boundaries
monitor <-
  Camp_Fire %>%
  monitor_filterDatetime(
    "2018111502",
    "2018112206",
    timezone = "America/Los_Angeles"
  )

monitor %>%
  monitor_timeRange(timezone = "America/Los_Angeles")

# Trim to full days only
monitor %>%
  monitor_trimDate() %>%
  monitor_timeRange(timezone = "America/Los_Angeles")

Description

The NW_Megafires dataset provides a quickly loadable version of a mts_monitor object for practicing and code examples.

Usage

NW_Megafires

Format

A mts_monitor object with 1080 rows and 143 columns of data.

Details

In the summer of 2015, Washington state had several catastrophic wildfires that led to many days of heavy smoke in eastern Washington, Oregon and northern Idaho. The NW_Megafires dataset contains monitoring data for the Pacific Northwest from July 24 through September 06, 2015.

This dataset was generated on 2022-10-28 by running:

library(AirMonitor)

NW_Megafires <-
  monitor_loadAnnual(2015, epaPreference = "epa_aqs") 
  monitor_filterMeta(stateCode 
  monitor_filterDate(20150724, 20150907, timezone = "America/Los_Angeles") 
  monitor_dropEmpty()

save(NW_Megafires, file = "data/NW_Megafires.rda")

Description

Character string identifiers of recognized pollutant names.

Usage

pollutantNames

Format

A vector of character strings

Details

pollutantNames

Examples

print(coreMetadataNames, width = 80)

Description

Invalidates values within a timeseries that appear "sticky". Some temporary monitoring data has stretches of consecutive values, sometimes well outside the range of reasonable. This QC function identifies these "sticky" stretches and returns the original timeseries data with "sticky" stretches replaced with NA.

Usage

QC_invalidateConsecutiveSuspectValues(
  x = NULL,
  suspectValues = c(0:10 * 1000, NA),
  consecutiveCount = 2
)

Arguments

Value

Returns x with some values potentially replaced with NA.

Description

State codes for the 50 states +DC +PR (Puerto Rico).

US_52 <- c( "AK","AL","AZ","AR","CA","CO","CT","DE","FL","GA", "HI","ID","IL","IN","IA","KS","KY","LA","ME","MD", "MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ", "NM","NY","NC","ND","OH","OK","OR","PA","RI","SC", "SD","TN","TX","UT","VT","VA","WA","WV","WI","WY", "DC","PR" )

Usage

US_52

Format

A vector with 52 elements

Details

US state codes

Description

Official, US EPA AQI levels, names, colors and action text are provided in a list for easy coloring and labeling.

Usage

US_AQI

Format

A list with named elements

Details

AQI breaks and associated names and colors

Breaks

Breakpoints are given in units reported for each parameter and include:

• breaks_AQI

• breaks_CO

• breaks_NO2

• breaks_OZONE_1hr

• breaks_OZONE_8hr

• breaks_PM2.5

• breaks_PM10

Colors

Several different color palettes are provided:

• colors_EPA – official EPA AQI colors

• colors_subdued – subdued colors fo use with leaflet maps

• colors_deuteranopia – color vision impaired colors

Names

Names of AQI categories are provided in several languages identified by the ISO 639-2 alpha-3 code:

• names_eng

• names_spa

Actions

Text for "actions to protect yourself" are provided for each category in several languages identified by the ISO 639-2 alpha-3 code:

• actions_eng

• actions_spa

Currently supported languages include English (eng) and Spanish (spa).

AQI breaks and colors are defined at https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf and are given in units appropriate for each pollutant.

Note

The low end of each break category is used as the breakpoint.

Examples

print(US_AQI$breaks_AQI)
print(US_AQI$colors_EPA)
print(US_AQI$names_eng)
print(US_AQI$names_spa)

Description

Loads pre-generated .rda files containing annual WRCC data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

Current year files loaded by this function are updated once per week.

For the most recent data in the last 10 days, use wrcc_loadLatest().

For daily updates covering the most recent 45 days, use wrcc_loadDaily().

Usage

wrcc_loadAnnual(
  year = NULL,
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with WRCC data. (A list with meta and data dataframes.)

Note

Some older WRCC timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

wrcc_loadDaily

wrcc_loadLatest

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

# See https://en.wikipedia.org/wiki/Snake_River_Complex_Fire

# WRCC monitors during the Snake River Complex Fire
wrcc_loadAnnual(2021) \
  monitor_filter(stateCode \
  monitor_filterDate(20210707, 20210820, timezone = "America/Denver") \
  monitor_timeseriesPlot(
    ylim = c(0, 300),
    xpd = NA,
    addAQI = TRUE,
    main = "WRCC monitors during Snake River Complex Fire"
  )

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing daily WRCC data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated once per day and contain data for the previous 45 days.

For the most recent data in the last 10 days, use wrcc_loadLatest().

For data extended more than 45 days into the past, use wrcc_loadAnnual().

Usage

wrcc_loadDaily(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with WRCC data. (A list with meta and data dataframes.)

Note

Some older WRCC timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

wrcc_loadAnnual

wrcc_loadDaily

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

wrcc_loadDaily() \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)

Description

Loads pre-generated .rda files containing the most recent WRCC data.

If archiveDataDir is defined, data will be loaded from this local archive. Otherwise, data will be loaded from the monitoring data repository maintained by the USFS AirFire team.

The files loaded by this function are updated multiple times an hour and contain data for the previous 10 days.

For daily updates covering the most recent 45 days, use wrcc_loadDaily().

For data extended more than 45 days into the past, use wrcc_loadAnnual().

Usage

wrcc_loadLatest(
  archiveBaseUrl = paste0("https://airfire-data-exports.s3.us-west-2.amazonaws.com/",
    "monitoring/v2"),
  archiveBaseDir = NULL,
  QC_negativeValues = c("zero", "na", "ignore"),
  QC_removeSuspectData = TRUE
)

Arguments

Value

A mts_monitor object with WRCC data. (A list with meta and data dataframes.)

Note

Some older WRCC timeseries contain only values of 0, 1000, 2000, 3000, ... ug/m3. Data from these deployments pass instrument-level QC checks but these timeseries generally do not represent valid data and should be removed. With QC_removeSuspectData = TRUE (the default), data is checked and periods reporting only values of 0:10 * 1000 ug/m3 are invalidated.

Only those personally familiar with the individual instrument deployments should work with the "suspect" data.

See Also

wrcc_loadAnnual

wrcc_loadDaily

Examples

## Not run: 
library(AirMonitor)

# Fail gracefully if any resources are not available
try({

wrcc_loadLatest() \
  monitor_leaflet()

}, silent = FALSE)

## End(Not run)