J Occup Environ Hyg. 2026 Jun 18:1-13. doi: 10.1080/15459624.2026.2664464. Online ahead of print.
ABSTRACT
Exposure monitoring data available to industrial hygienists will eventually surpass the ability of a practitioner to effectively interpret without a consolidating computational approach. For large sets of time-series exposure data, the most important question that can be answered to determine if further exposure evaluation is required may be formulated as: Does this exposure data contain any period of time during which the identified occupational exposure limit value could be exceeded? Determining the answer to this question may be computationally intensive using current exposure decision methodology, better suited for small data sets consisting of single time-weighted average exposures. The authors instead proposed an algorithmic method for evaluating large sets of time-series exposure data without clearly defined exposure start and stop times, while also incorporating recommended data cleaning methods and an evaluation of statistical uncertainty. The outcome of this analysis was a binary “thumbs up,” indicating the time-series exposure data did not contain any time period that may have exceeded the limit value when considering confidence intervals, or “thumbs down,” indicating the opposite. Finally, the authors demonstrated the algorithmic heuristic using a proof-of-concept available to the public at no cost at https://athena-heuristic.streamlit.app/.
PMID:42314188 | DOI:10.1080/15459624.2026.2664464
