Scalable and effective calibration is a fundamental requirement for Low Cost Air Quality Monitoring Systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensors specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in Air Quality (AQ) monitoring networks. Concept and sensor drift often force calibration process to be frequently repeated. These issues lead to unbearable calibration costs which denies their massive deployment when accuracy is a concern. In this work, We propose a zero transfer samples, global calibration methodology as a technological enabler for IoT AQ multisensory devices which relies on low cost Particulate Matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning concepts and can be universally applied to all units of the same type. A multi season test campaign shown that, when applied to different sensors, this methodology performances match those of state of the art methodology which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded on board of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long range data transfer needs.

翻译：可扩展且有效的校准是低成本空气质量监测系统的基本要求，将有助于在城市中实现精确且泛在的监测。受环境干扰和制造差异的影响，这些设备需要包含特定传感器且复杂的校准过程，才能达到足够的精度，从而作为空气质量监测网络中的指示性测量设备进行部署。概念漂移和传感器漂移经常迫使校准过程频繁重复。这些问题导致难以承受的校准成本，当精度成为关注点时，便阻碍了其大规模部署。在本工作中，我们提出一种零传输样本的全局校准方法，作为物联网空气质量多传感器设备的技术推动者，该方法依赖于低成本颗粒物传感器。此方法基于有限数量的物联网空气质量多传感器单元现场记录的响应及机器学习概念，并可通用地应用于所有同类型单元。一项多季节测试活动表明，当应用于不同传感器时，该方法的性能与需要为每个不同单元推导不同校准参数的最先进方法的性能相匹配。若得到证实，这些结果表明，在恰当推导的情况下，全局校准定律可被用于大量联网设备，从而大幅降低成本，最终实现精确的物联网空气质量监测设备的大规模部署。此外，此校准模型可轻松嵌入设备内部或在边缘端实现，从而允许个人暴露监测应用即时获取精确读数，同时减少长距离数据传输需求。