The near field/far field model with constant application of chemical mass and exponentially decreasing emission of the mass applied

Estimating evaporation rates and contaminant air concentrations due to small spills of non-ideal aqueous organic solvent mixtures in a controlled environment

Although small spills of non-ideal organic solvent mixtures are ubiquitous undesirable events in occupational settings, the potential risk of exposure associated with such scenarios remains insufficiently investigated. This study aimed to examine the impact of non-ideality on evaporation rates and contaminant air concentrations resulting from small spills of organic solvent mixtures. Evaporation rate constants alphas (α) were experimentally measured for five pure solvents using a gravimetric approach during solvent evaporation tests designed to simulate small spills of solvents. Two equations were used for estimating contaminants' evaporation rates from aqueous mixtures assuming either ideal or non-ideal behavior based on the pure-chemical alpha values. A spill model also known as the well-mixed room model with exponentially decreasing emission rate was used to predict air concentrations during various spill scenarios based on the two sets of estimated evaporation rates. Model predictive performance was evaluated by comparing the estimates against real-time concentrations measured for the same scenarios. Evaluations for 12 binary non-ideal aqueous mixtures found that the estimated evaporation rates accounting for the correction by the activity coefficients of the solvents (median = 0.0318 min^-1) were higher than the evaporation rates estimated without the correction factor (median = 0.00632 min^-1). Model estimates using the corrected evaporation rates reasonably agreed with the measured values, with a median predicted peak concentrations-to-measured peak concentrations ratio of 0.92 (0.81 to 1.32) and a median difference between the predicted and the measured peak times of -5 min. By contrast, when the non-corrected evaporation rates were used, the median predicted peak concentrations-to-measured peak concentrations ratio was 0.31 (0.08 to 0.75) and the median difference between the predicted and the measured peak times was +33 min. Results from this study demonstrate the importance of considering the non-ideality effect for accurately estimating evaporation rates and contaminant air concentrations generated by solvent mixtures. Moreover, this study is a step further in improving knowledge of modeling exposures related to small spills of organic solvent mixtures.

COVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior

The coronavirus disease 2019 (COVID-19) Exposure Assessment Tool (CEAT) allows users to compare respiratory relative risk to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for various scenarios, providing understanding of how combinations of protective measures affect risk. CEAT incorporates mechanistic, stochastic, and epidemiological factors including the (i) emission rate of virus, (ii) viral aerosol degradation and removal, (iii) duration of activity/exposure, (iv) inhalation rates, (v) ventilation rates (indoors/outdoors), (vi) volume of indoor space, (vii) filtration, (viii) mask use and effectiveness, (ix) distance between people (taking into account both near-field and far-field effects of proximity), (x) group size, (xi) current infection rates by variant, (xii) prevalence of infection and immunity in the community, (xiii) vaccination rates, and (xiv) implementation of COVID-19 testing procedures. CEAT applied to published studies of COVID-19 transmission events demonstrates the model's accuracy. We also show how health and safety professionals at NASA Ames Research Center used CEAT to manage potential risks posed by SARS-CoV-2 exposures.

Predicting first-order evaporation rate constant alpha (α) from small spills of organic solvents in a controlled environment

Exposures to vapors generated by small spills of organic solvents are common in the occupational hygiene practice. In these scenarios, contaminant mass release is exponentially decreasing, driven by an evaporation rate constant alpha (α). Knowing α is fundamental for adequately modeling peak concentrations and/or short-term exposures that occur and for achieving efficient occupational risk analysis and management. The purpose of this study was to measure alpha experimentally using a gravimetric approach in a controlled environment during solvent evaporation tests designed to simulate small spills of solvents. The effects of several factors on α were evaluated. Equations based on regression models derived from the experimental data were proposed for predicting α. Predictions were externally validated against experimental data. A total of 183 tests was performed. Data analyses found that alpha (α) values increased with vapor pressure, spill surface area-to-spill volume ratio, and air speed across the spill. Larger α were associated with petri dish containers compared to watch glasses. Three regression models were created for predicting α. They had four variables in common, namely vapor pressure, molecular weight, air speed above the liquid, and surface tension of the liquid. The fifth variable was either spill volume, spill surface area, or spill surface area-to-spill volume ratio. The R² of the regression models were equal to 0.98. External validation showed mean relative errors of -32.9, -32.0, and -25.5%, respectively, with associated standard deviations of the relative errors of 17.7, 33.3, and 26.0%, respectively, and associated R² of 0.92, 0.65, and 0.87, respectively. The proposed equations can be used for estimating α in exposure scenarios similar to those evaluated in this study. Moreover, these models constitute a step further in the improvement of knowledge on estimating evaporation rates for small spills of organic solvents.

Model Predictions of Occupational Exposures to Diacetyl and 2,3-Pentanedione Emitted From Roasted Whole Bean and Ground Coffee: Influence of Roast Level and Physical Form on Specific Emission Rates

Roasted coffee emits hazardous volatile organic compounds including diacetyl and 2,3-pentanedione. Workers in non-flavored coffee roasting and packaging facilities might inhale diacetyl and 2,3-pentanedione from roasted coffee above occupational exposure limits depending on their work activities and proximity to the source of emissions. Objectives of this laboratory study were to: (1) investigate factors affecting specific emission rates (SERs) of diacetyl and 2,3-pentanedione from freshly roasted coffee, (2) explore the effect of time on SERs of coffee stored in sealed bags for 10-days, and (3) predict exposures to workers in hypothetical workplace scenarios. Two roast levels (light and dark) and three physical forms (whole bean, coarse ground, and fine ground) were investigated. Particle size for whole bean and ground coffee were analyzed using geometric mean of Feret diameter. Emitted chemicals were collected on thermal desorption tubes and quantified using mass spectrometry analysis. SERs developed here coupled with information from previous field surveys provided model input to estimate worker exposures during various activities using a probabilistic, near-field/far-field model. For freshly roasted coffee, mean SER of diacetyl and 2,3-pentantedione increased with decreasing particle size of the physical form (whole bean < coarse ground < fine ground) but was not consistent with roast levels. SERs from freshly roasted coffee increased with roast level for diacetyl but did not change for 2,3-pentanedione. Mean SERs were greatest for diacetyl at 3.60 mg kg−1 h−1 for dark, fine ground and for 2,3-pentanedione at 3.88 mg kg−1 h−1 for light, fine ground. For storage, SERs of whole bean remained constant while SERs of dark roast ground coffee decreased and light roast ground coffee increased. Modeling demonstrated that near-field exposures depend on proximity to the source, duration of exposure, and air velocities in the near-field further supporting previously reported chemical air measurements in coffee roasting and packaging facilities. Control of source emissions using local exhaust ventilation especially around grinding activities as well as modification of work practices could be used to reduce exposures in this workforce.

Covid-19 Exposure Assessment Tool (CEAT): Easy-to-use tool to quantify exposure based on airflow, group behavior, and infection prevalence in the community

The COVID-19 Exposure Assessment Tool (CEAT) allows users to compare respiratory relative risk to SARS-CoV-2 for various scenarios, providing understanding of how combinations of protective measures affect exposure, dose, and risk. CEAT incorporates mechanistic, stochastic and epidemiological factors including the: 1) emission rate of virus, 2) viral aerosol degradation and removal, 3) duration of activity/exposure, 4) inhalation rates, 5) ventilation rates (indoors/outdoors), 6) volume of indoor space, 7) filtration, 8) mask use and effectiveness, 9) distance between people, 10) group size, 11) current infection rates by variant, 12) prevalence of infection and immunity in the community, 13) vaccination rates of the community, and 14) implementation of COVID-19 testing procedures. Demonstration of CEAT, from published studies of COVID-19 transmission events, shows the model accurately predicts transmission. We also show how health and safety professionals at NASA Ames Research Center used CEAT to manage potential risks posed by SARS-CoV-2 exposures. Given its accuracy and flexibility, the wide use of CEAT will have a long lasting beneficial impact in managing both the current COVID-19 pandemic as well as a variety of other scenarios.

Exploring the Local Determinants of SARS-CoV-2 Transmission and Control via an Exposure-Based Model

A simulation model was developed aimed at assisting local public health authorities in exploring strategies for the suppression of SARS-CoV-2 transmission. A mechanistic modeling framework is utilized based on the daily airborne exposure of individuals defined in terms of inhaled viruses. Comparison of model outputs and observed data confirms that the model can generate realistic patterns of secondary cases. In the example investigated, the highest risk of being newly infected was among young adults, males, and people living in large households. Among risky occupations are food preparation and serving, personal care and service, sales, and production-related occupations. Results also show a pattern consistent with superspreading with 70% of initial cases who do not transmit at all while 13.4% of primary cases contribute 80% of secondary cases. The impacts of school closure and masking on the synthetic population are very small, but for students, school closure resulted in more time at home and increased secondary cases among them by over 25%. Requiring masks at schools decreased the case count by 80%. We conclude that the simulator can be useful in exploring local intervention scenarios and provides output useful in assessing the confidence that might be placed on its predictions.

Real-life measurement of size-fractionated aerosol concentration in a plethysmography box during the COVID-19 pandemic and estimation of the associated viral load

INTRODUCTION

There are concerns about pulmonary function tests (PFTs) being associated with aerosol generation and enhanced virus transmission. As a consequence, the number of PFTs was reduced significantly during the coronavirus disease 2019 pandemic. However, there are no robust data supporting this fear.

OBJECTIVES

To perform real-life measurement of aerosol concentrations in a PFT laboratory to monitor the concentration of particles near the patient, and to model the associated potential viral load.

METHODS

Two optical particle counters were used to sample the background concentration and the concentration of particles near the patient's mouth in a whole-body plethysmography box. Statistical evaluation of the measured particle concentration time series was completed. The particle exhalation rate was assessed based on the measured particle concentration data by applying the near-field/far-field theory. The number of exhaled viruses by an infected patient during the test was compared with the emission of viruses during quiet breathing and speaking.

RESULTS

Twenty-five patients were included in the study. Eighteen patients showed a significant increase in aerosol concentration [mean 1910 (standard deviation 593) particles/L]. Submicron particles dominated the number size distribution of the generated particles, but large particles represented a higher volume fraction in the generated particles compared with background. An average gene exhalation rate of 0.2/min was estimated from this data. This is one order of magnitude higher than the release rate for the same infected person during quiet breathing, and of the same order of magnitude as the release rate during normal speaking.

CONCLUSIONS

This study demonstrated that PFTs are aerosol-generating procedures. Based on these results, the moderate increase in viral load does not underpin stopping such examinations.

Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

One- and two-box models have been pointed out as useful tools for modelling indoor particle exposure. However, model performance still needs further testing if they are to be implemented as trustworthy tools for exposure assessment. The objective of this work is to evaluate the performance, applicability and reproducibility of one- and two-box models on real-world industrial scenarios. A study on filling of seven materials in three filling lines with different levels of energy and mitigation strategies was used. Inhalable and respirable mass concentrations were calculated with one- and two-box models. The continuous drop and rotating drum methods were used for emission rate calculation, and ranges from a one-at-a-time methodology were applied for local exhaust ventilation efficiency and inter-zonal air flows. When using both dustiness methods, large differences were observed for modelled inhalable concentrations but not for respirable, which showed the importance to study the linkage between dustiness and processes. Higher model accuracy (ratio modelled vs. measured concentrations 0.5-5) was obtained for the two- (87%) than the one-box model (53%). Large effects on modelled concentrations were seen when local exhausts ventilation and inter-zonal variations where parametrized in the models. However, a certain degree of variation (10-20%) seems acceptable, as similar conclusions are reached.

Modeling occupational exposure to solvent vapors using the Two-Zone (near-field/far-field) model: a literature review

The Two-Zone model is used in occupational hygiene to predict both near-field and far-field airborne contaminant concentrations. A literature review was carried out on 21 scientific publications in which the Two-Zone model was used to assess occupational exposure to solvent vapors. Data on exposure scenarios, solvents, generation/emission rates, near- and far-field parameters, and model performance were collected and analyzed. Over the 24 exposure scenarios identified, 18 were evaluated under controlled conditions, 5 under normal workplace activities, and 1 was reported based on literature data. The scenarios involved a variety of tasks which consisted, mostly, of cleaning metal parts, spraying solvents onto surfaces, spilling liquids, and filling containers with volatile substances. Twenty-eight different solvents were modeled and the most commonly tested were benzene, toluene, and acetone. Emission rates were considered constant in 16 scenarios, exponentially decreasing in 6 scenarios, and intermittent in 2 scenarios. Four-hundred-and-forty-six (446) predicted-to-measured concentration ratios were calculated across the 21 studies; 441 were obtained in controlled conditions, 4 under normal workplace activities, and 1 was calculated based on the literature data. For controlled studies, the Two-Zone model predictive performance was within a factor of 0.3-3.7 times the measured concentrations with 93% of the values between 0.5 and 2. The model overestimated the measured concentrations in 63% of the evaluations. The median predicted concentration for the near-field was 1.38 vs. 1.02 for the far-field. Results suggest that the model might be a useful tool for predicting occupational exposure to vapors of solvents by providing a conservative approach. Harmonization in model testing strategies and data presentation is needed in future studies to improve the assessment of the predictability of the Two-Zone model. Moreover, this review has provided a database of exposure scenarios, input parameter values, and model predictive performances which can be useful to occupational hygienists in their future modeling activities.

Estimating the time-varying generation rate of acetic acid from an all-purpose floor cleaner

Understanding the relationship between consumer product use and risk of adverse health outcomes facilitates appropriate risk management and product stewardship. A preferred method for estimating the systemic and respiratory tract exposure and dose tailored to cleaning products use has been proposed, refining previously issued exposure guidance. Consistent with other exposure and risk-assessment frameworks, it is dependent upon high-quality exposure determinant data that also serve as model inputs. However, as publicly available exposure determinant data are scarce, the risk assessor is left with the option of estimating determinants such as the generation rate or employing empirical methods to estimate them. When the exposure scenario involves a chemical mixture, estimating the generation rate may not be feasible. We present an approach for estimating the time-varying generation rate of an aqueous acetic acid mixture representative of the base formulation for many consumer and DIY cleaning products that was previously assessed in a screening-level assessment. The approach involved measuring the evaporation rate for a reasonable worst-case scenario under controlled conditions. Knowing the mass applied, a time-varying generation rate was estimated. To evaluate its portability, a field study was conducted in a home where measurements were collected in an all-purpose room with the exterior door open (Room 1) and closed (Room 2), and a bathroom (Room 3) using a portable Fourier Transform Infrared (FTIR) spectrophotometer. Acetic acid concentrations were modeled using two common indoor air models, the Well Mixed Room model. Measured and modeled acetic acid concentrations were compared, with the WMR 95% confidence intervals encompassing measured concentrations for all three rooms, supporting the utility of the approach used and portability of the generation rate derived from it.

Modeling of High Nanoparticle Exposure in an Indoor Industrial Scenario with a One-Box Model

Mass balance models have proved to be effective tools for exposure prediction in occupational settings. However, they are still not extensively tested in real-world scenarios, or for particle number concentrations. An industrial scenario characterized by high emissions of unintentionally-generated nanoparticles (NP) was selected to assess the performance of a one-box model. Worker exposure to NPs due to thermal spraying was monitored, and two methods were used to calculate emission rates: the convolution theorem, and the cyclic steady state equation. Monitored concentrations ranged between 4.2 × 104-2.5 × 105 cm-3. Estimated emission rates were comparable with both methods: 1.4 × 1011-1.2 × 1013 min-1 (convolution) and 1.3 × 1012-1.4 × 1013 min-1 (cyclic steady state). Modeled concentrations were 1.4-6 × 104 cm-3 (convolution) and 1.7-7.1 × 104 cm-3 (cyclic steady state). Results indicated a clear underestimation of measured particle concentrations, with ratios modeled/measured between 0.2-0.7. While both model parametrizations provided similar results on average, using convolution emission rates improved performance on a case-by-case basis. Thus, using cyclic steady state emission rates would be advisable for preliminary risk assessment, while for more precise results, the convolution theorem would be a better option. Results show that one-box models may be useful tools for preliminary risk assessment in occupational settings when room air is well mixed.

Testing the performance of one and two box models as tools for risk assessment of particle exposure during packing of inorganic fertilizer

Modelling of particle exposure is a useful tool for preliminary exposure assessment in workplaces with low and high exposure concentrations. However, actual exposure measurements are needed to assess models reliability. Worker exposure was monitored during packing of an inorganic granulate fertilizer at industrial scale using small and big bags. Particle concentrations were modelled with one and two box models, where the emission source was estimated with the fertilizer's dustiness index. The exposure levels were used to calculate inhaled dose rates and test accuracy of the exposure modellings. The particle number concentrations were measured from worker area by using a mobility and optical particle sizer which were used to calculate surface area and mass concentrations. The concentrations in the worker area during pre-activity ranged 63,797-81,073 cm^-3, 4.6 × 10⁶ to 7.5 × 10⁶ μm² cm^-3, and 354 to 634 μg m^-3 (respirable mass fraction) and during packing 50,300 to 85,949 cm^-3, 4.3 × 10⁶ to 7.6 × 10⁶ μm² cm^-3, and 279 to 668 μg m^-3 (respirable mass fraction). Thus, the packing process did not significantly increase the exposure levels. Chemical exposure was also under control based on REACH standards. The particle surface area deposition rate in respiratory tract was up to 7.6 × 10⁶ μm² min^-1 during packing, with 52%-61% of deposition occurring in the alveolar region. Ratios of the modelled and measured concentrations were 0.98 ± 0.19 and 0.84 ± 0.12 for small and big bags, respectively, when using the one box model, and 0.88 ± 0.25 and 0.82 ± 0.12, when using the two box model. The modelling precision improved for both models when outdoor particle concentrations were included. This study shows that exposure concentrations in a low emission industrial scenario, e.g. during packing of a fertilizer, can be predicted with a reasonable accuracy by using the concept of dustiness and mass balance models.

Interzonal airflow rates for use in near-field far-field workplace concentration modeling

Interzonal air flow rates (β) for a workspace above a table were measured in 12 indoor air spaces using an experimental apparatus simulating a vapor release into an occupied near zone. The near field was modeled as a 0.32 m³ rectangular cube volume 0.60 m high above the 0.60 m × 0.90 m table. A total of 74 experimental measurements of β were made. The apparatus consisted of photoionization detectors measuring concentrations of acetone around an evaporating liquid surface with a robot arm simulating worker motion in the near field. The vapor release rate and the resulting concentrations were used in a near-field far-field (NF-FF) model to calculate β. Measures of mixing within the near-field supported the assumption of the NF-FF model that the near field is well-mixed. Measured values of β ranged from 0.4-19 m³/min with an average of 4.8 m³/min. This corresponds to 1.2-59 air changes per minute in the near field and an average of 15 air changes per minute. The values of β were log normally distributed with a geometric mean of 3.4 m³/min and a geometric standard deviation of 2.3. The 95% confidence interval on the geometric mean of β was 2.8-4.2 m³/min. The product of the random air speed in the room and one half of the near-field free surface area was shown to be a good method of determining β. There was a slight correlation seen between room volume and β, but the effect size was small. Room air change rate was not found to be correlated with β. The observed distribution of β will be helpful in selecting values for interzonal airflow rate in NF-FF modeling of worker exposures.

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.