Air Sensor Network Analysis Tool: R-Shiny Application
2025
Karoline K. Barkjohn | Todd Plessel | Jiacheng Yang | Gavendra Pandey | Yadong Xu | Stephen Krabbe | Catherine Seppanen | Renée Bichler | Huy Nguyen Quang Tran | Saravanan Arunachalam | Andrea L. Clements
Poor air quality can harm human health and the environment. Air quality data are needed to understand and reduce exposure to air pollution. Air sensor data can supplement national air monitoring data, allowing for a better understanding of localized air quality and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality, and analyzing sensor data often requires extensive data analysis. To address these issues, an R-Shiny application has been developed to assist air quality professionals in (1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, (2) applying basic quality assurance and quality control, and (3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the reproducibility of analyses. While more in-depth custom analysis may still be needed for some sensor types (e.g., advanced correction methods), this tool provides an easy starting place for analysis. This paper highlights two case studies using the tool to explore PM2.5 sensor performance under the conditions of wildfire smoke impacts in the Midwestern United States and the performance of O3 sensors for a year.
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