Air Sensor Network Analysis Tool: R-Shiny Application

Area of Science:

• Environmental science
• Data science
• Public health

Background:

• Poor air quality poses risks to human health and the environment.
• Air quality data is crucial for understanding and mitigating pollution exposure.
• Low-cost air sensors offer localized data but require rigorous quality control.

Purpose of the Study:

• To develop and present an R-Shiny application for analyzing air sensor data.
• To assist air quality professionals in data quality assessment and local air quality analysis.
• To improve the usability and reproducibility of air sensor data.

Main Methods:

• Development of an R-Shiny application for air sensor data analysis.
• Comparison of air sensor data with reference monitors for quality assurance.
• Application of basic quality assurance and control procedures.
• Case studies involving PM2.5 sensors during wildfire events and O3 sensors over a year.

Main Results:

• The R-Shiny tool facilitates quicker analysis and utilization of air sensor data.
• The application aids in understanding data quality through comparisons with reference monitors.
• Case studies demonstrate the tool's utility in evaluating sensor performance under various conditions.

Conclusions:

• The developed R-Shiny application provides an accessible starting point for air sensor data analysis.
• The tool enhances the ability of agencies to analyze and use sensor data effectively.
• Increased reproducibility of analyses is a key benefit for air quality professionals.