Sensortoolkit-A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for...

Area of Science:

• Environmental Science
• Data Science
• Air Quality Monitoring

Background:

• Limited availability of open-source software for air sensor performance evaluation.
• Existing tools lack standardized statistical metrics and data format handling for diverse air sensors.
• Need for accessible tools to implement U.S. EPA's recommended performance evaluation protocols.

Purpose of the Study:

• Introduce sensortoolkit v1.1.0, a free, open-source Python library for air sensor evaluation.
• Facilitate the use of U.S. EPA's performance evaluation protocols for particulate matter and gas sensors.
• Provide a standardized method for comparing air sensor data against reference monitors.

Main Methods:

• Developed sensortoolkit as a Python library (v3.8.20).
• Implemented an interactive setup module for data reformatting.
• Included procedures for calculating performance metrics (R², slope, intercept, RMSE) and generating plots.

Main Results:

• Sensortoolkit enables standardized comparison of air sensor data with reference monitors.
• Calculated metrics and visualizations assess sensor accuracy, precision, and meteorological influences at 1-h and 24-h averages.
• A reporting template facilitates easy comparison of sensor performance results across organizations.

Conclusions:

• Sensortoolkit addresses the need for a comprehensive, open-source tool for air sensor performance evaluation.
• The library promotes consistent application of U.S. EPA protocols.
• Case study demonstrates sensortoolkit's utility in understanding and reporting air sensor performance.