Abstract
There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and 4 ppb for O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> could be achievable with a few mobile references in urban environments.
Highlights
D UE to the health impact of low air quality [1], [2], a lot of research with low-cost gas and particulate matter sensors for high-resolution air quality monitoring has been conducted in the last years [3]–[7]
Figure 2 illustrates the performance of device 1 with the three algorithms under different encounter frequencies using a simple base calibration, whereas Figure 3 shows the same result with an extended base calibration
The offset in the absolute error at low encounter frequency is the result from the recalibration during deployment
Summary
D UE to the health impact of low air quality [1], [2], a lot of research with low-cost gas and particulate matter sensors for high-resolution air quality monitoring has been conducted in the last years [3]–[7]. Data quality objectives imposed by legislators must be met [12]. Researchers came up with the idea of combining an array of different sensors into so-called low-cost sensors systems with the purpose of compensating interfering effects with models obtained from machine learning algorithms [13], [14] such as neural networks [4] or random forests [9] trained on field data This generally leads to non-representative models followed by concept drift [15], [16]; the environmental conditions vary over time and space, so the calibration parameters need to change frequently. Maintaining reliability and trustworthiness over the course of the lifetime of an air quality sensor network remains a challenge
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