Intra-workday fluctuations of airborne contaminant concentration and the time-weighted average

Abstract

Air contaminant concentrations vary between and within workdays and are often measured across a workday by passing a known air volume through a collection device. Laboratory analysis determines the contaminant mass trapped, providing a time-weighted average air concentration (C_TWA). This approach was driven by the best technologies available as exposure measurement processes developed and accuracy and measurement precision were sought. However, all integrated concentration•time (C•t) values determining C_TWA are equally weighted in assessing exposures, intra-workday concentration variability is unknown, and results are available days later. At times inappropriately, an occupational exposure limit (OEL) expressed as a C_TWA also requires equal weighting of all C•t values across an exposure period following concepts of Haber’s law. Continuous monitoring (real-time detection) informs both the C_TWA and the variability of C during sampling, which are needed for stressors where a ceiling or peak OEL exists, for dangerous exposures to permanent gas-type contaminants, and for immediately dangerous to life or health (IDLH) conditions. Selective and accurate real-time detection instruments are not available for all air contaminants, but exposure magnitude information may be provided. The large amounts of data from continuous monitoring and the ability to correlate exposure maxima to specific tasks are also important. An exposure assessment role exists for selective and nonselective monitors, and in some cases, similar accuracy and precision are provided compared to laboratory analyses. Continuous monitoring may be of value when the alternative is the collection of a few C_TWA data points. Digitized personal monitor data can support the automation of some exposure control decisions or allow such decisions to be made by people in near real-time. The emerging Internet of Things (IoT) offers opportunities to integrate digital exposure data into decision-making to increase both efficiency and safety. The perceived and real uncertainty associated with real-time exposure assessments may be lessened with work to rule out the presence of know interferents and confirm the presence of target analytes.

Keywords:

• Continuous monitoring
• Haber’s law
• industrial hygiene sample
• IoT
• real-time detection
• time-weighted average

Acknowledgments

The author acknowledges the helpful review and comments provided by Steven Jahn, CIH, FAIHA.

Disclaimer

The views expressed here are those of the author. This document is not a standard or regulation. It creates no new legal obligations and alters no existing obligations created by OSHA standards or the Occupational Safety and Health Act. It may contain recommendations that are advisory in nature, informational in content, and intended to support the provision of safe and healthful workplaces. This article is not subject to U.S. copyright law.

Data availability

Data are available by contact.