Short answer: A Similar Exposure Group (SEG) is a group of workers who share the same general exposure profile because they do similar tasks, with similar materials, in similar conditions.[1] The concept is simple; doing it correctly across a plant with thousands of employees isn't — and it falls apart fast when the field data behind it is disorganized. Structured, task-tagged field data is what makes SEG mapping at scale actually work.
What is a Similar Exposure Group, and why does it matter?
AIHA defines an SEG as workers with the same general exposure profile "because of the similarity and frequency of the tasks they perform, the similarity of the materials and processes with which they work, and the similarity of the manner in which they perform the tasks."[1]
The point of an SEG isn't paperwork — it's efficiency. Rather than personally monitoring every one of, say, 250 plant employees, you identify the handful of groups that actually carry meaningful exposure risk and monitor representatively within each group.[2] Done well, SEGs are the foundation the rest of an IH program is built on.[2]
Why is SEG mapping harder in large assembly plants?
Because the variables multiply fast, and paper-based tracking can't keep up.
- Volume: thousands of employees across dozens of stations, shifts, and job classifications.
- Task variability: the same job title can carry different exposures depending on the specific assembly line, process, or material in use that shift.
- The core statistical goal works against ambiguity: the entire point of an SEG is to minimize variability *within* the group — a poorly defined group produces confidence intervals too wide to be useful.[3]
- Data currency: past exposure monitoring data helps define SEGs, but only if it's actually retrievable and tagged to the right group.[4]
When field notes don't reliably tag which specific task, line, and material a worker was doing at the time of an observation, SEG boundaries blur — and blurred boundaries defeat the purpose of grouping in the first place.
What does good SEG data capture look like?
For SEG classification to hold up, each field observation needs to tie cleanly to:
- The specific employee task being performed (not just job title)
- Assembly line or work area
- Materials/processes involved (solvents, welding, paint, etc.)
- Shift and frequency of the task
- Any prior monitoring data associated with that task/location combination
That's a lot of structured metadata to capture consistently across a large hourly workforce — exactly the kind of thing a clipboard loses.
How myIH supports SEG mapping at scale
myIH is industrial hygiene field-note software that structures field observations at the point of collection, including the task-level tagging that SEG classification depends on.
- Structured task tagging lets a technician quickly tag an employee's specific assembly-line task during a walkthrough, rather than reconstructing it later
- Consistent, retrievable field data so location- and task-based exposure history stays usable for SEG boundary decisions
- One-click export to Excel or PDF field notes, so your data is ready to move into enterprise EHS databases when needed
- Real-time oversight of what's being logged, so gaps in task tagging get caught before the walkthrough ends
To be clear on scope: myIH structures and exports your team's field observations — it doesn't perform the SEG statistical analysis itself. It makes sure the underlying task and exposure data is clean enough for that analysis to be meaningful.
Frequently asked questions
A group of workers with the same general exposure profile due to similar tasks, materials, processes, and manner of work — used to make sampling representative rather than exhaustive.
Because task variability, shift differences, and workforce volume make it easy for exposure groups to blur without consistent, task-tagged field data.
The specific task performed, work area/line, materials or processes involved, shift, and any prior monitoring data tied to that same task.
References
- "Quantitative Exposure Assessment Professional Judgment." U.S. Department of Energy Office of Scientific and Technical Information
- "IH 101: Introduction to IH Program Management Part 1 – SEGs." VelocityEHS, 8 Dec. 2025
- "Industrial Hygiene Exposure Assessments: Worst-Case vs. Random Sampling." Spear ✞ Lancaster LLC, 2 July 2024
- "Exposure Assessment." 3M Center for Respiratory Protection
- "Industrial Hygiene: Exposure Assessments." ISHN, 26 June 2019
Curious how myIH would fit your workflow?
Managing SEG mapping across a large manufacturing client? See how myIH structures task-level field data.
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