Evaluation methods for low-cost particulate matter sensors
<p>Understanding and improving the quality of data generated from low-cost sensors represent a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month-long campaign that included side-by-side measurements and compa...
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/372676af792143b291ab1995d94f3d0b |
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| Sumario: | <p>Understanding and improving the quality of data generated from low-cost sensors represent a crucial step in using these sensors to fill gaps
in air quality measurement and understanding. This paper shows results from
a 10-month-long campaign that included side-by-side measurements and
comparison between reference instruments approved by the United States
Environmental Protection Agency (EPA) and low-cost
particulate matter sensors in Bartlesville, Oklahoma. At this rural site in
the Midwestern United States the instruments typically encountered only low
(under 20 <span class="inline-formula">µg</span> m<span class="inline-formula"><sup>−3</sup></span>) concentrations of particulate matter; however, higher concentrations (50–400 <span class="inline-formula">µg</span> m<span class="inline-formula"><sup>−3</sup></span>) were observed on 3 different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (<span class="inline-formula"><i>R</i><sup>2</sup></span> and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fit a specific need. Though <span class="inline-formula"><i>R</i><sup>2</sup></span> and root mean square error remain the dominant metrics
in sensor evaluations, an alternative approach using a prediction interval
may offer more consistency between evaluations and a more direct
interpretation of sensor data following an evaluation. Ongoing quality
assurance for sensor data is needed to ensure that data continue to meet
expectations. Observations of trends in linear regression parameters and
sensor bias were used to analyze calibration and other quality assurance
techniques.</p> |
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