Assessing the accuracy of low-cost optical particle sensors using a physics-based approach
David H. Hagan and Jesse H. Kroll
Atmospheric Measurement Techniques 2020 13, 6343-6355
DOI: 10.5194/amt-13-6343-2020
These data files are intended as additional supplementary information for this publication. If you would like to use any data herein in a publication, please contact the corresponding authors, David Hagan ([email protected]) or Jesse Kroll ([email protected]) first.
If you have any questions or comments about this data or manuscript, please contact either David Hagan or Jesse Kroll.
David H Hagan
Jesse H Kroll
The final manuscript can be downloaded directly from the Atmospheric Measurement Techniques website, as it is open access.
Assessing the accuracy of low-cost optical particle sensors using a physics-based approach
Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.
The environment, code, and most data used to produce this manuscript can be found on this page. By releasing the Dockerfile needed to build the analysis environment, there should be no issues related to software development environment (knock on wood...). The goal is to get as close as possible to reproducible, open science. If you are unfamiliar with Docker or how to use it, please check out this fantastic tutorial or the Docker documentation.
Analysis is done using Python and heavily leverages the python data science ecosystem including pandas, numpy, feather, scipy, seaborn, and many other wonderful libraries.