Extension of the Advanced REACH Tool (ART) to include welding fume exposure

Abstract

Introduction: Welding is basic process commonly carried out in the workplace. Robot or automated welding are typically used in welding processes where the weld required is repetitive and quality and speed are crucial. Not every welding operation is suitable for automated welding. If the project is limited to a single non-repetitive process, manual welding may be more suitable. The welding process is applied in various production fields and the demand for welders worldwide is increasing. Welders are exposed to health hazard from inhalation of metal fumes produced as a by-product of the process. The concentrations of welding fumes inhaled by workers can be measured, but it would be advantageous if there were also predictive exposure models to estimate exposure. However, presently, there are few reliable estimation models for welding fume exposure. Objectives: To develop estimation model for welding fume exposure. Methods: This study consisted of five main stages. The first stage comprised a literature review, including an evaluation of relevant generic exposure models, particularly the Advanced REACH Tool (ART), principles of exposure modelling, and various research studies related to welding fumes. The second stage describes an investigation to measure welding fume exposure at a production site. The third stage comprised welding fume exposure model development by adapting the ART model (to be the weldART model), including the identification of key modifying factors (MF) and a suitable computational form to undertake the model calculations. The fourth stage was modelling calibration, which used data obtained from the sampling in stage 2. The last stage was model verification, which applied welding fume measurement data from reports and published papers to test the reliability and uncertainty of the weldART model. Results: The model was developed within a well-mixed mass-balance computational framework. An important MF to be used in model development was fume formation rate (FFR), i.e., the mass emission rate of total metal fume from the welding process. The identified variables that affect fume formation rate were type of welding process, electrical current and input power, shielding gas, and welding consumables. In addition, the model also incorporates other important factors, such as convective dissipation of the welding fume away from the welding area and the welder’s interaction with the fume plume. The review indicated that welding process types with the highest to lowest welding fume particulate emission rates were flux-cored arc welding (FCAW), shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW). In order to develop effective and probabilistic weldART model, variables, namely welder's head (WH) and localized control (LC) were also taken into consideration. A deterministic four-compartment mass-balance mathematical model, the weldART model, was developed. In the measurement study two types of sample were collected: a Swinnex sampler to collect fume for gravimetric analysis and a MicroPEM direct-reading aerosol monitor. The comparison of fume concentrations between these two samplers showed that the MicroPEM monitors significantly underestimated exposure concentrations and had low correlation with the corresponding data from the Swinnex samplers. It was concluded that it was possible that particles were lost in the sampling tube of the MicroPEM due to the electrostatic deposition before the entering the aerosol sensor, and these data were only used to indicate the duration of welding activity. Meanwhile, estimation of the calibrated four-compartment mass-balance weldART model gave a strong correlation with the welding fume exposure measurements made during this research. To accommodate the uncertainties involved in verifying the model using published exposure data, the weldART was extended to incorporate a probabilistic aspect. This may be due to a positive systematic bias across the whole applicability domain, which becomes dominant at low measured values. Conclusions: The weldART model can produce reliable and accurate estimates of welding fume exposure. Especially, if factors related to distance of welder’s head and localized control were taken into account, along with the presence of additional workplace exposure sources. The weldART could offer an alternative approach to evaluate fume concentration for occupational hygienists. At present the model is available as standalone R-code that is freely available, but it lacks a suitable user-friendly user interface. The weldART is calibrated and has had a limited verification exercise completed, but further development and evaluation is necessary.