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AirMonitor

Utilities for working with hourly air quality monitoring data
with a focus on small particulates (PM2.5). A compact data model is 
structured as a list with two dataframes. A 'meta' dataframe contains 
spatial and measuring device metadata associated with deployments at known 
locations. A 'data' dataframe contains a 'datetime' column followed by 
columns of measurements associated with each "device-deployment".

Background

The USFS AirFire group is focused on air quality measurements associated with
wildfire smoke and maintains both historical and real-time databases of PM2.5
monitoring data obtained from stationary monitors. This data is used in
operational displays and for retrospective analysis. Data ingest and management
of air quality “stationary time series” are both important ongoing activities.

Related Packages

The AirMonitorIngest
package is used to create data archives for the AirMonitor package and
isolates the work of meticulously cleaning, validating and harmonizing data from
various sources.

The AirMonitor package contains data access functions to easily download
harmonized data files as well as data manipulation functions that
make it easy to create "recipe style" analysis pipelines. The combination allows
analysts to work efficiently with short, readable R scripts. Interactive and
base R plotting functions allow for visual review of the data.

The AirMonitorPlots
package contains ggplot2 based plotting functions for advanced plots.

Installation

NOTE: This package has not yet been uploaded to CRAN

Install the latest version from GitHub with:

devtools::install_github('mazamascience/AirMonitor')

Data Model

The AirMonitor package uses the mts data model defined in
MazamaTimeSeries.

In this data model, each unique time series is referred to as a
"device-deployment" -- a timeseries collected by a particular device at a
specific location. Multiple device-deployments are stored in memory as a
monitor object -- an R list with two dataframes:

monitor$meta -- rows = unique device-deployments; cols = device/location metadata

monitor$data -- rows = UTC times; cols = device-deployments (plus an additional datetime column)

A key feature of this data model is the use of the deviceDeploymentID as a
"foreign key" that allows data columns to be mapped onto the associated
spatial and device metadata in a meta row. The following will always be true:

identical(names(monitor$data), c('datetime', monitor$meta$deviceDeploymentID))

Each column of monitor$data represents a timeseries associated with a particular
device-deployment while each row represents a synoptic snapshot of all
measurements made at a particular time.

In this manner, software can create both timeseries plots and maps from a single
monitor object in memory.

Note: The monitor object time axis specified in data$datetime is
guaranteed to be a regular hourly axis with no gaps.

This project is supported by the USFS AirFire group.