Air Sensor Network Analysis Tool: R-Shiny Application

Poor air quality can harm human health and the environment. Air quality data is needed to understand and reduce exposure to air pollution. Air sensor data can sup-plement national air monitoring network data to better understand localized air qual-ity and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality. Analyzing sensor data requires not only a background knowledge of air quality but also often requires extensive data analysis skills which may require new skills or time that burdens many air agencies (e.g., small states, local). 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) ap-plying basic quality assurance (QA) and quality control (QC), 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 re-producibility of analyses. This paper highlights a case study using the tool to explore sensor performance during Canadian wildfire smoke impacts in the midwestern United States during June of 2023.