Estimating methyl bromide exposure due to offgassing from fumigated commodities

Chemical pollutants in closed environments of transportation and storage of non-dangerous goods - Insufficient legislation, low awareness, and poor practice in Hungary

Several chemical pollutants can accumulate within the closed environments of transportation and storage. Pollutants are mainly residues of pesticides, volatile organic compounds and components of diesel exhaust. The study objectives were to (i) review the regulations relevant to occupational chemical exposures in closed environments of inland transportation and storage; and (ii) explore the practice of preventing these exposures. A systematic search and content analysis of international and Hungarian nation legal documents were carried out. In addition, semi-structured interviews with occupational health and safety (OHS) professionals and warehouse managers were conducted. Analysis of legal documents highlighted the lack of explicit regulations on the investigated problem. The 21 interviews revealed that the participants had limited knowledge about the pollutants; they deemed chemical exposure rare and related health effects negligible. The revealed limitations indicate that this field should be more specifically regulated and OHS professionals should be better informed about these workplace hazards.

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.

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.

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

The near field/far field (NF/FF) model is a contaminant dispersion construct that permits making airborne contaminant exposure estimates for an individual located close to an emission source. In the present analysis, chemical emission involves a constant mass rate of chemical application to surfaces, denoted I (mg/min), and an exponentially decreasing rate of emission of the chemical from the surfaces with rate constant α (min(-1)). The time-dependent emission rate function is: G(t), mg/min = I - I exp(- αt), where time t is in minutes. The exact time-dependent equations for the contaminant concentration in the NF and the FF are presented. These equations are used to revise a previous analysis of a study in which a penetrant liquid containing benzene was applied to parts on a work table in a test room. The previous analysis assumed that the benzene was applied as a bolus at the start of a 15-min use period, whereas the present analysis assumes the same total benzene mass was applied at a uniform rate over the 15-min use period, but with the same evaporation rate constant α. The new G(t) function leads to a lower 15-min time weighted average NF benzene concentration that better matches the experimental data. It is also shown that the exact equation for the NF concentration is well approximated by combining two well-mixed single-zone equations. The approximation method is mathematically simpler and obviates the need to derive the exact NF equation.

The role of exposure reconstruction in occupational human health risk assessment: current methods and a recommended framework

Exposure reconstruction for substances of interest to human health is a process that has been used, with various levels of sophistication, as far back as the 1930s. The importance of robust and high-quality exposure reconstruction has been recognized by many researchers. It has been noted that misclassification of reconstructed exposures is relatively common and can result in potentially significant effects on the conclusions of a human health risk assessment or epidemiology study. In this analysis, a review of the key exposure reconstruction approaches described in over 400 papers in the peer-reviewed literature is presented. These approaches have been critically evaluated and classified according to quantitative, semiquantitative, and qualitative approaches. Our analysis indicates that much can still be done to improve the overall quality and consistency of exposure reconstructions and that a systematic framework would help to standardize the exposure reconstruction process in the future. The seven recommended steps in the exposure reconstruction process include identifying the goals of the reconstruction, organizing and ranking the available data, identifying key data gaps, selecting the best information sources and methodology for the reconstruction, incorporating probabilistic methods into the reconstruction, conducting an uncertainty analysis, and validating the results of the reconstruction. Influential emerging techniques, such as Bayesian data analysis, are highlighted. Important issues that will likely influence the conduct of exposure reconstruction into the future include improving statistical analysis methods, addressing the issue of chemical mixtures, evaluating aggregate exposures, and ensuring transparency with respect to variability and uncertainty in the reconstruction effort.

The use of multizone models to estimate an airborne chemical contaminant generation and decay profile: occupational exposures of hairdressers to vinyl chloride in hairspray during the 1960s and 1970s

Vinyl chloride (VC) was used as a propellant in a limited percentage of aerosol hairspray products in the United States from approximately 1967 to 1973. The question has arisen whether occupational exposures of hairdressers to VC-containing hairsprays in hair salons were sufficient to increase the risk for developing hepatic angiosarcoma (HAS). Transient two-zone and steady-state three-zone models were used to estimate the historical airborne concentration of VC for individual hairdressers using hairspray as well as estimated contributions from other hairdressers in the same salon. Concentrations of VC were modeled for small, medium, and large salons, as well as a representative home salon. Model inputs were determined using published literature, and variability in these inputs was also considered using Monte Carlo techniques. The 95th percentile for the daily time-weighted average exposure for small, medium, and large salons, assuming a market-share fraction of VC-containing hairspray use from the Monte Carlo analysis, was about 0.3 ppm, and for the home salon scenario was 0.1 ppm. The 95th percentile value for the cumulative lifetime exposure of the hairdressers was 2.8 ppm-years for the home salon scenario and 2.0 ppm-years for the small, medium, and large salon scenarios. If using the assumption that all hairsprays used in a salon contained VC, the 95th percentile of the theoretical lifetime cumulative dose was estimated to be 52-79 ppm-years. Estimated lifetime doses were all below the threshold dose for HAS of about 300 to 500 ppm-years reported in the published epidemiology literature.

Evaluating indoor exposure modeling alternatives for LCA: a case study in the vehicle repair industry

We evaluated three exposure models with data obtained from measurements among workers who use "aerosol" solvent products in the vehicle repair industry and with field experiments using these products to simulate the same exposure conditions. The three exposure models were the (1) homogeneously mixed-one-box model, (2) multizone model, and (3) eddy-diffusion model. Temporally differentiated real-time breathing zone volatile organic compound (VOC) concentration measurements, integrated far-field area samples, and simulated experiments were used in estimating parameters, such as emission rates, diffusivity, and near-field dimensions. We assessed differences in model input requirements and their efficacy for predictive modeling. The One-box model was not able to resemble the temporal profile of exposure concentrations, but it performed well concerning time-weighted exposure over extended time periods. However, this model required an adjustment for spatial concentration gradients. Multizone models and diffusion-models may solve this problem. However, we found that the reliable use of both these models requires extensive field data to appropriately define pivotal parameters such as diffusivity or near-field dimensions. We conclude that it is difficult to apply these models for predicting VOC exposures in the workplace. However, for comparative exposure scenarios in life-cycle assessment they may be useful.

Bayesian modeling of exposure and airflow using two-zone models

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters.