ACAGPM

Particulate Matter (PM2.5) is a term referring to microscopic particles, not greater than 2.5 micro-meters in diameter, suspended in air. These particles have a variety of adverse health effects on humans, given their ability to quickly enter the bloodstream via inhalation.

Research initiatives, especially those relating to climate and human health, can greatly benefit from access to PM2.5 data; this package aims to provide users with easy access to that data by aligning WUSTL's Atmospheric Composition Analysis Group Particulate Matter 2.5 raster files with 2019 state, county, and census tract shape files.

Dependencies

This package requires installation of the following packages.

Imports

• devtools

• doParallel

• dplyr

• methods

• parallel

• plyr

• raster

• rlang

• sf

• tibble

• tidycensus

• tigris

Suggests

• ggplot2

• gridExtra

• knitr

• rmarkdown

• testthat

Installation

Executing the following code will install the package, using the devtools package:

install.packages("devtools")
devtools::install_github("mitre/ACAGPM", ref = "main")

Usage and structure

The package has 2 basic functionalities, pulling PM2.5 data internally and pulling PM2.5 data externally (pull_state_ACAG, pull_county_ACAG, pull_tract_ACAG). An additional functionality allowing users to look up the GEOID of a geography is also included (lookup_GEOID). Further detail on each function and their uses is provided in the vignette.

Particulate Matter values for a geography are found using area-weighted mean calculations. Some things to note:

• PM concentrations are the PM values found in a raster file grid block.

• Weight is the area of a grid block contained in the geography, divided by the total area of the geography.

• Exact value is found by summing the PM concentrations multiplied by the corresponding weights, then dividing that sum by the sum of all weights.

• Units are micro-grams per cubic meter

Output of the basic functionalities are formed around their potential for use with census data via the GEOID field. Users will be able to take other data available through packages such as tidycensus and join with this data, creating practical analyses with ease.

Basic: Internal Data Pulls

Data can be pulled from within the package with the functions pull_state_ACAG, pull_county_ACAG, and pull_tract_ACAG. Years 2015-2018 are currently available for internal pulls. Data for Alaska and Hawaii is not available. Each function has a similar structure:

• Process user input and make sure it is acceptable using the lookup tables from the /ACAGPM/inst/extdata/input subdirectory.

• Iterate through input, pulling files from the /ACAGPM/inst/extdata/output subdirectory and combining into a dataframe.

• Return output to the user containing NAME, GEOID, and Particulate.Matter fields for geographies.

Basic: External Data Pulls

Data for years 2000-2014 can be pulled externally with the functions pull_state_ACAG, pull_county_ACAG, and pull_tract_ACAG. Additional steps must be taken before using the functions.

1. Retrieve Raster files

   • Go to the Surface PM2.5 Data Folder owned by Washington University in St. Louis' Atmospheric Composition and Analysis Group. Files will be in the format V4NA03_PM25_NA_YEAR01_YEAR12-RH35-NoNegs.asc.zip where YEAR is the desired year of data. Save the file to a directory of your choosing. Then, use your favorite file compression tool to unzip the file. There will be 2 files: V4NA03_PM25_NA_YEAR01_YEAR12-RH35-NoNegs.asc and V4NA03_PM25_NA_YEAR01_YEAR12-RH35-NoNegs.prj. The .asc file will be of use to us in this case.

2. Load Raster Object in R

   • Exectute the following code in R. This will load the raster file into R as a RasterLayer object. Make sure that file_dir is the directory which contains the PM2.5 file, and year matches the YEAR value in the filename.

library(raster)

file_dir <- "C:/Users/userName/Desktop/ACAGPM"
year <- 2000

acag <- raster::raster(
  file.path(
    file_dir, 
    paste0("V4NA03_PM25_NA_", year, "01_", year, "12-RH35-NoNegs.asc")
  ))

acag@data@names <- "Value"

With those 2 steps complete, a function of your choosing may be called. Each function has a similar structure:

• Process user input and make sure it is acceptable using the lookup tables from the /ACAGPM/inst/extdata/input subdirectory.

• Pass states containing selected input to an internal function which does most of the heavy lifting.

• Pull geography shapefiles from tigris. Turn into lists for use with lapply and parallelized equivalent, llply.

• Filter geographies to those selected in user input.

• Set crs of raster object to match that of the geographies.

• Create a cluster for parallel computation.

• Perform parallel computation of area-weighted mean particulate matter values for each geography after projecting raster file's values onto shapfile.

• Combine list into dataframe and pull GEOID, NAME, and Particulate.Matter fields.

• Return dataframe to the user.

Additional: lookup_GEOID

This function allows the user to look up the GEOID of a state, county, or tract.

• Uses dataframes saved as .RDS files in the /ACAGPM/inst/extdata/input subdirectory.

• Relatively straightforward in build. Input is split, used as a filter for the dataframe, output is pulled, and returned to the user as character string.

References

[1] Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. Link

[2] van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 2019, doi: 10.1021/acs.est.8b06392. Link

Contact

For more information about ACAGPM, please contact:

Hannah De los Santos, PhD, Principal Investigator at hdelossantos@mitre.org

Approved for Public Release; Distribution Unlimited. Public Release Case Number 22-1546. ©2021 The MITRE Corporation. ALL RIGHTS RESERVED.