July 18, 2023 / Taylor Tarpey, Heather Lynch, and Andrew Maier

Workshop Examines Opportunities for the Use of Exposure Modeling in Occupational Risk Assessment

AIHA and the Foundation for Chemistry Research and Initiatives (FCRI) are hosting a series of workshops on occupational risk assessment. The third webinar in the Many Paths – One Goal series, which was held on June 29, 2023, brought together industrial hygiene practitioners, exposure modelers, data scientists, and risk assessors to discuss exposure model availability and selection practices as well as strategies to optimize the use of modeling tools in occupational risk assessment.

Exposure modeling is a valuable tool for estimating worker exposure if high-quality empirical exposure data are not available. Models offer flexibility and are typically more efficient than empirical approaches to estimating exposures. However, there are many nuances that must be considered when selecting and using exposure models in occupational risk assessment. These considerations include assessing model design, intended use (for example, internal assessments vs. regulatory submissions), data quality and availability for model inputs, and model approaches for optimization and evaluation.

The workshop included presentations that provided an introduction to the array of commonly used models, highlighting the varying level of detail and complexity available (such as moving from a basic well-mixed box model to a sophisticated computational fluid dynamics model). While there may be a relatively small fraction of occupational exposure modelers with deep expertise in specific models, many models are accessible, and outreach on this point might allay some IHs' concerns that models are too complex for routine IH practice. The presentation highlighted that increased application of models could greatly benefit the occupational risk assessment community.

The landscape of available exposure models and their application was addressed. A wide variety of models exists for estimating inhalation exposures in the workplace, and these models can be used to address a variety of purposes (prioritization, screening, risk assessment, and risk management). Several models are widely accepted by government agencies and have been assessed with published evaluations. For example, for many EPA assessments, the ChemSTEER model is a near-field screening tool that can be used to estimate exposure for a worker directly involved in or near a specific process. There are also models widely used under the European regulations for chemicals management that can be applied within an overall tiered risk assessment framework. The ECETOC Targeted Risk Assessment (TRA) is one such model. The TRA model uses a banding approach based on chemical properties, workplace processes, and degrees of exposure containment, to which exposure modifiers can be applied. In the European context, refined tools might be used, such as The Advanced Reach Tool (ART), which is a Bayesian mechanistic exposure model. These tools and others highlighted during the workshop, including AIHA's IHMod, provide an excellent toolkit. The key is understanding the intended use of each model, the model's design applicability to that use, and how the model output can be evaluated.

Speakers and discussants emphasized that models are used for a wide variety of purposes, and selection of the appropriate model is highly dependent on the reason the modeling is being done. For example, an IH may use a screening-level tool for a quick result or a full model with less refined information to give general exposure context. These models can also be used to fill data gaps, supplement biased or weak monitoring data, model exposures to occupational nonusers, and inform workplace guidance. A mechanistic model that calculates mass balance of the chemical in the work environment may be more accurate for a specific application but requires more initial data input (such as chemical source emission rates) to increase confidence that the outputs are accurate. Empirical data models can often address common representative chemical use scenarios but may be less accurate for a specific application than a mechanistic model. Regulatory risk assessors may have limited information on industry processes and typically have statutory or regulatory requirements or standards as well as associated deadlines; thus, models may need to be applicable across many uses and often rely on default inputs that are not scenario specific. For example, EPA uses models to evaluate new chemicals before they enter commerce in the U.S.; such chemicals generally lack exposure monitoring data. Initially, EPA conducts screening-level assessments to identify scenarios where further assessment (for example, more refined modeling) or exposure mitigation may be needed. Looking to the future, models built on large-scale sets of exposure data are being developed for screening risk assessment and prioritization. In addition, efforts to improve exposure judgment and connect this training to the ongoing development of model estimation is another burgeoning activity of AIHA technical work groups.

Unfortunately, not all risk assessment practitioners are aware of existing modeling tools, and many risk assessors perceive models as too complex. However, there are many types of models, and each has advantages, limitations, and different levels of uncertainty. Any IH risk assessor using models must consider the model's purpose (that is, whether the model applies to the scenario being evaluated), domain of applicability (whether the model is appropriate for the chemical being evaluated), confidence and uncertainty in the parameters, and the intended audience. There will always be uncertainty in any approach, which may over- or underestimate exposures, but when considering the purpose and context of the modeling exercise, the user can select and refine a model by balancing efficiency, specificity, and applicability. Further, models are one piece of the larger risk decision puzzle, complementing IH expertise, qualitative assessments, and monitoring data. When integrated with other sources of data and information, models can be a powerful tool. Wider education will facilitate increased uptake and advance the science of modeling. Already, numerous accessible textbooks, training courses, and resources are available from AIHA and other organizations.

Two workshops remain in the series. The next workshop, to be held September 21, 2023, will focus on dermal exposure assessment. None of the workshops will be recorded, so anyone interested in occupational risk assessment should plan to attend. Registration for the series is required, and registration is free. To register, visit the workshop series home page.

Taylor Tarpey, Heather Lynch, and Andrew Maier

Taylor Tarpey, MPH, is a health scientist with Stantec.

Heather Lynch, MPH, DABT, is managing health scientist at Stantec.

Andy Maier, PhD, CIH, DABT, FAIHA, is the director of the nonprofit Occupational Alliance for Risk Science initiative and a principal health scientist at Stantec.


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