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sensortoolkit: A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for Performance Evaluations
Citation:
Frederick, S., K. Barkjohn, R. Duvall, AND A. Clements. sensortoolkit: A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for Performance Evaluations. Air sensor international conference, Pasadena, California, May 11 - 13, 2022.
Impact/Purpose:
Air sensors have become very popular for use by the public and research community for monitoring local trends in ambient air quality. However, sensor data quality is highly variable and it is important to test sensors against collocated regulatory-grade measurements. Simultaneously, sensor and reference data are presented in a wide range of data formats and existing software packages intended for analyzing sensor data may be limited in scope to the data format for a single sensor make and model. These limitations present challenges for individuals who may wish to characterize their sensor’s performance using common statistical metrics and figures. This presentation discusses a new Python library, “sensortoolkit”, which permits the analysis of sensor and reference data regardless of sensor model and data formatting. Sensor performance is characterized via the performance targets and metrics recently published by EPA for fine particulate matter (PM2.5) and ozone (O3) sensors, and results can be compiled into the testing report included alongside EPA’s performance targets documents. The presentation will be made to researchers attending the Air Sensors International Conference in Pasadena, CA (May 11-13, 2022).
Description:
Past efforts to establish open-source software tools for reporting sensor performance have been limited. Sensor and reference data formats vary widely, and in turn, many existing software packages evaluate only one type of sensor model. Other packages allow broader utilization of air quality data yet may not be specifically tailored for evaluating sensor performance against reference data. Additionally, these packages do not provide means for summarizing sensor performance in a reporting template using common statistical metrics and figures. To encourage broader utilization of the U.S. Environmental Protection Agency’s (EPA) recommended performance metrics and target values for sensors measuring fine particulate matter (PM2.5) and ozone (O3), EPA developed a new, open-source Python library named “sensortoolkit”. The library compares collocated sensor data against reference monitor data and includes methodology to re-format both datasets into a standardized format using an interactive setup module. Library modules are included for calculating EPA’s recommended sensor performance metrics and for making relevant plots. 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-hr and 24-hr averages. Results can be compiled into the reporting template included alongside EPA’s performance targets documents. The sensortoolkit library is designed for any user, from novices to researchers.
URLs/Downloads:
SENSOR TOOLKIT A PYTHON LIBRARY FOR STANDARDIZING THE INGESTION, ANALYSIS, AND REPORTING OF AIR SENSOR DATA FOR PERFORMANCE EVALUATIONS.PDF (PDF, NA pp, 746.033 KB, about PDF)ORD 044916 SENSOR TOOLKIT PYTHON LIBRARY FOR STANDARDIZING THE INGESTION, ANALYSIS, AND REPORTING OF AIR SENSOR DATA FOR PERFORMANCE EVALUATION 508 FORM.PDF (PDF, NA pp, 197.276 KB, about PDF)
https://youtu.be/GAn70JHngAY
