Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks,ACS Sensors

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Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks
ACS Sensors ( IF 8.2 ) Pub Date : 2018-03-06 00:00:00 , DOI: 10.1021/acssensors.8b00074
Georgia Miskell _{1,

2} , Jennifer A. Salmond ₂ , David E. Williams _{1,

3}

Affiliation

We provide a simple, remote, continuous calibration technique suitable for application in a hierarchical network featuring a few well-maintained, high-quality instruments (“proxies”) and a larger number of low-cost devices. The ideas are grounded in a clear definition of the purpose of a low-cost network, defined here as providing reliable information on air quality at small spatiotemporal scales. The technique assumes linearity of the sensor signal. It derives running slope and offset estimates by matching mean and standard deviations of the sensor data to values derived from proxies over the same time. The idea is extremely simple: choose an appropriate proxy and an averaging-time that is sufficiently long to remove the influence of short-term fluctuations but sufficiently short that it preserves the regular diurnal variations. The use of running statistical measures rather than cross-correlation of sites means that the method is robust against periods of missing data. Ideas are first developed using simulated data and then demonstrated using field data, at hourly and 1 min time-scales, from a real network of low-cost semiconductor-based sensors. Despite the almost naïve simplicity of the method, it was robust for both drift detection and calibration correction applications. We discuss the use of generally available geographic and environmental data as well as microscale land-use regression as means to enhance the proxy estimates and to generalize the ideas to other pollutants with high spatial variability, such as nitrogen dioxide and particulates. These improvements can also be used to minimize the required number of proxy sites.

中文翻译：

网络中低成本空气质量测量传感器标定问题的解决

我们提供一种简单，远程，连续的校准技术，适用于具有少量维护良好的高质量仪器（“代理”）和大量低成本设备的分级网络。这些想法基于对低成本网络的明确定义，此处定义为在小时空范围内提供有关空气质量的可靠信息。该技术假设传感器信号呈线性。通过将传感器数据的平均值和标准偏差与同时从代理导出的值进行匹配，可以得出运行斜率和偏移量估计值。这个想法非常简单：选择适当的代理和平均时间，该时间足够长以消除短期波动的影响，但又要足够短以至于可以保留规则的昼夜变化。使用连续的统计量而非站点的互相关性意味着该方法对于丢失数据的时期是可靠的。首先使用模拟数据开发思想，然后使用每小时一次和1分钟时标的现场数据从低成本的基于半导体的传感器的实际网络中进行演示。尽管该方法非常简单，但它对于漂移检测和校准校正应用都非常可靠。我们讨论了使用普遍可用的地理和环境数据以及微观尺度的土地利用回归来增强代理估算并将这些思想推广到具有高空间变异性的其他污染物（例如二氧化氮和颗粒物）的方法。这些改进还可以用于最大程度地减少所需的代理站点数量。

更新日期：2018-03-06

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