Open-source software tools designed specifically for evaluating and reporting air sensor performance are limited. Available tools do not provide a means for summarizing sensor performance using common statistical metrics and figures nor are they suited for handling the wide variety of data formats currently used by air sensors. We developed sensortoolkit as a free, open-source Python library to encourage use of the U.S. Environmental Protection Agency’s (U.S. EPA) recommended performance evaluation protocols for air sensors measuring particulate matter and gases. The library compares collocated air sensor against reference monitor data and includes procedures to reformat both datasets into a standardized format using an interactive setup module. Library modules calculate performance metrics (e.g., coefficient of determination (R2), slope, intercept, root mean square error (RMSE)) and make plots to visualize the data. These metrics and plots can be used to better understand sensor accuracy, precision between sensors of the same make and model, and the influence of meteorological parameters at 1-hour and 24-hour averages. Results can be compiled into a reporting template allowing for easier comparison of sensor performance results generated by different organizations. This paper provides a summary of the sensortoolkit and a case study to demonstrate its utility.

Open-source software tools designed specifically for evaluating and reporting air sensor performance are limited. The available tools do not provide a means for summarizing the sensor performance using common statistical metrics and figures, nor are they suited for handling the wide variety of data formats currently used by air sensors. We developed sensortoolkit v1.1.0 as a free, open-source Python v3.8.20 library to encourage the use of the U.S. Environmental Protection Agency’s (U.S. EPA) recommended performance evaluation protocols for air sensors measuring particulate matter and gases. The library compares the collocated air sensor against reference monitor data and includes procedures to reformat both datasets into a standardized format using an interactive setup module. Library modules calculate performance metrics (e.g., the coefficient of determination (R²), slope, intercept, and root mean square error (RMSE)) and make plots to visualize the data. These metrics and plots can be used to better understand sensor accuracy, the precision between sensors of the same make and model, and the influence of meteorological parameters at 1 h and 24 h averages. The results can be compiled into a reporting template allowing for the easier comparison of sensor performance results generated by different organizations. This paper provides a summary of the sensortoolkit and a case study to demonstrate its utility.