Abstract
As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.
Highlights
For studies where methane concentration is being measured, a slight decrease in accuracy and precision as assessed with goodness of fit statistics might be viewed as worthwhile if the ease of implementing that sensor quantification scheme is significantly reduced
In Boulder, only nine days of co-location data were available, resulting in worse fits than we would anticipate if designing an experiment for the implementation of a universal calibration model
The 1-Hop and Sensor signal normalization approaches, which require the “main” pod co-located with the reference instrument to be co-located with the remainder of the pods, were not utilized
Summary
Low-cost sensors are increasingly utilized to monitor air quality in rural and urban spaces alike. Their decreased cost is generally in the $1000 range, as opposed to regulatory grade monitoring instruments, which can cost tens of thousands of dollars each. A lower cost means lower quality data, as low-cost sensors generally rely on additional calibration methods, which can be lengthy and time-consuming. It can take up to a year to find a suitable sensor calibration model, yielding high enough data quality and low enough uncertainty. Low-cost sensors are generally effective in elucidating spatial and temporal differences on neighborhood and regional scales alike [1,2,3,4,5]
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