Systematic Assessments
Improving Professional Judgment with the Structured Deterministic Model 2.0
BY SUSAN ARNOLD, PULENG MOSHELE, MARK STENZEL, AND DANIEL DROLET
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Exposure assessment and risk assessment are critical elements of any occupational hygiene program. These core competencies are needed for much more than simply determining whether an exposure exceeds a regulatory limit. Accurate assessments depend on effective decision-making strategies: they must have the ability to reach the appropriate assessment decision related to the need for the assessment and clearly able to detect unacceptable exposures. Efficient strategies will minimize the number of exposure measurements or limit the costs of personnel, travel, and laboratory analysis.
Strategies that rely on professional judgment based on subjective inputs can introduce biases and inconsistencies that make reaching a correct decision much less likely. This has been demonstrated in many fields, perhaps most notably in aviation safety. It is also true for occupational hygiene, where, according to papers published in the Annals of Occupational Hygiene and the Journal of Occupational and Environmental Hygiene, making judgments about the acceptability of exposure using a nonsystematic and opaque approach leads to conclusions that are often wrong and tend to underestimate exposure and health risks.
Where robust personal exposure measurement data, representative of the true exposure distribution associated with the exposure group, are available (n greater than or equal to 5 and censoring is less than 80 percent), statistical methods can be employed to derive accurate exposure estimates that are more likely to produce correct conclusions. In contrast, decisions based on “eyeballing” the data from a small number of samples lead to incorrect conclusions because they do not adequately account for variability in exposure that occurs between workers over time, shift, and location.
Due to resource, technological, and temporal constraints, the vast majority of occupational hygiene exposure assessments are conducted without exposure measurements. Research shows that the most common number of exposure measurements is zero, followed by one measurement, and then two, and so on. For this reason, hygienists need alternate strategies to inform or aid their professional judgment that are accurate and precise.
One such strategy involves giving comparable weight to qualitative and quantitative measurements, modeling, and professional judgment. When a structured, systematic approach is used, informed by objective and relevant inputs, decision-making improves significantly. For example, algorithms that include only the critical inputs, filtering out extraneous detail that can distract us, have been shown to drive effective and efficient decision-making when they are applied in a structured manner, such as through use of a checklist. This premise is the heart of modern aviation safety. It is also central to exposure and risk assessment.
Algorithms for assessing occupational exposure and health risk have been developed and evaluated in studies with practicing and novice hygienists. Both groups showed significant improvement in their exposure assessment performance after applying a set of algorithms in a structured manner. These algorithms require only a few readily available inputs and are applicable to a wide range of exposure and risk scenarios involving volatile and semi-volatile pure chemicals and chemical mixtures, as well as fibers, particulates, and aerosols. The algorithms have been incorporated into an Excel-based tool called the Structured Deterministic Model (SDM), which is freely available from the University of Minnesota.
STRUCTURED DETERMINISTIC MODEL
The SDM is a visually informative tool that is structured like a checklist, guiding the user through its application step by step. Incorporating qualitative and quantitative inputs, its outputs include estimates of exposure concentration, predicted exposure control category (ECC), and health risk ranking (see Figure 1). It comprises two spreadsheets: the main SDM model and the SDM 2.0 support file. Algorithms are informed by the model’s embedded database with chemical-specific data. The user also has the option to set up a custom database for chemicals of interest, such as proprietary ingredients that are not in the SDM database. An expanded ECC framework that has a more granular categorization of exposures that exceed the OEL and divides the current ECC 2 into two categories, ECC 2 (exposure 0.1 to 0.25 times the OEL), and ECC 2.5 (exposure 0.25 to 0.5 times the OEL), is used to inform practical risk management measures. The results are captured on a single report page that shows both inputs and outputs and can be saved as a PDF.
The SDM model is driven by three key algorithms—the Rule of 10 (ROT), vapor hazard ratio (VHR), and particulate hazard ratio (PHR)—that are used in two separate checklists. Checklist 1 uses the ROT and VHR to assess exposures involving volatile and semi-volatile chemicals and chemical mixtures, while checklist 2 uses the PHR to assess exposures involving fibers, particulates, and aerosols.
Figure 1. Report tab of SDM 2.0. Tap or click on the figure to open a larger version in your browser.
Rule of 10
The ROT is based on chemical principles and has evolved over many years through empirical observations of exposure scenarios where quantitative measurements were available. The output of the ROT is a point estimate of the 95th percentile, calculated as a fraction of the saturated vapor concentration according to the level of control (for example, general or mechanical ventilation). The exposure estimate can be compared to the corresponding occupational exposure limit to determine compliance status and the most likely ECC. These ECCs are then linked to the chemical’s health hazard to establish a health risk ranking (HRR). Both ECCs and HRRs are used to trigger decisions, actions, levels of communication, and so on.
Vapor Hazard Ratio
The VHR measures a chemical’s potential to exceed its OEL and is expressed in the SDM as the ratio of a chemical’s vapor pressure (in millimeters of mercury, mm Hg) to its OEL (ppm). This form of the algorithm is slightly different from the VHR in the OSHA Technical Manual, which is expressed as the ratio of the chemical’s equilibrium concentration (ppm) to its OEL (ppm). But the outcomes are the same because the two VHRs differ by a constant: the VHROSHA is equal to the VHRSDM multiplied by 1,316. The constant cancels because the SDM uses relative VHRs in its calculations. In the SDM, vapor pressure is converted to concentration. The VHR table, which is based on empirical observations, links the VHR to the required level of control (RLC), which is the level deemed necessary to adequately control the airborne concentration.
The VHR is applicable to both pure chemicals and chemical mixtures. An additional step is required to estimate the adjusted vapor pressure of a mixture’s constituents. Depending on the mixture’s composition and the properties of its constituents, one of two algorithms can be used to estimate the adjusted vapor pressure: Raoult’s law, which states that the vapor pressure of a solvent above a solution is equal to the vapor pressure of the pure solvent at the same temperature; or Henry’s law, which states that the solubility of a gas in a liquid is directly proportional to the partial pressure of the gas above the liquid. The VHR is able to identify the “controlling compound” of a mixture, which is the chemical most likely to exceed its OEL. Adequately controlling this compound ensures adequate control of all other constituents.
Particulate Hazard Ratio
The PHR was adapted from the performance-based exposure control framework that was developed by Naumanet al. and published in the American Industrial Hygiene Association Journal in January 1996. Like the other algorithms, it has been refined empirically over many years, providing a link between the chemical’s OEL and the RLC. In the SDM, the output of the PHR is categorical, applying a decision logic to predict the likely ECC.
SDM Support File
Like the SDM model, the support file is an Excel spreadsheet. It helps the user understand how to apply each SDM checklist to real-world scenarios and interpret their output. The support file contains tools that provide guidance for each algorithm in the SDM and for the chemical laws they use to estimate the adjusted vapor pressure of chemical mixture constituents. These tools facilitate use of the SDM for scenarios under nonambient conditions. For example, the tool shown in Figure 2 can be used to calculate vapor pressures at elevated temperatures. Reference values for commonly used OELs and administrative help can also be found here.
Figure 2. SDM 2.0 support file vapor pressure calculator using the Antoine equation. Tap or click on the figure to open a larger version in your browser.
Applications of the SDM 2.0
As an example of how to operationalize checklist 1, which uses the ROT, consider the 2023 ACGIH revision of its benzene Threshold Limit Value from 0.5 ppm to 0.02 ppm. A key consideration when such changes occur is the impact they will have on operations. Are the existing controls capable of adequately controlling exposures, given the new OEL?
We can assess the impact of the new TLV on exposures associated with handling various fuels. EPA restricts the benzene yearly average concentration in gasoline to 0.62 percent by volume. Tasks related to handling gasoline generally require the use of vapor collection systems. Will handling less volatile fuels such as diesel result in benzene exposure above the 0.02 ppm TLV?
As shown in Table 1, diesel is a complex mixture of constituents. Using the ROT, we can assess the exposure to each of these constituents and to the overall mixture under various conditions. To do this, the user iteratively selects each chemical substance from the SDM database (or, if applicable, the custom database) to populate the required fields with the appropriate values. This information is transferred to the composition table, one chemical at a time, to build a recipe. When all the chemical information has been entered, the table is transferred to the analysis and report page. Using the drop-down menu on the report tab, the user selects the level of control currently in place. From this tab, the user can see the predicted concentration of each constituent across a range of control conditions, including the existing level of control. In this example, the SDM indicates that if the task involves filling, which causes vapors to be displaced, benzene exposures will likely exceed the TLV of 0.02 ppm. Even if the task does not involve filling, the exposure would likely be approximately 100 percent of the OEL and therefore unacceptable. Thus, the 2023 benzene TLV will likely require improved exposure controls for much of the fueling industry, even beyond gasoline.
Table 1. Composition of No. 1 Diesel
Tap or click on the table to open a larger version in your browser.
Source: “Hydrocarbon Characterization for Use in the Hydrocarbon Risk Calculator and Example Characterizations of Selected Alaskan Fuels: Technical Background Document and Recommendations” (PDF).
The VHR has many other practical exposure assessment applications, such as the introduction of a new chemical to an existing process, changes to processes that use existing chemicals, changes to batch operations that use the same equipment, and campaign operations where the same equipment is used to produce multiple products. Because the VHR is linked to the VHR table, a hygienist can use it to identify the RLC to adequately control exposures to a chemical or chemical mixture. For example, methyl ethyl ketone (MEK) has a vapor pressure of 86.7 mm Hg and an OEL of 200 ppm. Thus, the VHR is 0.42, which requires good general ventilation, approximately 6–12 air changes per hour. In contrast, methylene chloride has a vapor pressure of 430 mm Hg, an OEL of 25 ppm, and a VHR of 17.2, and thus requires good general ventilation as well as local exhaust ventilation at emission points.
One fundamental yet powerful application of this output is calibrating one’s expectation ahead of a site visit. This is especially helpful for situations where the hygienist is new to the site and at risk of being distracted by a deluge of new mental inputs. By identifying the type of control one should see for a particular work process based on the VHR, the hygienist is more likely to notice controls that do not match and may indicate a possible overexposure.
For exposure scenarios involving fibers, particulates, and aerosols, the PHR in checklist 2 is used. Operationalizing this algorithm requires the hygienist to select the range that captures the chemical’s OEL and identify the existing level of control. For example, OSHA’s permissible exposure limit for hexavalent chromium is 0.005 mg/m3 with a corresponding RLC of containment, based on the PHR. A decision logic is applied to this algorithm to predict the ECC. If the existing level of control is more stringent than the RLC dictated by the PHR, the tool predicts that the exposure is in ECC 1. If the existing level of control matches the RLC, the predicted ECC equals 2. If the existing level of control is less stringent than the RLC, the predicted ECC equals 4. In this example, use of hexavalent chromium indoors without containment will result in a predicted ECC equaling 4.
Experienced users can apply the SDM to an exposure scenario in just a few minutes, yielding a systematic, transparent, and defensible approach to assessing exposures for a wide range of scenarios. The power of this tool to inform professional judgment will be fully realized when it is applied in a Bayesian framework with the output of the SDM informing the prior judgment, which is subsequently updated with a dataset of representative exposure measurements to produce a posterior judgment. With the increasing affordability and reliability of direct-reading instruments, such an approach is becoming more feasible.
SUSAN ARNOLD, PhD, CIH, FAIHA, is an associate professor in the Division of Environmental Health Sciences at the University of Minnesota. At UMN, she serves as director of the Midwest Center for Occupational Health and Safety, and director of the Exposure Science and Sustainability Institute.
PULENG MOSHELE, MS, is pursuing a PhD in occupational hygiene at the University of Minnesota.
MARK STENZEL, MS, CIH (1978-2018), FAIHA, is owner of Exposure Assessment Applications LLC in Arlington, Virginia.
DANIEL DROLET, MS, is a consultant in the greater Montreal metropolitan area.
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Explore the SDM 2.0
The SDM 2.0 is available to download from the University of Minnesota website after a free license is executed. Users will find narrated, step-by-step instructions for using the SDM 2.0 and its support file on the website of ESSI, the Exposure Science and Sustainability Institute.
An AIHA University virtual PDC on the SDM will be offered from 12 to 3:45 p.m. ET on Nov. 5 and Nov. 7. The PDC will cover fundamental principles, functionality, and practical applications of the tool. Register on AIHA's website.
A recording of a webinar from July 2024, “Using the SDM 2.0 to Assess Real-World Situations,” is available through the AIHA Marketplace.
RESOURCES
AIHA: “Traditional vs. Statistical Exposure Assessment Approaches,” AIHA Connect 2024 (presentation by Michael Johnson and Carolyn Whitaker, May 2024).
Alaska Department of Environmental Conservation: “Hydrocarbon Characterization for Use in the Hydrocarbon Risk Calculator and Example Characterizations of Selected Alaskan Fuels: Technical Background Document and Recommendations” (PDF, September 2006).
American Industrial Hygiene Association Journal: “Performance-Based Exposure Control Limits for Pharmaceutical Active Ingredients” (January 1996).
Annals of Occupational Hygiene: “A Comparison of the β-Substitution Method and a Bayesian Approach for Handling Left-Censored Data” (January 2016).
Annals of Occupational Hygiene: “Effect of Training, Education, Professional Experience, and Need for Cognition on Accuracy of Exposure Assessment Decision-Making” (April 2012).
Annals of Occupational Hygiene: “Occupational Exposure Decisions: Can Limited Data Interpretation Training Help Improve Accuracy?” (June 2009).
Journal of Occupational and Environmental Hygiene: “Using Checklists and Algorithms to Improve Qualitative Exposure Judgment Accuracy” (March 2016).
The Synergist: “Judgment Day: How Accurate Are Industrial Hygienists’ Qualitative Exposure Assessments?” (January 2014).
University of Minnesota: “Examining the Predictive Potential of the SDM 2.0 for Chemical Concentrations and Exposure Control Categories through Comparative Analysis with Data from a Semi-Conductor Industry Manufacturer” (2024).