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Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges

机译：校准低成本传感器，用于环境空气监测：技术，趋势和挑战

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摘要

Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs.
机译：低成本传感器（濒海战斗舰）被广泛认可为引进补充传统的空气通过空气监管机构监控的模式转变。然而，有关于它的数据质量和性能的稳定性，大大限制了其大规模应用的关注。知道最近的技术进步，LCS校准的挑战是推动环境监测领域有着重大的意义。通过总结公布的证据，这项审查表明，全球传感器市场正在迅速由于汹涌的需求不断扩大，但校准的努力都集中在传感器的选择有限。相对湿度修正，回归和机器学习是三大主流校准技术。虽然没有一个放之四海而皆准的解决办法，最新的研究趋势的特点是机器学习。校准的持续时间在很大程度上被忽视了实验设计，但发现它是影响不同的校准方法的性能，尤其是那些数据驱动的。在地理上，中国和美国获得了大多数研究关注的传感器校准领域，但颗粒物的热点和校准点之间的空间不匹配，为世界的其余部分是相当明显的。不完整的，分布不均的研究足迹可能限制方法的普遍性的大型试验，以及减少在遭受更大的环境正义危机不足的地区的监测能力。在一般情况下，模型的性能是由包括影响因素的关键增强，但改善的程度并不明显相关解释变量的数目。总体而言，研究证明现场校准的至关重要的传感器部署之前，但需要更多的研究，以建立可定制特殊需求的实验方案。

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