Extension of the Advanced REACH Tool (ART) to Include Welding Fume Exposure

Modulatory role of welding fumes on serum zinc and copper levels and oxidative stress markers among welders: Considering smoking as a possible implication

The presence of heavy metals in welding fumes and the numerous metals that make up welding gases expose welders to numerous occupational dangers, including major occupational health issues worldwide. The gases from welding are a significant and highly skilled process that have a considerable negative impact on welders' overall health and wellbeing. This study evaluated the influence of welding fumes on serum zinc and copper levels and oxidative stress biomarkers in welders considering smoking as a potential risk factor. The study used a case-control experimental design. Forty (40) healthy adult males were randomly selected comprising twenty (20) in the experimental group involving smokers and nonsmokers with welding experience and twenty (20) in the control group involving smokers and nonsmokers without welding experience. Data are expressed as the mean±SEM, and comparisons of means across groups were performed using one-way ANOVA, followed by Turkey's multiple comparisons test. The results showed that the serum zinc and copper levels of smokers were significantly (p < 0.05) increased in comparison to the control group, and a graded increase in the serum GST and MDA levels was observed across groups. The serum SOD level of smoker nonwelders was significantly (p < 0.05) increased when compared with the control group. Smokers who did not weld had significantly (p < 0.05) higher serum SOD levels. The results likewise showed a statistically nonsignificant reduction in glutathione levels and a substantial decrease in total antioxidant capacity (TAC) in the experimental group. Overall, changes in the antioxidant parameters showed that smoking and welding fumes can exacerbate an increase in the activity of reactive oxygen species (ROS), resulting in deteriorated health conditions.

Comparative Study of Exposure Assessment of Dust in Building Materials Enterprises Using ART and Monte Carlo

Background

Dust generated during the processing of building materials enterprises can pose a serious health risk. The study aimed to compare and analyze the results of ART and the Monte Carlo model for the dust exposure assessment in building materials enterprises, to derive the application scope of the two models.

Methods

First, ART and the Monte Carlo model were used to assess the exposure to dust in each of the 15 building materials enterprises. Then, a comparative analysis of the exposure assessment results was conducted. Finally, the model factors were analyzed using correlation analysis and the scope of application of the models was determined.

Results

The results show that ART is mainly influenced by four factors, namely, localized controls, segregation, dispersion, surface contamination, and fugitive emissions, and applies to scenarios where the workplace information of the building materials enterprises is specific and the average dust concentration is greater than or equal to 1.5 mg/m3. The Monte Carlo model is mainly influenced by the dust concentration in the workplace of building materials enterprises and is suitable for scenarios where the dust concentration in the workplace of the building materials enterprises is relatively uniform and the average dust concentration is less than or equal to 6mg/m3.

Conclusion

ART is most accurate when workplace information is specific and average dust concentration is > 1.5 mg/m3; whereas, The Monte Carlo model is the best when dust concentration is homogeneous and average dust concentration is < 6 mg/m3.

Levels and control of welding fume exposure to total particulate, hexavalent chromium, and manganese in contracted activities in an oil refinery setting (2008-2018)

In response to increasing focus on occupational exposures to welding fume, a 10-year series of personal exposure measurements was analyzed for the two main welding processes (Shielded Metal Arc Welding or Stick and Tungsten Inert Gas welding or TIG) used in an oil refinery setting. Exposures from ancillary gouging and grinding were also analyzed. The operations were conducted under a permit-to-work system, which stipulated control measures in the form of ventilation and respiratory protective equipment (RPE) depending on the work environment, base metal, and welding process. The analysis focused on three health hazards of interest: total particulate (TP); hexavalent chromium (Cr (VI)); and manganese (Mn). The study's aims were the analysis of exposure levels related to operational conditions to verify the adequacy of required control measures and the generation of quantitative information for the development of predictive exposure models. Arithmetic mean exposures were 2.01 mg/m3 for TP (n = 94), 13.86 µg/m3 for Cr (VI) (n = 160), and 0.024 mg/m3 for Mn (n = 95). Requirements and practices for ventilation and use of RPE appeared adequate for maintaining exposure levels below maximum use concentrations. Predictive models for mean exposure levels were developed using multiple linear regression. Different patterns emerged for TP, Cr (VI), and Mn exposure determinants. Enclosed or confined work environments were associated with elevated exposure levels, regardless of the provision of local exhaust or general dilution ventilation. Carbon arc, used with gouging and grinding, contributed significantly to TP exposure (p = 0.006). The relative TP source strengths of the two main welding processes were comparable to the literature data. For Cr (VI), stick welding was associated with approximately 50-fold (p < 0.001) higher exposure potential than TIG welding. For Mn, this difference was approximately 2.5-fold. Differences were observed across the three analytes in exposure reduction efficiency of local exhaust ventilation (LEV) compared to natural ventilation, possibly due to ineffective use in confined spaces. These findings contribute to the overall understanding of TP, Cr (VI), and Mn exposures from welding and required controls in an oil refinery setting.

Understanding the limitations and application of occupational exposure models in a REACH context

Exposure modeling plays a significant role for regulatory organizations, companies, and professionals involved in assessing and managing occupational health risks in workplaces. One context in which occupational exposure models are particularly relevant is the REACH Regulation in the European Union (Regulation (EC) No 1907/2006). This commentary describes the models for the occupational inhalation exposure assessment of chemicals within the REACH framework, their theoretical background, applications, and limitations, as well as the latest developments and priorities for model improvement. Summing up the debate, despite its relevance and importance in the context of REACH not being in question, occupational exposure modeling needs to be improved in many respects. There is a need to reach a wide consensus on several key issues (e.g., the theoretical background and the reliability of modeling tools), to consolidate and monitor model performance and regulatory acceptance, and to align practices and policies regarding exposure modeling.

Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool

STOFFENMANAGER^® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER^® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER^® and ART are based on a modified multiplicative model where an exposure base level (mg m⁻³) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m⁻³) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER^® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER^® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER^® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER^® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER^® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is <0.6) for both STOFFENMANAGER^® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER^® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Managing Occupational Exposure to Welding Fume: New Evidence Suggests a More Precautionary Approach is Needed

Welding is a common industrial process with many millions of workers exposed worldwide. In October 2017, the International Agency for Research on Cancer (IARC) concluded that exposure to welding fumes causes lung cancer in humans, based primarily on the available epidemiological literature. These research studies did not show that the cancer risk differed between mild steel and stainless steel welding but were related to the total welding aerosol. Lung cancer risks were observable at very low exposure levels; below 1 mg m-3 and perhaps as low as 0.1 mg m-3, averaged over a working lifetime. As a result of this IARC evaluation, in Britain, the Health and Safety Executive has acted to strengthen its enforcement expectations for fume control at welding activities. In the light of these developments, it would seem appropriate to review current health-based exposure limits for metal dust and fumes from welding to ensure they are protective.

Evaluation of Stoffenmanager and a New Exposure Model for Estimating Occupational Exposure to Styrene in the Fiberglass Reinforced Plastics Lamination Process

This study aims to evaluate occupational exposure models by comparing model estimations of Stoffenmanager, version 8.2, and exposure scores calculated using a new exposure model with personal exposure measurements for styrene used in the fiberglass-reinforced plastic (FRP) lamination processes in Korea. Using the collected exposure measurements (n = 160) with detailed contextual information about the type of process, working conditions, local exhaust ventilation, respiratory protections, and task descriptions, we developed a new model algorithm to estimate the score for occupational exposures on situation level. We assumed that the source of exposure originates from the near field only (within the breathing zone of workers). The new model is designed as a simple formula of multiplying scores for job classification, exposure potential, engineering controls, chemical hazard, and exposure probability and then dividing the score for workplace size. The final score is log-transformed, ranging from 1 to 14, and the exposure category is divided into four ratings: no exposure (1), low (2), medium (3), and high (4) exposures. Using the contextual information, all the parameters and modifying factors are similarly entered into the two models through direct translation and coding processes with expert judgement, and the exposure estimations and scores using the two models are calculated for each situation. Overall bias and precision for Stoffenmanager are −1.00 ± 2.07 (50th) and −0.32 ± 2.32 (90th) for all situations (n = 36), indicating that Stoffenmanager slightly underestimated styrene exposures. Pearson’s correlation coefficients are significantly high for Stoffenmanager (r = 0.87) and the new model (r = 0.88), and the correlation between the two models is significantly high (r = 0.93) (p < 0.01). Therefore, the model estimations using Stoffenmanager and the new model are significantly correlated with the styrene exposures in the FRP lamination process. Further studies are needed to validate and calibrate the models using a larger number of exposure measurements for various substances in the future.

Calibration of the Welding Advanced REACH Tool (weldART)

OBJECTIVES

This paper reports a study to develop and calibrate a deterministic model of welding fume exposure based on a four-compartment mass-balance model - The Welding Advanced REACH Tool (weldART). To achieve this aim, measurements of welding fume exposure were collected along with data on exposure determinants needed in the modelling.

METHODS

The welding fume exposure data was obtained from workers in a structural steel fabrication plant. Welders were engaged in three processes: flux-cored arc welding (FCAW), shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW). Aerosol concentration was measured using 13 mm diameter Swinnex sampling heads and MicroPEM direct-reading aerosol monitors. The model was initially developed with three spatial compartments (near-field (NF), far-field (FF), and welding plume (WP)). However, in the welding scenario investigated the FF had a very large volume and it was necessary to subdivide the room volume into an intermediate zone representing the FF along with the remaining room zone (RM). We fitted linear equations forced through the origin to the gravimetric concentrations measured inside the welders' visor and the weldART model estimates. The flowrates between the model compartments were adjusted by trial and error to obtain proportionate concentrations in each compartment.

RESULTS

The FCAW process generated higher welding fume particulate concentrations than SMAW and GTAW. The MicroPEM monitors considerably underestimated and were poorly correlated with the corresponding data from the Swinnex samplers. It was concluded that the MicroPEM data were unreliable. The model calibration showed a strong association between the personal exposure measurement and the weldART model values (R² = 0.94), with the average estimated value 1.3 times the measurements. The NF and the FF model estimates were poorly correlated with the corresponding compartment measurements (R² = 0.37 and 0.35, respectively), although on average the model estimates were close to the measurement data (ratio of modelled to measured 0.9, and 1.0, respectively).

CONCLUSIONS

The calibration shows that the weldART model is able to predict the exposure of welding fume particulate.

How to Obtain a Reliable Estimate of Occupational Exposure? Review and Discussion of Models' Reliability

Evaluation and validation studies of quantitative exposure models for occupational exposure assessment are still scarce and generally only consider a limited number of exposure scenarios. The aim of this review was to report the current state of knowledge of models’ reliability in terms of precision, accuracy, and robustness. A systematic review was performed through searches of major scientific databases (Web of Science, Scopus, and PubMed), concerning reliability of Tier1 (“ECETOC TRA”-European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment, MEASE, and EMKG-Expo-Tool) and Tier2 models (STOFFENMANAGER^® and “ART”-Advanced Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) Tool). Forty-five studies were identified, and we report the complete information concerning model performance in different exposure scenarios, as well as between-user reliability. Different studies describe the ECETOC TRA model as insufficient conservative to be a Tier1 model, in different exposure scenarios. Contrariwise, MEASE and EMKG-Expo-Tool seem to be conservative enough, even if these models have not been deeply evaluated. STOFFENMANAGER^® resulted the most balanced and robust model. Finally, ART was generally found to be the most accurate and precise model, with a medium level of conservatism. Overall, the results showed that no complete evaluation of the models has been conducted, suggesting the need for correct and harmonized validation of these tools.