Micro-environmental factors impact breathing zone exposures: A simulated petrochemical manufacturing facility task.

This study investigates the impact of micro-environmental factors on worker breathing zone exposure levels in petrochemical facilities. A laboratory simulation study evaluated near-field exposure to methane for a typical maintenance task. Individual and combinations of micro-environmental factors significantly affected methane exposure. Airflow direction and speed were significant determinants of exposure concentration reduction. A side airflow direction at medium to high speed produced the lowest gas concentration in the breathing zone. Worker body orientation relative to the methane emission point was also a critical factor affecting gas concentration in the worker's breathing zone. The study provides insights into how variations in airflow and small changes in position impact near-field exposures for petrochemical tasks, guiding industrial hygiene professionals' training on qualitative exposure estimation and providing input for near-field exposure modeling to guide quantitative exposure and risk assessment.

Comparing Antoine parameter sources for accurate vapor pressure prediction across a range of temperatures.

Determining the vapor pressure of a substance at the relevant process temperature is a key component in conducting an exposure assessment to ascertain worker exposure. However, vapor pressure data at various temperatures relevant to the work environment is not readily available for many chemicals. The Antoine equation is a mathematical expression that relates temperature and vapor pressure. The objective of this analysis was to compare Antoine parameter data from 3 independent data sources; Hansen, Yaws, and Custom data and identify the source that generates the most accurate vapor pressure values with the least bias, relative to the referent data set from the CRC Handbook of Chemistry and Physics. Temperatures predicted from 3 different Antoine sources across a range of vapor pressures for 59 chemicals are compared to the reference source. The results show that temperatures predicted using Antoine parameters from the 3 sources are not statistically significantly different, indicating that all 3 sources could be useful. However, the Yaws dataset will be used in the SDM 2.0 because the data is readily available and robust.

Accuracy of professional judgments for dermal exposure assessment using deterministic models.

The accuracy of exposure judgments, particularly for scenarios where only qualitative information is available or a systematic approach is not used, has been evaluated and shown to have a relatively low level of accuracy. This is particularly true for dermal exposures, where less information is generally available compared to inhalation exposures. Relatively few quantitative validation efforts have been performed for scenarios where dermal exposures are of interest. In this study, a series of dermal exposure judgments were collected from 90 volunteer U.S. occupational health practitioners in a workshop format to assess the accuracy of their judgments for three specific scenarios. Accuracy was defined as the ability of the participants to identify the correct reference exposure category, as defined by the quantitative exposure banding categories utilized by the American Industrial Hygiene Association (AIHA®). The participants received progressively additional information and training regarding dermal exposure assessments and scenario-specific information during the workshop, and the relative accuracy of their category judgments over time was compared. The results of the study indicated that despite substantial education and training in exposure assessment generally, the practitioners had very little experience in performing dermal exposure assessments and a low level of comfort in performing these assessments. Further, contrary to studies of practitioners performing inhalation exposure assessments demonstrating a trend toward underestimating exposures, participants in this study consistently overestimated the potential for dermal exposure without quantitative data specific to the scenario of interest. Finally, it was found that participants were able to identify the reference or "true" category of dermal exposure acceptability when provided with relevant, scenario-specific dermal and/or surface-loading data for use in the assessment process. These results support the need for additional training and education of practitioners in performing dermal exposure assessments. A closer analysis of default loading values used in dermal exposure assessments to evaluate their accuracy relative to real-world or measured dermal loading values, along with consistent improvements in current dermal models, is also needed.

Assessing Exposures from the Deepwater Horizon Oil Spill Response and Clean-up.

The GuLF Study is investigating adverse health effects from work on the response and clean-up after the Deepwater Horizon explosion and oil release. An essential and necessary component of that study was the exposure assessment. Bayesian statistical methods and over 135 000 measurements of total hydrocarbons (THC), benzene, ethylbenzene, toluene, xylene, and n-hexane (BTEX-H) were used to estimate inhalation exposures to these chemicals for >3400 exposure groups (EGs) formed from three exposure determinants: job/activity/task, location, and time period. Recognized deterministic models were used to estimate airborne exposures to particulate matter sized 2.5 µm or less (PM2.5) and dispersant aerosols and vapors. Dermal exposures were estimated for these same oil-related substances using a model modified especially for this study from a previously published model. Exposures to oil mist were assessed using professional judgment. Estimated daily THC arithmetic means (AMs) were in the low ppm range (<25 ppm), whereas BTEX-H exposures estimates were generally <1000 ppb. Potential 1-h PM2.5 air concentrations experienced by some workers may have been as high as 550 µg m-3. Dispersant aerosol air concentrations were very low (maximum predicted 1-h concentrations were generally <50 µg m-3), but vapor concentrations may have exceeded occupational exposure excursion guidelines for 2-butoxyethanol under certain circumstances. The daily AMs of dermal exposure estimates showed large contrasts among the study participants. The estimates are being used to evaluate exposure-response relationships in the GuLF Study.

Modeled Air Pollution from In Situ Burning and Flaring of Oil and Gas Released Following the Deepwater Horizon Disaster.

The GuLF STUDY, initiated by the National Institute of Environmental Health Sciences, is investigating the health effects among workers involved in the oil spill response and clean-up (OSRC) after the Deepwater Horizon (DWH) explosion in April 2010 in the Gulf of Mexico. Clean-up included in situ burning of oil on the water surface and flaring of gas and oil captured near the seabed and brought to the surface. We estimated emissions of PM2.5 and related pollutants resulting from these activities, as well as from engines of vessels working on the OSRC. PM2.5 emissions ranged from 30 to 1.33e6 kg per day and were generally uniform over time for the flares but highly episodic for the in situ burns. Hourly emissions from each source on every burn/flare day were used as inputs to the AERMOD model to develop average and maximum concentrations for 1-, 12-, and 24-h time periods. The highest predicted 24-h average concentrations sometimes exceeded 5000 µg m-3 in the first 500 m downwind of flaring and reached 71 µg m-3 within a kilometer of some in situ burns. Beyond 40 km from the DWH site, plumes appeared to be well mixed, and the predicted 24-h average concentrations from the flares and in situ burns were similar, usually below 10 µg m-3. Structured averaging of model output gave potential PM2.5 exposure estimates for OSRC workers located in various areas across the Gulf. Workers located nearest the wellhead (hot zone/source workers) were estimated to have a potential maximum 12-h exposure of 97 µg m-3 over the 2-month flaring period. The potential maximum 12-h exposure for workers who participated in in situ burns was estimated at 10 µg m-3 over the ~3-month burn period. The results suggest that burning of oil and gas during the DWH clean-up may have resulted in PM2.5 concentrations substantially above the U.S. National Ambient Air Quality Standard for PM2.5 (24-h average = 35 µg m-3). These results are being used to investigate possible adverse health effects in the GuLF STUDY epidemiologic analysis of PM2.5 exposures.

Estimation of Airborne Vapor Concentrations of Oil Dispersants COREXIT™ EC9527A and EC9500A, Volatile Components Associated with the Deepwater Horizon Oil Spill Response and Clean-up Operations.

The Deepwater Horizon (DWH) drilling unit explosion above the Macondo oil well on 20 April 2010 caused the release of approximately 4.9 million barrels (779 million L) of oil into the Gulf of Mexico. As part of a larger spill response and clean-up effort, approximately 1.84 million gallons (6.81 million L) of chemical dispersants COREXIT™ EC9500A and COREXIT™ EC9527A were applied to the resultant oil slicks through spraying on the water surface by plane and by vessel and through injection at the release source near the seabed. The GuLF STUDY is investigating the health effects of workers involved in the oil spill response and clean-up after the DWH explosion, and estimates of possible exposure to chemical dispersants were needed. Exposures were estimated to the volatile components of COREXIT™ EC9500A [petroleum distillates, hydrotreated light, and propylene glycol (PG)] and of COREXIT™ EC9527A [2-butoxyethanol (2-BE) and PG] using two of AIHA IHMOD2.0© mathematical modeling tools along with the dispersants' chemical and physical properties. Monte Carlo simulations were used to reflect uncertainty in input parameters with both the two-box, constant emission model and the near and mid field plume model for indoor and outdoor activities, respectively. Possible exposure scenarios considered various evaporation rates, sizes of the dispersant pool, wind speeds, and ventilation rates. For the two-box model, mean near field exposure estimates to 2-BE ranged from 0.9 to 5.7 ppm, while mean far field estimated exposures ranged from 0.3 to 3.5 ppm. Estimates of mean near field plume model exposures ranged from 0.01 to 3.7 ppm at 2.5 ft from the source, and <0.01 to 0.3 ppm at 10 ft from the source. Estimated exposures to PG were approximately 10% of the calculated 2-BE exposures and exposures to petroleum distillates about 40% higher than the 2-BE estimates. Results indicate that compared with current occupational exposure guidelines, overexposure to petroleum distillates and PG probably did not occur in our study, but under some conditions, for short periods, exposure to 2-BE may have exceeded the limits for peak exposures. These estimates were developed for use in job-exposure matrices to estimate exposures of workers having contact with dispersant vapors for the GuLF STUDY.

Setting surface wipe limits for skin sensitizers.

Guidance for managing potential dermal exposures has historically been qualitative in nature, for example, in the form of a DSEN notation. We propose a method that can provide quantitative guidance on how to establish and use surface wipe limits for skin sensitizers. The murine local lymph node assay (LLNA) is a validated test that not only identifies potential skin sensitizers but also provides an effective concentration (EC3) value. This provides quantitative dose-response information on induction of skin sensitization that permits estimates of sensitization thresholds and potency. Building upon the previously established correlation between LLNA EC3 values and human repeat insult patch testing no-effect levels, we present a quantitative method for setting surface wipe guidelines using the LLNA EC3. These limits can be used to assign compounds to occupational exposure bands and provide handling guidance for skin sensitizers of varying potency, supporting both exposure assessment and control strategies. A table is included that suggests a band of reasonable surface wipe limits (mg/100 cm2) for potentially all chemical sensitizers. When used in conjunction with a comprehensive industrial hygiene program that includes hazard communication, engineering controls, and personal protective equipment, skin exposure and consequent skin sensitization risks in the workplace can be minimized.

Source specific exposure and risk assessment for indoor aerosols.

Poor air quality is a leading contributor to the global disease burden and total number of deaths worldwide. Humans spend most of their time in built environments where the majority of the inhalation exposure occurs. Indoor Air Quality (IAQ) is challenged by outdoor air pollution entering indoors through ventilation and infiltration and by indoor emission sources. The aim of this study was to understand the current knowledge level and gaps regarding effective approaches to improve IAQ. Emission regulations currently focus on outdoor emissions, whereas quantitative understanding of emissions from indoor sources is generally lacking. Therefore, specific indoor sources need to be identified, characterized, and quantified according to their environmental and human health impact. The emission sources should be stored in terms of relevant metrics and statistics in an easily accessible format that is applicable for source specific exposure assessment by using mathematical mass balance modelings. This forms a foundation for comprehensive risk assessment and efficient interventions. For such a general exposure assessment model we need 1) systematic methods for indoor aerosol emission source assessment, 2) source emission documentation in terms of relevant a) aerosol metrics and b) biological metrics, 3) default model parameterization for predictive exposure modeling, 4) other needs related to aerosol characterization techniques and modeling methods. Such a general exposure assessment model can be applicable for private, public, and occupational indoor exposure assessment, making it a valuable tool for public health professionals, product safety designers, industrial hygienists, building scientists, and environmental consultants working in the field of IAQ and health.

Evaluation of the well mixed room and near-field far-field models in occupational settings.

Drawing appropriate conclusions about a scenario for which the exposure is truly unacceptable drives appropriate exposure and risk management, and protects the health and safety of those individuals. To ensure the vast majority of these decisions are accurate, these decisions must be based upon proven approaches and tools. When these decisions are based solely on professional judgment guided by subjective inputs, however, they are more than likely wrong, and biased, underestimating the true exposure. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. Possible reasons are a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research is the second phase of work building upon the robust evaluation of the Well Mixed Room (WMR) and Near Field Far Field (NF-FF) models under controlled conditions in an exposure chamber, [5] in which good concordance between measured and modeled airborne concentrations of three solvents under a range of conditions was observed. In real world environments, the opportunity to control environmental conditions is limited and measuring the model inputs directly can be challenging; in many cases, model inputs must be estimated indirectly without measurement. These circumstances contribute to increased model input uncertainty and consequent uncertainty in the output. Field studies of model performance directly inform us about how well models predict exposures given these practical limitations, and are, therefore, an important component of model evaluation. The evaluation included ten diverse contaminant-exposure scenarios at five workplaces involving six different contaminants. A database of parameter values and measured and modeled exposures was developed and will be useful for modeling similar scenarios in the future.

Evaluating well-mixed room and near-field-far-field model performance under highly controlled conditions.

Exposure judgments made without personal exposure data and based instead on subjective inputs tend to underestimate exposure, with exposure judgment accuracy not significantly more accurate than random chance. Therefore, objective inputs that contribute to more accurate decision making are needed. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. This may be attributable to a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research addresses the need to systematically evaluate two widely applicable models, the Well-Mixed Room (WMR) and Near-Field-Far-Field (NF-FF) models. The evaluation, conducted under highly controlled conditions in an exposure chamber, allowed for model inputs to be accurately measured and controlled, generating over 800 pairs of high quality measured and modeled exposure estimates. By varying conditions in the chamber one at a time, model performance across a range of conditions was evaluated using two sets of criteria: the ASTM Standard 5157 and the AIHA Exposure Assessment categorical criteria. Model performance for the WMR model was excellent, with ASTM performance criteria met for 88-97% of the pairs across the three chemicals used in the study, and 96% categorical agreement observed. Model performance for the NF-FF model, impacted somewhat by the size of the chamber was nevertheless good to excellent. NF modeled estimates met modified ASTM criteria for 67-84% of the pairs while 69-91% of FF modeled estimates met these criteria. Categorical agreement was observed for 72% and 96% of NF and FF pairs, respectively. These results support the use of the WMR and NF-FF models in guiding decision making towards improving exposure judgment accuracy.

Using checklists and algorithms to improve qualitative exposure judgment accuracy.

Most exposure assessments are conducted without the aid of robust personal exposure data and are based instead on qualitative inputs such as education and experience, training, documentation on the process chemicals, tasks and equipment, and other information. Qualitative assessments determine whether there is any follow-up, and influence the type that occurs, such as quantitative sampling, worker training, and implementing exposure and risk management measures. Accurate qualitative exposure judgments ensure appropriate follow-up that in turn ensures appropriate exposure management. Studies suggest that qualitative judgment accuracy is low. A qualitative exposure assessment Checklist tool was developed to guide the application of a set of heuristics to aid decision making. Practicing hygienists (n = 39) and novice industrial hygienists (n = 8) were recruited for a study evaluating the influence of the Checklist on exposure judgment accuracy. Participants generated 85 pre-training judgments and 195 Checklist-guided judgments. Pre-training judgment accuracy was low (33%) and not statistically significantly different from random chance. A tendency for IHs to underestimate the true exposure was observed. Exposure judgment accuracy improved significantly (p <0.001) to 63% when aided by the Checklist. Qualitative judgments guided by the Checklist tool were categorically accurate or over-estimated the true exposure by one category 70% of the time. The overall magnitude of exposure judgment precision also improved following training. Fleiss' κ, evaluating inter-rater agreement between novice assessors was fair to moderate (κ = 0.39). Cohen's weighted and unweighted κ were good to excellent for novice (0.77 and 0.80) and practicing IHs (0.73 and 0.89), respectively. Checklist judgment accuracy was similar to quantitative exposure judgment accuracy observed in studies of similar design using personal exposure measurements, suggesting that the tool could be useful in developing informed priors and further demonstrating its usefulness in producing accurate qualitative exposure judgments.

Influence of parameter values and variances and algorithm architecture in ConsExpo model on modeled exposures.

This study evaluated the influence of parameter values and variances and model architecture on modeled exposures, and identified important data gaps that influence lack-of-knowledge-related uncertainty, using Consexpo 4.1 as an illustrative case study. Understanding the influential determinants in exposure estimates enables more informed and appropriate use of this model and the resulting exposure estimates. In exploring the influence of parameter placement in an algorithm and of the values and variances chosen to characterize the parameters within ConsExpo, "sensitive" and "important" parameters were identified: product amount, weight fraction, exposure duration, exposure time, and ventilation rate were deemed "important," or "always sensitive." With this awareness, exposure assessors can strategically focus on acquiring the most robust estimates for these parameters. ConsExpo relies predominantly on three algorithms to assess the default scenarios: inhalation vapors evaporation equation using the Langmuir mass transfer, the dermal instant application with diffusion through the skin, and the oral ingestion by direct uptake algorithm. These algorithms, which do not necessarily render health conservative estimates, account for 87, 89 and 59% of the inhalation, dermal and oral default scenario assessments,respectively, according them greater influence relative to the less frequently used algorithms. Default data provided in ConsExpo may be useful to initiate assessments, but are insufficient for determining exposure acceptability or setting policy, as parameters defined by highly uncertain values produce biased estimates that may not be health conservative. Furthermore, this lack-of-knowledge uncertainty makes the magnitude of this bias uncertain. Significant data gaps persist for product amount, exposure time, and exposure duration. These "important" parameters exert influence in requiring broad values and variances to account for their uncertainty. Prioritizing them for research will not only help fill a large and influential knowledge gap, but also lead to more accurate assessments and thus refine the studies informing policy decisions.

Modeling mixtures resulting from concurrent exposures to multiple sources.

There is a growing recognition of the need to identify when exposures to specific combinations of chemicals result in toxicological effects of concern. In order to meet this need new tools are required to evaluate the doses of multiple chemicals that occur from the concurrent exposures to multiple sources of the chemicals. Limitations associated with the traditional approach for exposure modeling (source-to-dose models) have led to the development of a new approach that focuses on the person. These Person Oriented Models (POMs) use available data on personal characteristics that are statistically representative of the population receiving an exposure. Once the person's characteristics are defined, the information is used to model the probability of being exposed during a particular period of time. This process is repeated for different time periods and for hundreds or thousands of persons to produce a description of longitudinal exposures across a population. This approach allows the modeling of route-specific doses from multiple concurrent exposures; allows the modeling of doses from time varying exposures across, individuals, and provides a basis for modeling the person-related characteristics in subsequent steps in the process of assessing risks from mixtures.