Air Sensor Data Unifier: R-Shiny Application

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management.

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Publication Details

Journal
UNC Libraries
Published
2025-09-04
DOI
https://doi.org/10.17615/6ssf-ay76
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00

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article

Air Sensor Data Unifier: R-Shiny Application

Karoline K. Barkjohn, Andrea L. Clements, Saravanan Arunachalam, Stephen Krabbe et al.
UNC Libraries
Air Quality Monitoring and Forecasting
article

Air Sensor Data Unifier: R-Shiny Application

Karoline K. Barkjohn, Andrea L. Clements, Saravanan Arunachalam, Stephen Krabbe, Catherine Seppanen
article en

Abstract

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management.

UNC Libraries
University of North Carolina at Chapel Hill (US)
U.S. Environmental Protection Agency, New Jersey Department of Environmental Protection, Pennsylvania Department of Environmental Protection, Office of Research and Development, Desert Research Institute
Openalex Percentile: Top 70%
Air Quality Monitoring and Forecasting
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Air Sensor Data Unifier: R-Shiny Application — Karoline K. Barkjohn, Andrea L. Clements, et al. · UNC Libraries (2025) | TGRS Research Map | TGRS