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Air Sensor Data Unifier: R-Shiny Application.

Karoline K Barkjohn1, Catherine Seppanen2, Saravanan Arunachalam2

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This study introduces an R-Shiny application to simplify processing diverse air sensor data for improved local air quality analysis and health impact assessments.

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Area of Science:

  • Environmental Science
  • Data Science
  • Public Health

Background:

  • Local air quality monitoring is crucial for public health and exposure reduction.
  • Integrating national networks with diverse air sensor data presents format and quality control challenges.
  • Existing data formats hinder consistent air quality assessment.

Purpose of the Study:

  • To develop a user-friendly tool for processing and standardizing air sensor data.
  • To streamline data reformatting and quality control for air quality professionals.
  • To facilitate the integration of sensor data into existing air quality analysis platforms.

Main Methods:

  • Development of an R-Shiny application for data import and manipulation.
  • Implementation of features for describing data formats and performing quality control.
  • Enabling export of processed data into standard formats for further analysis.

Main Results:

  • The R-Shiny application efficiently imports, formats, and quality-controls text-based air sensor data.
  • Saved format information accelerates processing for similar sensors.
  • The tool supports integration with various air quality analysis software.

Conclusions:

  • The developed application enhances the efficiency of working with air sensor data.
  • It empowers air quality professionals to conduct more robust analyses.
  • This tool contributes to better understanding and management of local air quality.