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rintro.bib
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@article{collier-oxandale2022,
title = {{AirSensor} v1.0: {Enhancements} to the open-source {R} package to enable deep understanding of the long-term performance and reliability of {PurpleAir} sensors},
volume = {148},
issn = {1364-8152},
shorttitle = {{AirSensor} v1.0},
url = {https://www.sciencedirect.com/science/article/pii/S136481522100298X},
doi = {10.1016/j.envsoft.2021.105256},
abstract = {As low-cost air quality sensors become more widely utilized, more tools and methods are needed to help users access/process sensor data, identify poorly performing sensors, and analyze/visualize sensor data. Free and open-source software (FOSS) packages developed for use on FOSS data science platforms are well-suited to support this need by offering replicable and shareable tools that can be adapted to meet a user or project's specific needs. This paper describes enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 1.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor. This paper also demonstrates how these enhancements may be used to track and assess the health of air sensors in real-time or for large historical datasets. The dataset used for this analysis was collected during a multi-year project (with sensors deployed from October 2017 to October 2020) involving the distribution of approximately 400 PA-II sensors across 14 communities in southern, central, and northern California. Applying the tools in the AirSensor package revealed a dramatic variability in sensor performance, mainly driven by seasonal trends or particulate matter source type. These results also indicate that this sensor can provide useful data for at least three years with little evidence of substantial or consistent drift. Further, high agreement was observed between co-located sensors deployed at different times, indicating that it may be reasonable to compare data from old and new PA-II sensors. In addition to assessing the long-term performance and reliability of the PA-II sensor, this analysis serves as a model for how data from large sensor networks may be effectively processed, evaluated, interpreted, and communicated.},
language = {en},
urldate = {2023-06-29},
journal = {Environmental Modelling \& Software},
author = {Collier-Oxandale, Ashley and Feenstra, Brandon and Papapostolou, Vasileios and Polidori, Andrea},
month = feb,
year = {2022},
keywords = {Air quality sensors, Citizen science, Open-source R-package, Particulate matter sensors, QA/QC, Sensor networks},
pages = {105256},
file = {ScienceDirect Full Text PDF:C\:\\Users\\wiinu\\Zotero\\storage\\ABRJZGG3\\Collier-Oxandale et al. - 2022 - AirSensor v1.0 Enhancements to the open-source R .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\wiinu\\Zotero\\storage\\2MECGSYM\\S136481522100298X.html:text/html},
}
@article{feenstra2020,
title = {The {AirSensor} open-source {R}-package and {DataViewer} web application for interpreting community data collected by low-cost sensor networks},
volume = {134},
issn = {1364-8152},
url = {https://www.sciencedirect.com/science/article/pii/S1364815220308896},
doi = {10.1016/j.envsoft.2020.104832},
abstract = {While large-scale low-cost sensor networks are now recording air pollutant concentrations at finer spatial and temporal scales than previously measured, the large environmental data sets generated by these sensor networks can become overwhelming when considering the scientific skills required to analyze the data and generate interpretable results. This paper summarizes the development of an open-source R package (AirSensor) and interactive web application (DataViewer) designed to address the environmental data science challenges of visualizing and understanding local air quality conditions with community networks of low-cost air quality sensors. AirSensor allows users to access historical data, add spatial metadata, and create maps and plots for viewing community monitoring data. The DataViewer application was developed to incorporate the functionality and plotting functions of the R package into a user-friendly web experience that would serve as the primary source for data communication for community-based organizations and citizen scientists.},
language = {en},
urldate = {2023-06-29},
journal = {Environmental Modelling \& Software},
author = {Feenstra, Brandon and Collier-Oxandale, Ashley and Papapostolou, Vasileios and Cocker, David and Polidori, Andrea},
month = dec,
year = {2020},
keywords = {Citizen scientist, Community air monitoring, Data interpretation, Low-cost air quality sensor, Open-source R package, Particulate matter PM2.5},
pages = {104832},
file = {ScienceDirect Full Text PDF:C\:\\Users\\wiinu\\Zotero\\storage\\D2KE4785\\Feenstra et al. - 2020 - The AirSensor open-source R-package and DataViewer.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\wiinu\\Zotero\\storage\\G9F43WMK\\S1364815220308896.html:text/html},
}
@article{rcoreteam2013,
title = {R: {A} language and environment for statistical computing},
author = {R Core Team, R.},
year = {2013},
note = {Publisher: Vienna, Austria},
}
@misc{purpleair,
title = {{PurpleAir} {\textbar} {Real}-time {Air} {Quality} {Monitoring}},
url = {https://www2.purpleair.com/},
abstract = {Hyper-local, real-time, public air quality map. Visualize PM2.5 AQI in your area for free. Useful to community scientists or air quality professionals alike, PurpleAir sensors are easy to install, requiring only a power outlet and WiFi.},
language = {en},
urldate = {2023-06-29},
journal = {PurpleAir, Inc.},
file = {Snapshot:C\:\\Users\\wiinu\\Zotero\\storage\\ZX4PRT2R\\www2.purpleair.com.html:text/html},
}
@misc{rcoreteam2013a,
title = {The {Comprehensive} {R} {Archive} {Network}},
url = {https://cran.r-project.org/},
urldate = {2023-06-29},
file = {The Comprehensive R Archive Network:C\:\\Users\\wiinu\\Zotero\\storage\\EB6KTGG3\\cran.r-project.org.html:text/html},
}
@book{rstudioteam2020,
address = {Boston, MA},
title = {{RStudio}: {Integrated} {Development} {Environment} for {R}},
url = {http://www.rstudio.com/},
publisher = {RStudio, PBC.},
author = {{RStudio Team}},
year = {2020},
}
@misc{creating2023,
title = {Creating {API} {Keys}},
url = {https://community.purpleair.com/t/creating-api-keys/3951},
abstract = {This article will go over how to create API keys and navigate the API portal. You will need a Gmail or Google-associated account to sign in. You can learn more information here: Sign in to your Google Account with another email address - Computer - Google Account Help The PurpleAir API uses two different API keys, a read key and a write key, which need to be used to make specific API calls. If you want to use the PurpleAir API, you can create your own API keys here: PurpleAir Develop. API key...},
language = {en},
urldate = {2023-06-29},
journal = {PurpleAir Community},
month = mar,
year = {2023},
note = {Section: Data},
file = {Snapshot:C\:\\Users\\wiinu\\Zotero\\storage\\Y33R9S7D\\3981.html:text/html},
}
@misc{collier-oxandale2022a,
title = {{AirSensor} {R} {Package}},
copyright = {GPL-3.0},
url = {https://github.com/MazamaScience/AirSensor},
abstract = {Utilities for working with data from PurpleAir sensors},
urldate = {2023-06-29},
publisher = {Mazama Science},
month = may,
year = {2023},
note = {original-date: 2019-01-18T19:53:47Z},
}
@misc{callahan_pat_2023,
title = {{AirSensor} pat\_introduction.{Rmd}},
copyright = {GPL-3.0},
url = {https://github.com/MazamaScience/AirSensor/blob/master/vignettes/articles/pat_introduction.Rmd},
abstract = {Utilities for working with data from PurpleAir sensors},
urldate = {2023-06-30},
publisher = {Mazama Science},
author = {Callahan, Jonathan},
month = mar,
year = {2023},
note = {original-date: 2019-01-18T19:53:47Z},
}
@misc{callahan_pas_2023,
title = {{AirSensor} pas\_introduction.{Rmd}},
copyright = {GPL-3.0},
url = {https://github.com/MazamaScience/AirSensor/blob/master/vignettes/articles/pas_introduction.Rmd},
abstract = {Utilities for working with data from PurpleAir sensors},
urldate = {2023-06-30},
publisher = {Mazama Science},
author = {Callahan, Jonathan},
month = mar,
year = {2023},
note = {original-date: 2019-01-18T19:53:47Z},
}
@article{AirSensor,
title = {AirSensor: Process and Display Data from Air Quality Sensors},
author = {Callahan, Jonathan and Martin, Hans and Wilson, Kayleigh and Brasel, Tate and Miller, Helen},
year = {2023},
date = {2023},
url = {https://CRAN.R-project.org/package=AirSensor}
}
@article{base,
title = {R: A Language and Environment for Statistical Computing},
author = {, R Core Team},
year = {2023},
date = {2023},
url = {https://www.R-project.org/}
}
@book{rstudioteam2020a,
title = {RStudio: Integrated Development Environment for R},
author = {RStudio Team, },
year = {2020},
date = {2020},
publisher = {RStudio, PBC.},
url = {http://www.rstudio.com/},
address = {Boston, MA}
}
@manual{dplyr,
title = {dplyr: A Grammar of Data Manipulation},
author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan},
year = {2023},
note = {R package version 1.1.4},
url = {https://CRAN.R-project.org/package=dplyr},
}
@book{ggplot2,
author = {Hadley Wickham},
title = {ggplot2: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag New York},
year = {2016},
isbn = {978-3-319-24277-4},
url = {https://ggplot2.tidyverse.org},
}
@article{MazamaCoreUtils,
title = {MazamaCoreUtils: Utility Functions for Production R Code},
author = {Callahan, Jonathan},
year = {2023},
date = {2023},
url = {https://CRAN.R-project.org/package=MazamaCoreUtils}
}
@article{MazamaSpatialUtils,
title = {MazamaSpatialUtils: Spatial Data Download and Utility Functions},
author = {Callahan, Jonathan and Carroll, Rachel and Grosman, Eli and Andre, Roger and Bergamaschi, Tom and Chen, Tina and Fore, Ruby and Leahy, Will and Miller, Helen and Nguyen, Henry and Winstanley, Robin and Yang, Alice},
year = {2023},
date = {2023},
url = {https://CRAN.R-project.org/package=MazamaSpatialUtils}
}
@article{AirSensor-2,
title = {AirSensor: Process and Display Data from Air Quality Sensors},
author = {Callahan, Jonathan and Martin, Hans and Wilson, Kayleigh and Brasel, Tate and Miller, Helen},
year = {2023},
date = {2023},
url = {https://CRAN.R-project.org/package=AirSensor}
}
@misc{monitori,
title = {Monitoring v4.1.1},
url = {https://tools.airfire.org/monitoring/v4#!/?category=PM2.5_nowcast¢erlat=42¢erlon=-95&zoom=4}
}
@misc{MazamaSpatialUtils,
title = {Introduction to {MazamaSpatialUtils}},
url = {https://cran.r-project.org/web/packages/MazamaSpatialUtils/vignettes/MazamaSpatialUtils.html},
abstract = {The MazamaSpatialUtils package was created to regularize work with spatial data. Many sources of shapefile data are available and can be used to make beautiful maps in R. Unfortunately, the data attached to these datasets, even when fairly complete, often lacks standardized identifiers such as the ISO 3166-1 alpha-2 encodings for countries. Maddeningly, even when these ISO codes are used, the dataframe column in which they are stored does not have a standardized name. It may be called “ISO” or “ISO2” or “alpha” or “COUNTRY” or any of a dozen other names we have seen.},
urldate = {2023-07-11},
publisher = {Mazama Science},
}
@article{here,
title = {here: A Simpler Way to Find Your Files},
author = {{Müller}, Kirill},
year = {2020},
date = {2020},
url = {https://CRAN.R-project.org/package=here}
}
@article{devtools,
title = {devtools: Tools to Make Developing R Packages Easier},
author = {Wickham, Hadley and Hester, Jim and Chang, Winston and Bryan, Jennifer},
year = {2022},
date = {2022},
url = {https://CRAN.R-project.org/package=devtools}
}
@manual{usethis,
title = {usethis: Automate Package and Project Setup},
author = {Hadley Wickham and Jennifer Bryan and Malcolm Barrett and Andy Teucher},
year = {2024},
note = {R package version 3.0.0},
url = {https://CRAN.R-project.org/package=usethis},
}
@article{tidyverse,
title = {Welcome to the {tidyverse}},
author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
year = {2019},
journal = {Journal of Open Source Software},
volume = {4},
number = {43},
pages = {1686},
doi = {10.21105/joss.01686},
}