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ASRC Atmospheric Chemistry Data Processing

R build status Codecov test coverage DOI

The atmoschem.process R package processes atmospheric chemistry data from ASRC sites in New York State. It provides tools to generate reports and processed datasets from the ASRC’s atmospheric chemistry data, and tools to visualize the data.

Table of contents

Overview

The atmospheric chemistry group at the ASRC, run by Dr. Jim Schwab, collects data from instruments at 4 sites across the state of New York: Whiteface Mountain (summit and base), Pinnacle State Park, and Queens College. We maintain a variety of instruments measuring ozone and ozone precursors, airborne particulate matter, sulfur dioxide, and meteorology.

The data undergoes a variety of adjustments and quality assurance checks before being released to the public, where it is used by government agencies and atmospheric science researchers, among others. Users can access the datasets online via our website at http://atmoschem.asrc.cestm.albany.edu/.

This software, which generates the processed datasets, synthesizes advice from atmospheric monitoring, statistical programming, and data management. In terms of data processing, we tend to follow guidelines from NARSTO (Christensen et al. 2000), and we also work regularly with EPA standards. The code is organized as an R package (Marwick, Boettiger, and Mullen 2018), and we use GitHub for project management (Bryan 2018). For data management, we tend to follow Briney (2015), and we try to make the data convenient to use (White et al. 2013).

The data collection at Whiteface Mountain is described in detail in a series of publications (Schwab, Wolfe, et al. 2016; Brandt et al. 2016; Schwab, Casson, et al. 2016), and another paper describes the measurements at Pinnacle State Park (Schwab, Spicer, and Demerjian 2009).

Installation

To install the package, run (from within R)

install.packages('remotes')
remotes::install_github('ASRCsoft/atmoschem.process')

To generate the processed dataset, additional dependencies are requried:

remotes::install_github('ASRCsoft/atmoschem.process', dependencies = TRUE)

Reproducing the routine chemistry dataset

Requirements

  • R and R package dependencies
  • GNU Make
  • 15GB of disk space

Creating the dataset

Download or clone the repository. The dataset package can be generated by running (from a terminal)

cd path/to/atmoschem.process
make routine

You will be asked for the atmoschem server’s data password (twice), which can be obtained from the atmoschem website.

Viewing the data

The R package comes with a Shiny app for viewing the processing steps. After data has been processed, it can be launched with make:

make view

Citation

The package citation can be obtained by running citation('atmoschem.process'):


To cite package 'atmoschem.process' in publications use:

  William May (2020). atmoschem.process: ASRC Atmospheric Chemistry
  Data Processing. R package version 0.5.0.
  https://github.com/ASRCsoft/atmoschem.process

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {atmoschem.process: ASRC Atmospheric Chemistry Data Processing},
    author = {William May},
    year = {2020},
    note = {R package version 0.5.0},
    url = {https://github.com/ASRCsoft/atmoschem.process},
  }

License

atmoschem.process is released under the open source MIT license.

References

Brandt, Richard E., James J. Schwab, Paul W. Casson, Utpal K. Roychowdhury, Douglas Wolfe, Kenneth L. Demerjian, Kevin L. Civerolo, Oliver V. Rattigan, and H. Dirk Felton. 2016. “Atmospheric Chemistry Measurements at Whiteface Mountain, NY: Ozone and Reactive Trace Gases.” Aerosol and Air Quality Research 16 (3): 873–84. https://doi.org/10.4209/aaqr.2015.05.0376.

Briney, Kristin. 2015. Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success. Pelagic Publishing Ltd.

Bryan, Jennifer. 2018. “Excuse Me, Do You Have a Moment to Talk About Version Control?” The American Statistician 72 (1): 20–27. https://doi.org/10.1080/00031305.2017.1399928.

Christensen, Sigurd W., Thomas A. Boden, Les A. Hook, and Meng-Dawn Cheng. 2000. “NARSTO Data Management Handbook.” NARSTO Quality Systems Science Center. https://web.archive.org/web/20030401082229/http://cdiac.esd.ornl.gov:80/programs/NARSTO/pdf/dmhb_current_version.PDF.

Marwick, Ben, Carl Boettiger, and Lincoln Mullen. 2018. “Packaging Data Analytical Work Reproducibly Using R (and Friends).” The American Statistician 72 (1): 80–88. https://doi.org/10.1080/00031305.2017.1375986.

Schwab, James J., Paul Casson, Richard Brandt, Liquat Husain, Vincent Dutkewicz, Douglas Wolfe, Kenneth L. Demerjian, et al. 2016. “Atmospheric Chemistry Measurements at Whiteface Mountain, NY: Cloud Water Chemistry, Precipitation Chemistry, and Particulate Matter.” Aerosol and Air Quality Research 16 (3): 841–54. https://doi.org/10.4209/aaqr.2015.05.0344.

Schwab, James J., John B. Spicer, and Kenneth L. Demerjian. 2009. “Ozone, Trace Gas, and Particulate Matter Measurements at a Rural Site in Southwestern New York State: 1995–2005.” Journal of the Air & Waste Management Association 59 (3): 293–309. https://doi.org/10.3155/1047-3289.59.3.293.

Schwab, James J., Douglas Wolfe, Paul Casson, Richard Brandt, Kenneth L. Demerjian, Liquat Husain, Vincent A. Dutkiewicz, Kevin L. Civerolo, and Oliver V. Rattigan. 2016. “Atmospheric Science Research at Whiteface Mountain, NY: Site Description and History.” Aerosol and Air Quality Research 16 (3): 827–40. https://doi.org/10.4209/aaqr.2015.05.0343.

White, Ethan P., Elita Baldridge, Zachary T. Brym, Kenneth J. Locey, Daniel J. McGlinn, and Sarah R. Supp. 2013. “Nine Simple Ways to Make It Easier to (Re)use Your Data.” Ideas in Ecology and Evolution 6 (2). https://doi.org/10.4033/iee.2013.6b.6.f.