The leakage of hazardous gases and chemical vapors is considered one of the dangerous accidents that can occur in laboratories, workshops, warehouses, and industrial sites that use or store these substances. The early detection and alarming of hazardous gases and volatile chemicals are significant to keep the safety conditions for the people and life forms who are work in and live around these places. In this paper, we investigate the available mobile detection and alarming systems for toxic, hazardous gases and volatile chemicals, especially in the laboratory environment. We included papers from January 2010 to August 2021 which may have the newest used sensors technologies and system components. We identified (236) papers from Clarivate Web of Science (WoS), IEEE, ACM Library, Scopus, and PubMed. Paper selection has been done based on a fast screening of the title and abstract, then a full-text reading was applied to filter the selected papers that resulted in (42) eligible papers. The main goal of this work is to discuss the available mobile hazardous gas detection and alarming systems based on several technical details such as the used gas detection technology (simple element, integrated, smart, etc.), sensor manufacturing technology (catalytic bead, MEMS, MOX, etc.) the sensor specifications (warm-up time, lifetime, response time, precision, etc.), processor type (microprocessor, microcontroller, PLC, etc.), and type of the used communication technology (Bluetooth/BLE, Wi-Fi/RF, ZigBee/XBee, LoRa, etc.). In this review, attention will be focused on the improvement of the detection and alarming system of hazardous gases with the latest invention in sensors, processors, communication, and battery technologies.

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.

The measurement of air quality parameters for indoor environments is of increasing importance to provide sufficient safety conditions for workers, especially in places including dangerous chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality index (IAQ-index) and total volatile organic Compounds (TVOC) are two important parameters to measure air impurities or air pollution. Both parameters are widely used in gases sensing applications. In this paper, the IAQ-index and TVOCs have been investigated to identify the best and most flexible solution for air quality threshold selection of hazardous/toxic gases detection and alarming systems. The TVOCs from the SGP30 gas sensor and the IAQ-index from the SGP40 gas sensor were tested with 12 different organic solvents. The two gas sensors are combined with an IoT-based microcontroller for data acquisition and data transfer to an IoT-cloud for further processing, storing, and monitoring purposes. Extensive tests of both sensors were carried out to determine the minimum detectable volume depending on the distance between the sensor node and the leakage source. The test scenarios included static tests in a classical chemical hood, as well as tests with a mobile robot in an automated sample preparation laboratory with different positions.

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer’s daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.