Welding is one of the commonest activities and the most popular methods for joining materials together carried out in the workplace [1]. With the rapid development of industry, welding is used in many production fields, and the number of welders is increasing [2]. Solano-Lopez et al. [3] reported that more than 2,000,000 welders worldwide conduct welding as part of their work tasks. There are about 190,000 welders in the UK, with around 73,000 professional, skilled welders and the remainder made up of other semi-skilled welders who carry out welding as part of their job [4].

There are a number of health hazards associated with welding, for example, ultraviolet (UV) radiation from the arc, electrical shock hazards, heat or fire risks and metal fume and gasses.

Welding fumes are basically produced in welding processes, specifically, using the consumable electrode and high-current [1]. NIOSH has identified welding fumes as one of the commonest mixed exposure problems, and the development of suitable exposure models as an important are for study [5].

It is exposure to metal fumes and the potential risks to health that are the main focus of this research. The arc or flame vaporizes the welding electrode and/or base metal, which then condenses into submicron particles called fumes. Fume may be suspended in the air for long periods and may then be inhaled into the lung [6]. At high concentration, welding fume many cause a hazard to health [7] and steps need to be taken to control exposures. Without adequate controls, the fume concentrations may be higher than the appropriate occupational exposure limit. To ensure the control measures are adequate it is prudent to undertake a risk assessment, which involves estimation of the exposure of workers to the fume and, where appropriate, identification of the steps required to control that exposure. Exposure can be estimated by measuring the concentration of fumes inhaled by workers in a number of specific instances or by using a mathematical model.

Exposure models are used in risk assessments and risk management to describe the association of concentrations and emissions, and to predict the impact of risk management measures. The strong point of using models is that they allow us to think about this correlation; the processes information can used to explain this relationship regarding the environment characteristics and properties of the hazardous substance [8]. Exposure models can be used to make an estimate of recorded exposures that cannot be re-created simply and possible future exposures in investigational scenarios or situations [9].

The recent interest in occupational exposure modelling has been tied to the need for exposure assessment for risk assessment required by regulations in the European Union, for example the REACH (Registration, Evaluation, Authorisation & restriction of CHemicals) Regulations [9]. The European Chemicals Agency (ECHA) has outlined a tiered approach for occupational exposures modelling. Tier 1 assessments are fundamental, conservative screening models, which need a limited range of input data. Several screening models are accessible to serve as an initial “Tier 1” approach within REACH, including Stoffenmanager, ECETOC TRA, and the EMKG-Expo-Tool [10]. Tier 1 models are generally designed to overestimate exposure, i.e. they are “conservative”. If a Tier 1 assessment fails to indicate an adequate protection level, then a Tier 2 assessment may be needed. ECHA advises that Tier 2 assessments can be performed by any proper method that is adequately accurate and valid [10]. Tier 2 assessments are designed to evaluate well-defined exposure situations with an extensive amount of input data to control uncertainty [11]. The only higher tier model for inhalation exposure assessments recognised by ECHA is the Advanced REACH Tool (ART) [10]. The ART uses a Bayesian methodology; a mechanistic model evaluates of inhalation exposure and any appropriate exposure measurement can then be used to update the model estimates [12]. However, neither the current ART model nor any of the Tier 1 model tools include welding processes within their scope.

The aim of this research is to review the structure of the ART model in relation to exposure arising welding fume, and to identify changes to the model structure to extend the applicability domain of the ART to include these processes.

2. Research Questions

How can the ART be adapted to evaluate metal welding fume exposure of workers?
Is the ART applicable to different worksites in industry where welding is undertaken?
Do further influencing variables need to be considered to ensure the accuracy of the ART model for welding fume exposure?

3. Research Objectives

To review the ART mechanistic model for its relevance to fume exposure, and update as appropriate.
To calibrate the revised ART mechanistic model for metal fume exposure.
To assess the accuracy of the updated mechanistic model in situations not used to develop the model.
Produce a specification for the ART for metal welding fumes.

4. Background

4.1 Welding

The American Welding Society defines welding as “a metal joining process wherein coalescence is produced by heating to suitable temperature with or without the use of filler metal”. Welding includes processes used to join pieces of material by heat, pressure or both, including closely allied processes such as cutting, brazing and soldering [13].

Electric arc welding was presented in the 1940s and manual metal arc welding has become the most important method used for welding. Uncoated and acidic coated electrodes were introduced in the early 1950s. These electrodes produced large quantities of fumes. By the mid 1950s basic coated electrodes were introduced and this reduced the amount of fume formation. In the 1970s, tungsten inert gas welding and gas shielded welding were developed. Welding on zinc primed steel and on aluminum infrequently took place before 1970, but until the mid-1970s welding was generally only performed on mild steel. From around 1977 onwards stainless steel was moderately presented as a material for welding and by 1990 about 50% of the welding arc time was conducted on stainless steel [14].

The type of welding process partly determines the quantity of fumes and gases that are generated. Accordingly, it is necessary to have a basic understanding of the process of welding in order to evaluate the exposure risk.

4.2 Type of Welding Process

Gas Welding

Oxy-acetylene gas welding is one of the oldest methods of welding and, for many years, was the most widely used welding technique. Its use is a lot less common today. Nevertheless, it is a versatile method, using simple and relatively cheap equipment. It is suit- able for repair and construction work, for welding pipes/tubes and structures with a wall thickness of 0.5–6 mm and in materials particularly prone to cracking, such as cast iron. It is also used for welding non-ferrous metals and for cladding and hardfacing. In addition to welding, the technique is often used for cutting, and is also very useful for heating and straightening materials.

The heat is generated by the combustion of acetylene in oxygen, which gives a flame temperature of about 3,100 °C. This is lower than the temperature of an electric arc, and also produces a less concentrated heat. The flame is directed onto the surfaces of the joint, which melt, after which filler material can be added as necessary. The melt pool is protected from air by the reducing outer zones of the flame. The flame should therefore be removed slowly when the weld is completed. [15]

Arc Welding

A welding arc is an electrical discharge between two electrodes. The welding current is conducted from the electrode to the workpiece through a heated and ionised gas, called plasma. The voltage drop and current in the arc determine the amount of electric power that is released, the heat from which melts the electrode and the joint faces, allowing welding to take place.

As a result of the intense heat from the arc, some of the electrode metal is vaporised. When this vapour leaves the arc it is oxidised and forms welding fumes. If some other material is present, having a lower vaporisation temperature, fume generation will increase. Examples of such substances are the flux in a flux-cored wire, or oil, paint or zinc coating on the workpiece.

Tungsten Inert Gas (TIG) Welding

Tungsten Inert Gas (TIG) welding (also called Gas Tungsten Arc Welding, or GTAW) involves striking an arc between a non-consumable tungsten electrode and the work- piece. The weld pool and the electrode are protected by an inert gas, usually argon, supplied through a gas cup at the end of the welding torch, in which the electrode is centrally positioned.

TIG welding can also be used for welding with filler material, which can be applied in rod form by hand similar to gas welding. Tools for mechanised TIG welding are used for applications such as joining pipes and welding tubes into the end plates of heat exchangers. Such automatic welding tools can incorporate many advanced features, including mechanised supply of filler wire.

The main advantages of the TIG process include the stable arc and excellent control of the welding result. Important applications are welding of stainless steel, light metals such as aluminium and magnesium alloys, and copper. It is suitable for welding all weldable materials, apart from lead and zinc. It can be used with all types of joints and in all welding positions. However, TIG welding is best suited to thin materials, from about 0.5 mm up to about 3 mm thick. In terms of productivity, TIG welding cannot compete with methods such as short arc welding.

Plasma Welding

The plasma welding method employs an inner plasma gas and outer shielding gas. The plasma gas flows around a retracted centred electrode, which is usually made of tungsten. The shielding gas flows through the outer gas nozzle, serving the same purpose as in TIG welding.

A plasma arc is considerably straighter and more concentrated than, for example, a TIG arc, which means that the method is less sensitive to arc length variations.

The plasma welding process can accept variations of 2–3 mm in the arc length without significantly altering the heat input to the workpiece. This is approximately ten times better than the corresponding value for TIG welding. However, because the arc is narrower, more accurate transverse control is important, which means that the method is normally used in mechanised welding.

MIG/MAG Welding

Until the 1970s, manual metal arc (MMA) was the dominant method of welding. Today MIG/MAG is the leading welding process in most industrial countries. Gas metal arc welding (GMAW) is also referred to as MIG (metal inert gas) welding if the shielding gas is inert (e.g. argon) or MAG (metal active gas) welding if the gas has a content of an active gas (such as CO₂).

MIG (and MAG) welding is a particularly flexible method with a wide range of applications. These include:

• Welding plate thicknesses from 0.5 mm and upwards. The low heat input in MIG welding is particularly useful when welding thin sheet, since it minimises deformation and distortion of the sheet.

• Better productivity in welding thicker metal than many other techniques.

• The ability to weld all commonly encountered structural materials such as mild, low- alloy and stainless steel, aluminium and its alloys, and several other non-ferrous metals (e.g. copper and copper alloys, and nickel and nickel alloys etc.)

• The ability to weld surface coated metals e.g. Zn-coated steel

• Application of the technique in all welding positions.

A limitation of the MIG method compared to MMA is that the welding equipment is more complex and therefore less portable. It has also a more limited application outdoors, as the shielding gas must be protected from draughts.

Manual Metal Arc (MMA) Welding with Coated Electrodes

Manual Metal Arc welding (MMA) is often referred to as Shielded Metal Arc Welding (SMAW) or stick electrode welding. It was the predominant form of fusion welding until the beginning of the 1980s. It uses electrode rods consisting of a wire core with an external coating containing mixtures of substances such as chemicals, minerals and iron powder. They are made in a range of core diameters, with each diameter being intended for a particular current range. Welding involves striking an arc between the electrode and the workpiece, with the heat of the arc melting the electrode coating which forms a protective slag. The weld metal is produced both by the core electrode wire and iron powder in the coating. The layer of slag on top of the joint needs to be removed after welding.

Submerged Arc Welding

Submerged arc welding (SAW) is a high-productivity method of welding, generally carried out using mechanical welding methods and suitable for use with 1–3 continuous wire electrodes.

The arc or arcs are struck and burn beneath a layer of flux, which is supplied to the welding head whilst welding is in progress. The flux closest to the arc melts and forms slag on the surface of the weld, thus protecting the molten metal from reacting with the oxygen and nitrogen in the air. Residual powder is sucked up, returned to the flux hopper and re-used. Welding can be carried out with DC or AC.

4.3 The Formation of Fumes and Fume Characteristics

Fumes are created when heating of metal more than its boiling point and the vapours produced condense into a fine suspension of solid particles [1]. These diameter range of particles are generally 1 to 2 mm, but the size distribution can be larger than 10 mm [16].

Fumes are formed by the volatilisation, oxidation and condensation of the hot metal vapour and gaseous matter that has evaporated from the electrode and the base material. The main metal vapour element comes from the electrode. Also, a small amount of the fume can be allocated to the spatter particles emitted from the arc region [16].

Slater [16] reported that a large amount of the fume arises from the arc region where the high temperature vapours is dispersed into the ambient atmosphere. Subsequently, in the ambient environment it rapidly oxidises and condenses forming fume.

The welding process generate fume may consist of many different metals mixture, including beryllium (Be), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), antimony (Sb), vanadium (V) and zinc (Zn) [17].

The composition varies depending on the metal being welded. With mild steel the fume will mainly consist of iron oxide but there is also likely to be a small percentage of manganese which is used in welding rods.

In the 1970s, Heile and Hill [18] reported that the welding fumes formation from GMAW was correlated with welding conditions such as shielding gas, current, voltage, and metal transfer mode including welding process parameters such as the electrode wire.

4.4 Health Effects from Welding Fume Exposure

Occupational exposure to welding fumes is a serious occupational health problem throughout the world [2]. The main health hazard with many welding operations is the welding fume particularly manual metal arc and metal inert gas welding.

Welding fume contain particles of metal oxide that form during welding [19]. This metal oxide is mainly formed from the welding wire or electrode used during the welding process. Stainless steel welding is particularly hazardous as the fume contains nickel and chromium VI oxides, which are highly toxic if inhaled – both are carcinogens and can also cause occupational asthma [3]. Solano-Lopez et al. [3] stated that chromium may effect to increase the risk of lung cancer in welding workers. Moreover, the authors concluded that manual metal arc-stainless steel welding fume produces atypical hyperplastic changes. These changes progress in the significantly absence raised bronchoalveolar lavage lung parameters expressive of lung inflammation and injury.

Airborne particulate matters from welding process are small, then they are easily inhaled and remain in human respiratory. [20] Fumes are harmful, both because of the size and from their chemical composition, with up to 60% of the particulate mass dispersed during processes of welding are smaller than 100 nm (also known as ultra-fine particles). Ultra-fine particles have the ability to cause inflammation and have the potential to escape from the deposition site in the lungs and move to the blood including other target organs [21].

Another hazard from welding fume are about nervous system. The high alloy manganese electrodes produce fume during the welding process. Repeated exposure to manganese low concentrations have been shown to affect the nervous system [4] including may build up the neurological impairment risk [22]. Several investigations have studied the exposure to manganese in welder. Sjögren et al. [23] reported that several welding workers appeared the central nervous system symptoms that were an indication of high manganese exposure. It claimed that exposure of manganese induce many symptoms from the sensory, motor, and peripheral nervous system. In addition the authors’ study demonstrated a higher prevalence of insomnia among the welders.

The HSE estimates that exposure to welding fume causes more than 150 deaths due to cancer every year. Exposure to the fume and gases can also cause other illnesses, including: metal fume fever, chronic obstructive pulmonary disease (COPD), which includes bronchitis and emphysema asthma and increase susceptibility to pneumonia [4]. Koh et al. [24] concluded that the exposure of welding fume affecting to increase the risk of COPD.

Many papers have proved that the effect of welding fume exposure can develop remarkable health risk of workers such as respiratory symptoms, decreasing pulmonary function, inflammatory responses, oxidation stress, airway irritation symptoms [6].

4.5 Modelling Exposure and the ART

Even though the best approach to assess exposure to hazardous substances would be expected to be personal monitoring on all workers in specific situation it is impractical to do this. In fact, personal monitoring on everyone in the workplace is impossible because of cost and resource limitations. Moreover, personal monitoring may not reflect exposure in the past because of changes in circumstances [25] and may not be representative of future conditions for similar reasons. As mentioned above, many scientists have developed exposure model for estimating of chemical exposure to overcome some of these limitations. Exposure model can estimate the concentration of hazardous substances for worker groups and periods of time for which personal monitoring is impracticable.

Most exposure models do not estimate human exposure directly. For example, in the inhalation exposure area, models estimate the chemical substances concentration in the ambient environment and assume that the worker is breathing air with this concentration [9].

The exposure model construction is relatively uncomplicated science. In the term of modelling human exposure this about exploration to know the factors of source of contaminant, generation and control. As the critical variables of exposure are recognised, the assessment tools are created that will improve experience, fundamental of knowledge, and simulate situations that will estimate actual concentrations and exposures in the real circumstance. The models development comprises formulating hypotheses about the predictors of exposure and the experiments evaluation of cause and effect [9].

The aims of the exposure assessment model are to obtain accurate and precise estimates [26] of the distribution and determinants of exposure. Sometimes a model calculation method can yield more quickly than though monitoring a correct estimate of the risk. Exposure assessors can also use the modelling constructs to posit testable hypotheses to enhance the basic understanding or ability to estimate real exposures [9]. In recent years, especially under the influence of REACH, a number of models have been developed. Unfortunately, the validation of these models usually has not been performed before their release, but only afterwards [4].

During welding, particulate substances are formed, which, according to composition, concentration and duration of exposure, present a hazard to the health of the workers. The determination of the concentration and the intensity of the effect of dominant hazardous substances is a precondition for the assessment of the relevant work condition, for the determination and execution of the required measures and thus for successful health care as a whole [27].

Welding fumes exposure in the worksite will vary depending on welding length, using materials and methods, and especially, the strategies of control. However, mostly workplace is unique. Therefore, a complete assessment of exposure should be made for establishing suitable control measures.

The ART mechanistic model develops inhalation exposure estimates in the lack of exposure data, but the estimates accuracy will improve when results of relevant exposure measurements are included via a Bayesian updating process. The Bayesian methodology concept is that the posterior estimate of exposure will provide more precise the exposure estimates than the mechanistic model or the measurements of exposure alone. Nevertheless, the raise in precision depends on the analogy level between the data and the scenario being modelled, the amount of available measurements (amount of measured companies, amount of measured workers, and repeated measurements), and the variability level of exposure in the measurements of exposure applied in the Bayesian update [28].

The mechanistic model develops a source–receptor structure consists of modifying factors (MFs) indicating the source, compartments of transmission and the receptor. The model uses MFs to describe the exposure process in a specific situation (e.g. substance emission potential, activity emission potential, localized controls, dispersion, personal enclosure, segregation, and surface contamination) [xxx].

The mechanistic model output is a score of exposure that allocates a relative ranking of geometric mean (GM) exposure levels for various situations [xxx].

Schinkel et al. [29] described how use of the ART could be improved by improvements of the guidance documentation for assessors, use of consensus procedures, and improving the methods of training. However, considerable variability can still be anticipated between assessment tools using ART to estimate the levels of exposure for a provided scenario.

The ART is a modern estimate exposure tool for various hazardous substances. ART was initially expanded to help risk assessments for the REACH Regulations, although it has proved equally useful for exposure assessment in other areas [12].

The ART mechanistic model allocates the exposure distribution estimate based on input of user for specific determinants of exposure for an exposure situations, and contains prior data about between-workplace, between-worker, and within worker variability. In addition the ART model allows a means to update the prior (mechanistic model) estimate of the exposure distribution with exposure measurements [28].

The mechanistic model is based on the ‘transport’ of a contaminant from the source to the receptor (in this case the worker) and uses independent principal modifying factors (MFs) in a multiplicative model form. The environment is divided into two compartments: the near-field (NF) centred on the worker (within 1 meter from the worker’s head) and the far-field (FF) comprising the remainder of the workspace [32].

Figure 4.5.1 The ART mechanistic model flow diagram [xxx]

The ART tool has some limitations because it was calibrated separately for vapours, mists, and dusts. While gases, fibres, and fume exposure were not included in the calibration and are considered outside the current applicability domain [12].

To date there has been very little study reported on the development of the model for estimating gases, fibres, and fumes exposure, including for welding fume inhalation exposure.

The ART model components are associated with the modifying factors (MFs) or determinants of exposure. In order to be useful for exposure modelling, these MFs have to be uniquely identifiable, observable, and quantifiable and be applicable across a wide range of different exposure scenarios. [30].

Activity Emission Potential (H)

The MF ‘activity emission potential’ describes the potential of the activity to generate exposure (e.g. types of welding processes) and is determined by the following characteristics: type and amount of energy transfer, scale (e.g. amount product used) and product-to-air interface (e.g. level of containment).

Substance Emission Potential (E)

The MF ‘substance emission potential’ determines the intrinsic emission potential of a substance, i.e. dustiness for particulate agents and volatility for liquids.

Localized Control (LC)

The MF ‘localized control’ measures in close proximity of the source intended to remove emissions, e.g. local exhaust ventilation, airborne capture sprays.

Segregation (Seg)

The MF ‘segregation’ describes the effectiveness of isolation of sources from the work environment.

Dilution (D)

The MF ‘dilution’ describes the influence of mechanical and natural ventilation and room size on the concentration in the NF or FF compartments.

Personal Behaviour (P)

The MF ‘personal behaviour’ is defined to take account of the influence due to worker movement, welder’s position relative to work piece, possible worker posture very close to the source and other factors causing deviations from a completely mixed NF.

Separation (Sep)

The MF ‘separation’ describes how effective the concentration in the personal enclosure compartments is reduced relative to the FF in which it is embedded. Note that a personal barrier, if present, encapsulates the person and could thus be taken as the NF zone.

Surface Contamination (Su)

The MF ‘surface contamination’ describes the emission related to release of deposited contaminants on surrounding surfaces (including worker clothing) due to natural means or general workplace activities (e.g. moving equipment/vehicles).

Respiratory Protective Equipment (RPE)

The MF ‘respiratory protective equipment’ describes the efficiency of RPE preventing the inhalation of airborne substances. However, RPE is not considered in the currently ART version.

5. Review of Previous Research Involving Modelling Welding Fume Exposure

Kromhout, Swuste and Boleij [31] used multiple linear regression models to assess chemical exposure including curing fumes in rubber-manufacturing plants. Besides, statistical linear models can be applied to many situations in work environment as well as in occupational hygiene settings [31]. Nevertheless, the authors reported that this model has many limitations. For example, it was possible to make consistent estimates of work tasks that only occurred frequently. In particular, they recognized that the linear models remained a lot of variance in exposure concentrations because the work pattern and the task content are dissimilar. Conversely, the ART can run for estimating exposure within this weakness situations but just only for vapours, mists, and dusts not yet including fumes exposure.

Boelter et al. [33] studied two-zone model for application to breathing zone (near-field) and area (far-field) concentration data of welding fume. They recommended to assemble the important information to validate the fume generation rates for apply in mathematical models. Likewise, the ART is based on a two-zone model to represent the NF and FF [34]. Cherrie et al. [34] noted that the small localized sources of working that involve with elevated temperature, e.g. welding present a difficult problems to link to the dispersion of the hazardous agent. In these cases, the workers may place their head into or close to the dispersing plume, giving higher than otherwise exposures. The authors suggested further work should be considered on this issue in the ART model.

Hobson and colleagues [22] developed and validated a multivariate statistical model to assess welding fume exposures. The authors summarized the exposure measurements and related determinants, such as sampling year, welding process, ventilation type, degree of enclosure of the work environment, base metal and sampling location. This paper was performed to select and evaluate the models by cross-validation. The authors concluded that it was possible to develop and validate the model of exposure for welding particulate mass and manganese means by using the measurement data from the published literature. They recommended that, however, providing more detailed of exposure determinants can be improved this model. The result of this study shows the mean exposure estimate generated from this model cannot expected to be as accurate as exposure measurements of historical individual.

The fume formation rate (FFR) is the rate at which welding fumes are generated [16]. From a review of the literature there are many factors identified that influence FFR, for example, electrode, shielding gas, welding parameters (voltage and current), and base metal. Accordingly, the effect of these factors, as known in the term of MFs, are contributed in the ART model for welding fume exposure.

The ART model for welding fume exposure consists of one algorithm for estimating the contribution from near-ﬁeld (NF) sources [equation (1)] and one to estimate the contribution from far-ﬁeld (FF) sources [equation (2)]. Personal exposure from a near-ﬁeld source (C_nf) is a multiplicative function of substance emission potential (E; e.g. welding process, consumables, shielding gas), and activity emission potential (H; i.e. power supply), localized control (LC), and dispersion (D). The algorithm for a far-ﬁeld source (C_ff ) also includes segregation (Seg) and personal enclosure or separation (Sep).

Cnf=Enf∙Hnf∙LCnf∙Dnf

(1)

Cff=Eff∙Hff∙LCff∙Segff∙Dff∙Sep

(2)

Then, the overall exposure is estimated by algorithm equation (3):

Ct=1ttotal∑taskstexp∙Cnf∙Cff+tnon-exp∙0

(3)

The algorithm considers multiple activities [and exposure time (t_exp)] within an 8-h work shift (t_total) and also allows periods with assumingly zero exposure (t_non-exp).

6. Factors That Influence Fume Formation

There are many factors that can influence the particulate and gaseous fumes generated by welding. However, there is little study on the fume plume dispersion of welding process. Also, it is not easy to identify individual factors because many factors are related to one other in some way. The effects that welding conditions play in the formation of fumes have long received attention of researchers; Heile and Hill [18], Castner [35], Brooks, Mahboubi, French, and Tyagi [36] to name a few.

6.1 Welding Conditions – Current and Voltage

The parameters that govern heat input due to welding, that is the current and voltage used are probably the single greatest influencing factor on the generation of welding fumes during arc welding. Heile and Hill [18] stated that “the shielding gas, voltage and current are the most important factors among welding parameters”. By either increasing or decreasing these parameters, the rate of generation of fume can change elements such as electrode tip temperature, metal transfer mode, and the rate at which the metal drops are transferred across the arc.

Of all the welding parameters, the welding current is considered the most critical element in fume generation. Firstly, increasing the current causes an increase in the temperature at the tip of the electrode as well as arc temperature, due to a larger supply of electrons at the tip. This rise in temperature causes an elevated evaporation rate, ultimately causing a greater amount of fume to be given off. Secondly, an increase in the current through the electrode causes an increase in the melting rate of the electrode. This means that more electrode material is transferred through the arc.

6.2 Type of Current and its Polarity

Research studies note that the fume formation rates observed occur with a direct current positive electrode [37]. This is indicative of the fact that the temperature of drop on the tip of the electrode is higher when welding with an electrode positive (DCEP). That is, higher fume emissions occur when welding with a DC electrode positive, because the electrode becomes an anode, experiencing greater heating, raising the tip temperature, which propagates the reaction rates of generating fumes. This polarity effect varies with the type of flux coating, appearing to a greater degree with a lime coated electrode. The difference in fume formation rates when welding with positive and negative electrodes can vary up to 30%. Welding with an AC current can produce fume formation rates quite similar to DC electrode negative welding [16].

6.3 Electrode Influence

The electrode wire (or filler metal) is considered the main source of the fume. Electrode wires are usually of similar composition to that of the base metal being welded. The most common metal used in electrodes is plain carbon steel. There are also ranges of alloyed steels that contain chemical elements such as Chromium, Aluminium, Cobalt, Molybdenum, Vanadium or Tungsten.

The preliminary fume investigations carried by Heile and Hill [18] explain the effect of the vapour pressures of the components in the wire on the mass of fume generated and the overall composition of the fume. It was concluded that the electrodes that contained elements of high volatility produced more fumes than those with lower volatile composites.

The effects of the electrode moisture content will be conductive to levels of fume [16]. The electrode moisture percentage increasing provides an increase in fumes because the levels of moisture change the arc behaviour.

6.4 Electrode Coating

The core material in a flux-cored electrode will affect the overall fume generation during welding. The flux contains substances such as metal alloys, silicates, metal oxides and arc stabilisers.

Voitkevich [37] concluded that when the flux melts, it has a partial input into the fumes formation. Varying the core composition changes the parameters of welding, thus affecting the formation of fume. They stated that the elements percentage in the electrode and the fume do not coincide.

6.5 Electrode Diameter

Electrode diameter has a moderate effect on the fume formation rates. Quimby and Ulrich [38] suggested that this is due to the change in welding current and voltage, and possibly the transfer mode. Besides, Heile and Hill [18] found that an increase in wire diameter produced an increase in FFR over a large current range. Likewise, Slater [16] reported that electrode diameter affects the number of material accumulated onto the workpiece. Larger diameter electrodes produce high fume dispersions for similar accumulation rates.

6.6 Arc Length

Arc length define as the distance from the electrode tip to the workpiece. Slater [16] reported that an increase in the arc length produces increased fume rates. Conversely, some studies argued that the increased contact of arc with the air can generate a fume dispersion increase.

6.7 Metal Transfer Mode

The transfer of metal from the tip of the electrode to the workpiece, through the arc, has an enormous influence on the fume generated. It also influences the overall performance of the welding process. Arc stability, droplet transfer and spatter formation. This is determined, as stated above, by the welding parameters. The influence of metal transfer on fume generation has been investigated in the past by Heile and Hill [18], Castner [35] and Brooks et al. [36]. Brooks et al. state that better control of arc stability and metal transfer influences a decline in the formation of welding fume.

6.8 Shielding Gas Effects

Fume levels are affected by an increase in oxidation potential of the shielding gas, since fumes are produced by the evaporation, condensation and oxidation of metal vapour. The Heile and Hill [18] experimental trials on the shielding gases effects on the formation of welding fume display evidence that increasing presence of oxygen within the shielding gas, influences the amount of fume generated. This is due to the oxidising effect of the shielding gas. The activating agent in the shielding gas is usually oxygen or carbon dioxide. Most shielding gases used for steel such as helium and argon based gases have mixtures of these active gases in them, with an additional oxygen supply also being entrained from the surrounding air by higher shielding gas flow rates. Thus, the higher the oxidation potential of the shielding gas, the greater the fume formation levels. Several investigations noted that helium and helium mixtures produce higher fume formation rate than argon based mixtures [16].

6.9 Shielding Gas Consumption Rate

The consumption rate of shielding gas also influences the rate of fume formation, as FFR increase with higher gas flows. The shielding gas flow has to be a compromise between being high enough to be able to maintain arc stability as well as protect the weld. However, the gas flow must also be low enough to not create turbulence (drawing ambient oxygen into the arc zone), affecting the oxidation rates of the particulate matter generated, resulting in the production of welding fume.

6.10 Base Metal (Compositions and Coatings)

The base material is assumed to have a limited effect to the generation of the welding fumes, especially when compared to the electrode. The exception to this is when the base material contains highly volatile compounds, or it has some form of surface coating, such as paint or metallic coating. With surface coatings, there exists a potential for high concentrations of harmful fumes.

6.11 Welding Speed

The effect that welding speed has on the overall fume generation is only minor. It has been claimed that doubling the welding speed gives a 5% decrease in the overall fume formation rate, Heile and Hill [18]. However, it was noted that decreasing the welding speed from 200 to 100 mm/min, under certain circumstances there was 20% rise in welding fume formation rate.

6.12 Welding Geometry and Orientation

The position or angle of the electrode with respect to the workpiece, affects the fume generation rate. The investigations by Slater [16], show that welding perpendicular to the base material generates the least amount of fume. The increase in the FFR generated by the inclination of the electrode is believed to be due to arc lengthening, as well as the degradation of melting zone protection against the ambient oxygen. A typical increase of 15-20% in arc length is experienced with an angular variation between vertical and 30 to the workpiece.

Fillet welds produce approximately 15-20% less fume per unit time than a flat-based weld. A British Occupational Hygiene Society study noted a 30% reduction from the use of fillet welds, attributing this to the proximity of the metal surface inducing condensation to the plume. [16].

An integral component in the operator exposure is the orientation of the welder with respect to the welding torch. The position of the welder’s head produces varying effects on the exposure concentration. [16] Highest recorded concentrations were for operations where the welder had their head within the fume plume (positioned above the source). A vertical operation, where the welder’s head was to the side of the rising plume, exhibited lower exposure concentrations, in comparison with directly above the source.

7. Extension of the ART to Encompass Welding Fume Exposure

Welding fumes as one of the most occupational hygiene problems relate to mixed exposure. Accordingly, using the suitable exposure models play an important role for assessment of welding fume exposure.

In many cases, including welding fumes, the components of the mixed exposure are correlated, as they arise from the same process or task; and there is the prospect that a multivariate probability distribution may serve as a useful exposure model [5].

Flynn and Susi [5] studied using the multivariate Johnson system of probability distributions as a mathematical model that includes the usual normal distribution and lognormal distribution. This paper showed that there was a strong correlation between manganese and total welding fume exposure. Besides, welding fume generation rates were found to be proportional to the electrode current, the process arc time, and process of welding. The result of this paper can be used in consideration of modifying factors (MFs) in ART model.

The results from the study of Flynn and Susi [5], are helpful for development the ART model to predict the inhalation welding fume exposure. To assess the reliability of the model, the review requires determination of potential effects of modelling because of the fume formation and generation rates. Similarly, the impact of fume formation and generation rates are discussed in the term of modifying factors (MFs).

Yoon, Paik and Kim [39] explored the generation rates of welding fume including total chromium and hexavalent chromium concentration when stainless steel was welded using flux-cored arc welding with CO₂ gas shielding. They found that the total fume concentration was significantly related to input electrical power level. The concentration of hexavalent chromium found in the Yoon et al. [39] study is similar to that from metal inert gas welding [40] whereas total chromium concentration in Yoon et al.’s study was similar to that from shielded metal arc welding [41], [42], [43], [44], [40], [37].

Dennis et al. [45] developed a model for prediction of fume generation rates in gas metal arc welding. The authors claimed that the model is applicable to optimal and high current modes, but not to a low current mode. The variables of this model are electrical current, wire velocity and droplet transfer frequency. The authors concluded that the model was fairly insensitive to droplet transfer frequency but was very dependent on the ratio of current to wire velocity. Moreover, they showed that shield gas composition can influence generation rates of welding. Also removing all the oxygen from the shield gas and surrounding atmosphere dramatically reduced fume generation rates.

Liu et al. [46] studied using a combination of multivariable linear regression models and linear mixed models to identify exposure determinants to total particulate matter and manganese. Linear mixed models were used to determine fixed effects due to different countries, industries and trades, process characteristics, and the sampling regimen, and to estimate components of variance within workers (both intra-day and inter-day), between workers (within worksites), and across worksites. The paper shows that the different area of sampling, type of ventilation, and type of welding process were the major factors affecting exposures to welding fume. In addition, the results showed that manganese (Mn) concentrations in fume were significantly affected by the welding consumables but not by the base metal. Moreover, resistance welding produced significantly lower total particulate matter (TP) and Mn exposure levels compared to other welding processes. The authors concluded that after controlling for fixed effects, variance components between worksites and between-individual workers within a worksite were reduced the concentration of TP and Mn.

The study of Heile and Hill [18] showed that increasing presence of oxygen within the shielding gas influenced the amount of fume generated. Some papers concluded that helium and helium mixtures generate FFR more than argon based mixtures [16]. The Slater [16] experiment showed evidence that the active gas CO₂ increases the FFR. In addition, Irving [47] claimed that changing the shielding gas from an Ar/O₂ mixture to an Ar/CO₂ mixture resulted in the FFR increasing.

Although many MFs affecting fume formation rate but some MFs unable to objectively quantify in the exposure distribution percentile and unable to apply in the mechanistic model because ART is meant to assess exposure across different workplace [xxx].

From reviewed studies, it has been shown that: input power level, welding process type, shield gas, and welding consumable have important impacts on fume formation and generation rates. Accordingly, the applicability of the source-receptor predictive exposure model ART, in view of the specific behaviour of fumes should be consider these factors in the fume exposure modelling as the principal MFs.

8. Discussion

In conclusion, the key modifying factors specific to welding are related to fume formation and generation rates (i.e. welding process type, input electrical power level, shield gas, and welding consumables) and the convective airflows resulting in the dispersion of fumes from the source and the interaction of the welder with the fume plume. These aspects or the ART require modification to include welding within the tool applicability domain; the remainder of the model is considered appropriate for welding fume exposure.

Slater [16] noted that the correlation between fume formation rate and the breathing zone exposure depends on the dispersion of fume. Due to the emission from a welding source tends to generate a reduction in the density within the fluid. The density difference between the ambient environment and the fluid accelerated fume plume vertically upwards. In addition, the ventilation system effect to the flow characteristics of the fume plume.

The welder head positioned directly above arc source seems to be exposed to higher levels than welding vertically with the head to the side [16]. Therefore, interaction between the welder and the source is one of the parameters (modifying factors) influencing fume formation rate.

It is proposed that the ART model for welding fume should to consider in three scenarios, accounting for the interaction between the welder and the welding fume plume. The first scenario demonstrated the chance of another welder colleague to expose fume in a FF scheme (Figure 1). The second scenario showed a welder in NF close to the fume plume (Figure 2). The last scenario illustrated a welder work in the area where NF and the fume plume are the same area (Figure 3).

Figure 8.1 Another welder colleague in a FF scheme

Figure 8.2 A NF close to the WP scheme

Figure 8.3 A NF and the WP are the same area scheme

An understanding of the modifying factors characterization in this paper revealed that need to be collected data from welding simulations and experiments for calibration of the model.

9. Future Plans

9.1 Finalise the literature review of MFs for welding and complete the paper for publishing.

9.2 Identify the relationship between the MFs and the magnitude of the various parameters, for example, different welding processes and welding current or the other identified factors.

9.3 Undertake some modelling work using computational fluid dynamics (CFD) and using a box-model approach, to develop the quantitative parameters that will go into the ART model.

9.4 Sampling of fume particulates generated during welding by setting up situations where someone is welding in a controlled environment.

9.5 Sampling of fume particulates generated during welding in the real workplace by corporation with HBM4EU project.

10. Timeline

Activities	2017				2018				2019
	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4
Develop a topic
Outline proposal
Complete detailed;
– Review of literature
– Construct theoretical framework
– Write review of literature chapter
Write method chapter
Conduct research for study
Analyze data from research
Outline dissertation
Write results chapter
Write conclusions chapter
Polish writing
Submit papers

11. References

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4. BOHS Breathe Freely Controlling Exposures to Prevent Occupational Lung Disease in Industry. http://www.breathefreely.org.uk/why-do-workers-need-protecting.html (June 14),

5. Flynn, M. R.; Susi, P., Modeling mixed exposures: an application to welding fumes in the construction trades. Stochastic Environmental Research and Risk Assessment 2010, 24, (3), 377-388.

6. Hariri, A.; Leman, A. M.; Yusof, M. Z. M.; Paiman, N. A.; Noor, N. M., Preliminary measurement of welding fumes in automotive plants. International Journal of Environmental Science and Development 2012, 3, (2), 146.

7. TWI Welding fume – Do you know your WEL? (July 2006). http://www.twi-global.com/technical-knowledge/published-papers/welding-fume-do-you-know-your-wel-july-2006/ (June 14),

8. Van de Meent, D.; De Bruijn, J., Environmental exposure assessment. In Risk Assessment of Chemicals, Springer: 2007; pp 159-193.

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16. Slater, G. R., Welding fume plume dispersion. 2004.

17. Li, G. J.; Zhang, L.-L.; Lu, L.; Wu, P.; Zheng, W., Occupational exposure to welding fume among welders: alterations of manganese, iron, zinc, copper, and lead in body fluids and the oxidative stress status. Journal of occupational and environmental medicine/American College of Occupational and Environmental Medicine 2004, 46, (3), 241.

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22. Hobson, A.; Seixas, N.; Sterling, D.; Racette, B. A., Estimation of particulate mass and manganese exposure levels among welders. Annals of occupational hygiene 2010, 55, (1), 113-125.

23. Sjögren, B.; Iregren, A.; Frech, W.; Hagman, M.; Johansson, L.; Tesarz, M.; Wennberg, A., Effects on the nervous system among welders exposed to aluminium and manganese. Occupational and environmental medicine 1996, 53, (1), 32-40.

24. Koh, D.-H.; Kim, J.-I.; Kim, K.-H.; Yoo, S.-W., Welding fume exposure and chronic obstructive pulmonary disease in welders. Occupational Medicine 2014, 65, (1), 72-77.

25. Letz, R.; Ryan, P. B.; Spengler, J. D., Estimated distributions of personal exposure to respirable particles. Environmental monitoring and assessment 1984, 4, (4), 351-359.

26. Vermeulen, R.; Stewart, P.; Kromhout, H., Dermal exposure assessment in occupational epidemiologic research. Scandinavian journal of work, environment & health 2002, 371-385.

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28. Schinkel, J.; Ritchie, P.; Goede, H.; Fransman, W.; van Tongeren, M.; Cherrie, J. W.; Tielemans, E.; Kromhout, H.; Warren, N., The Advanced REACH Tool (ART): incorporation of an exposure measurement database. Annals of occupational hygiene 2013, 57, (6), 717-727.

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30. Tielemans, E.; Schneider, T.; Goede, H.; Tischer, M.; Warren, N.; Kromhout, H.; Van Tongeren, M.; Van Hemmen, J.; Cherrie, J. W., Conceptual model for assessment of inhalation exposure: defining modifying factors. Annals of occupational hygiene 2008, 52, (7), 577-586.

31. Kromhout, H.; Swuste, P.; Boleij, J. S., Empirical modelling of chemical exposure in the rubber-manufacturing industry. The Annals of occupational hygiene 1994, 38, (1), 3-22.

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33. Boelter, F. W.; Simmons, C. E.; Berman, L.; Scheff, P., Two-zone model application to breathing zone and area welding fume concentration data. Journal of occupational and environmental hygiene 2009, 6, (5), 298-306.

34. Cherrie, J. W.; Maccalman, L.; Fransman, W.; Tielemans, E.; Tischer, M.; Van Tongeren, M., Revisiting the effect of room size and general ventilation on the relationship between near-and far-field air concentrations. Annals of occupational hygiene 2011, 55, (9), 1006-1015.

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47. Irving, B., Inverter Power Source Check Fume Emissions in GMAW. Welding journal 1992, 53-57.

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1. Introduction