2,224
Views
7
CrossRef citations to date
0
Altmetric
Research Article

Embedded systems and the Internet of Things: Can low-cost gas sensors be used in risk assessment of occupational exposure?

, , , &
 

Abstract

The Internet of Things (IoT) explores new perspectives and possible improvements in risk assessment practices and shows potential to measure long-term and real-time occupational exposure. This may be of value when monitoring gases with short-term maximum levels and for time-weighted average (TWA) concentrations used in standard measuring practices. A functional embedded system was designed using low-cost carbon monoxide (CO) electrochemical sensors and long-range-wide-area-network radio communication technology (LoRaWAN) was used to enable internet connectivity. This system was utilized to monitor gas levels continuously in the working atmosphere of an incineration plant over a 2-month period.

The results show that stable and long-term continuous data transfer was enabled by LoRaWAN, which proved useful for detecting rapid changes in gas levels. However, it was observed that raw data from the low-cost sensors did not meet the NIOSH accuracy criteria of ±25% of the estimated true concentration based on field data from a co-located gas detector that met the NIOSH accuracy criteria. The new IoT technologies and CO sensor networks shows potential for remote monitoring of exposure in order to: (1) detect rapid changes in CO and other possible hazardous airborne gases; and (2) show the dynamic range of real-time data that may be hazardous for workers in the sampled areas. While the IoT low-cost sensors appear to be useful as a sentinel for monitoring hazardous atmospheres containing CO, the more useful finding may be showing real-time changes and the dynamic range of exposures, thus shedding light on the transient and toxic nature of airborne hazards. More importantly, the low-cost CO sensors are not a clear substitute for the more costly real-time gas detectors that perform within the NIOSH accuracy criteria.

Acknowledgments

This project was partially funded by the Research Council of Norway. We would like to thank Joar Nicolaysen at Vestteknikk AS for assisting with the single-sample CO test.