SENTINEL: A Shiny App for Processing and Analysis of Fenceline Sensor Data

Area of Science:

• Environmental Science
• Data Science
• Sensor Technology

Background:

• Low-cost fenceline sensors generate large volumes of high-frequency pollutant data.
• Existing data processing methods are often overwhelming for users without specialized software.
• Industrial facilities and emergency responders need efficient tools for emissions monitoring.

Purpose of the Study:

• To introduce SENTINEL, an R Shiny application for processing and analyzing fenceline sensor data.
• To provide a user-friendly framework for interpreting emissions data from volatile organic compounds and methane.
• To support emerging user groups of lower-cost fenceline sensors.

Main Methods:

• Development of the SENTINEL application using R Shiny.
• Implementation of temporal data aggregation for synthesized analysis.
• Inclusion of automated quality assurance screening and baseline correction algorithms.
• Creation of a user-friendly graphical user interface for data visualization and interpretation.

Main Results:

• SENTINEL offers a consistent framework for processing, analyzing, and visualizing fenceline sensor data.
• Automated quality assurance and baseline correction improve data reliability.
• The application streamlines data analysis, aiding in the interpretation of source emissions.

Conclusions:

• SENTINEL provides a valuable tool for managing and interpreting fenceline sensor data.
• The application enhances the usability of low-cost sensor networks for environmental monitoring.
• SENTINEL supports informed decision-making in industrial emissions management and emergency response.