This article presents a literature review of sensors for the monitoring of benzene in ambient air and other volatile organic compounds. Combined with information provided by stakeholders, manufacturers and literature, the review considers commercially available sensors, including PID-based sensors, semiconductor (resistive gas sensors) and portable on-line measuring devices as for example sensor arrays. The bibliographic collection includes the following topics: sensor description, field of application at fixed sites, indoor and ambient air monitoring, range of concentration levels and limit of detection in air, model descriptions of the phenomena involved in the sensor detection process, gaseous interference selectivity of sensors in complex VOC matrix, validation data in lab experiments and under field conditions.

The Internet of Things (IoT) is characterized by a large number of interconnected devices or assets. Measurement instruments in the IoT are typically digital in the sense that their indications are available only as digital output. Moreover, a growing number of IoT sensors contain a built-in pre-processing system, e.g., for compensating unwanted effects. This paper considers the application of metrological principles to such so-called “smart sensors” in the IoT. It addresses the calibration of digital sensors, mathematical and semantic approaches, the communication of data quality and the meaning of traceability for the IoT in general.

Virtual experiments (VEs) and digital twins (DTs), pivotal for realizing European strategic policies on sustainability and digitalization within Industry 4.0 and the European Green Deal, simulate physical systems and characteristics in a virtual environment, with DTs incorporating dynamic inputs from and outputs to the real-world counterpart. To ensure confidence in their use and outcomes, traceability and methods to evaluate measurement uncertainty are needed, topics that are hardly covered by the literature so far. This paper provides a harmonized definition of VEs and DTs and introduces a framework for evaluating measurement uncertainty. Furthermore, it discusses how to propagate the uncertainty of the contributions coming from the different parts of the DT. For the core part of the DT, the framework derived for VEs can be used. For the physical-to-virtual (P2V) connection and the virtual-to-physical (V2P) connection, additional sources of uncertainty need to be considered. This paper provides a metrological framework for taking all these uncertainty contributions into account while describing a framework to establish traceability for DTs. Two case studies are presented to demonstrate the proposed methodology considering industrially relevant measuring instruments and devices, namely, a coordinate measuring machine (CMM) and a collaborative robot arm (cobot).

In 2015, the energy measurement of some static electricity meters was found to be sensitive to specific conducted electromagnetic disturbances with very fast current changes caused by highly nonlinear loads, leading to meter errors up to several hundred percent. This article describes new results on the electromagnetic compatibility (EMC) of 16 different meters from all over Europe when exposed to real-world disturbance signals. Those test signals were obtained from household appliances and onsite measurements at metered supply points all over Europe. The results show that also the interference signals recorded onsite can cause measurement errors as large as several hundred percent, even for meters that pass the present EMC standards. This unambiguously demonstrates that the present immunity testing standards do not cover the most disturbing conducted interference occurring in present daily-life situations due to the increased use of nonlinear electronics. Furthermore, to enable the adoption of potential new test waveforms in future standards for electricity meter testing, artificial test waveforms were constructed based on real-world waveforms using a piece-wise linear model. These artificial test waveforms were demonstrated to cause meter errors similar to those caused by the original real-life waveforms they are representing, showing that they are suitable candidates for use in improved standardization of electricity meter testing.

Sensor networks, which are increasingly being used in a broad range of applications, constitute a measurement paradigm involving ensembles of sensors measuring possibly different quantities at a discrete sample of spatial locations and temporal points outside the laboratory. If sensor networks are to be considered as true metrology systems and the measurement results derived from them used for decision-making, such as in a regulatory context, it is important that the results are accompanied by reliable statements of measurement uncertainty. This paper gives a preview of some of the work undertaken within the European-funded ‘Fundamental principles of sensor network metrology (FunSNM)’ project to address the challenges of measurement uncertainty evaluation in some real-world sensor network applications. The applications demonstrate that sensor networks possess features related to the nature of the measured quantities, to the nature of the measurement model, and to the nature of the measured data. These features make conventional methods of measurement uncertainty evaluation, and established guidelines for measurement uncertainty evaluation difficult to apply. An overview of some of the modelling tools used to address the challenges of measurement uncertainty evaluation in those applications is given.