Ann. Occup. Hyg., Vol. 56, No. 5, pp. 557–567, 2012
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doi:10.1093/annhyg/mes025
Exposure to Inhalable, Respirable, and Ultrafine
Particles in Welding Fume
MARTIN LEHNERT1†, BEATE PESCH1*†, ANNE LOTZ1,
JOHANNES PELZER2, BENJAMIN KENDZIA1, KATARZYNA GAWRYCH1,
EVELYN HEINZE1, RAINER VAN GELDER3, EWALD PUNKENBURG4,
TOBIAS WEISS5, MARKUS MATTENKLOTT6, JENS-UWE HAHN7,
CARSTEN MÖHLMANN2, MARKUS BERGES2, ANDREA HARTWIG8,
THOMAS BRÜNING9 and THE WELDOX STUDY GROUP
1
Center of Epidemiology, Institute for Prevention and Occupational Medicine of the German Social Accident
Insurance, Institute of the Ruhr-Universität Bochum (IPA), Bochum, Germany; 2Exposure assessment,
Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA), Sankt
Augustin, Germany; 3Monitoring of working conditions, Institute for Occupational Safety and Health
of the German Social Accident Insurance (IFA), Sankt Augustin, Germany; 4BerufsgenossenschaftHolz
und Metall, Hannover, Germany; 5Human Biomonitoring, Institute for Prevention and Occupational
Medicine of the German Social Accident Insurance, Institute of the Ruhr-Universität Bochum (IPA),
Bochum, Germany; 6Dusts – fibres, Institute for Occupational Safety and Health of the German Social
Accident Insurance (IFA), Sankt Augustin, Germany; 7Chemical agents I, Institute for Occupational
Safety and Health of the German Social Accident Insurance (IFA), Sankt Augustin, Germany; 8Institute
of Applied Biosciences, Food Chemistry, and Toxicology, Karlsruhe Institute of Technology (KIT),
Karlsruhe, Germany; 9Institute for Prevention and Occupational Medicine of the German Social
Accident Insurance, Institute of the Ruhr-Universität Bochum (IPA), Bochum, Germany
Received 6 January 2012; in final form 18 February 2012; published online 26 April 2012
This investigation aims to explore determinants of exposure to particle size-specific welding fume.
Area sampling of ultrafine particles (UFP) was performed at 33 worksites in parallel with the collection of respirable particles. Personal sampling of respirable and inhalable particles was carried
out in the breathing zone of 241 welders. Median mass concentrations were 2.48 mg m23 for inhalable and 1.29 mg m23 for respirable particles when excluding 26 users of powered air-purifying respirators (PAPRs). Mass concentrations were highest when flux-cored arc welding (FCAW)
with gas was applied (median of inhalable particles: 11.6 mg m23). Measurements of particles
were frequently below the limit of detection (LOD), especially inside PAPRs or during tungsten
inert gas welding (TIG). However, TIG generated a high number of small particles, including
UFP. We imputed measurements <LOD from the regression equation with manganese to estimate
determinants of the exposure to welding fume. Concentrations were mainly predicted by the welding process and were significantly higher when local exhaust ventilation (LEV) was inefficient or
when welding was performed in confined spaces. Substitution of high-emission techniques like
FCAW, efficient LEV, and using PAPRs where applicable can reduce exposure to welding fume.
However, harmonizing the different exposure metrics for UFP (as particle counts) and for the respirable or inhalable fraction of the welding fume (expressed as their mass) remains challenging.
Keywords: exposure; inhalable particles; manganese; respirable particles; UFP; welding fume
INTRODUCTION
*Author to whom correspondence should be addressed.
Tel: þ49-(0)234-302-4536; fax: þ49-(0)234-302-4505;
e-mail: pesch@ipa-dguv.de
yThese authors contributed equally to the work.
Welding joins metal pieces by intense heat where
consumable electrodes are frequently applied to
557
558
M. Lehnert et al.
improve the assembly of the larger parts. Fume
arises from the base metal and in particular from
electrodes. Welding fume is a complex mixture of
metals, gases, and other compounds. In addition, it
comprises very small particles, including ultrafine
matter (Berlinger et al., 2011). The exposure of
welders is dependent on several factors, including
the welding process itself, workplace characteristics,
and protective measures (Burgess, 1995).
The exploration of potential determinants of exposure to welding fume requires a large and informative dataset to assess measures needed for the
protection of welders from the hazards of welding
fume (Hewitt, 2001). Preventive measures are for
example local exhaust ventilation (LEV) and the wearing of respirators. Besides experimental data from
inhalation chambers, few datasets from field studies
have been analyzed to assess determinants of exposure
(Flynn and Susi, 2010; Hobson et al., 2011).
The International Agency for Research on Cancer
(IARC) classified welding fume as possibly carcinogenic for humans (Group 2B) (IARC, 1990).The US
Occupational Safety and Health Administration
(OSHA) has not yet set a permissible exposure limit specifically for welding fume. The US National Institute for
Occupational Safety and Health considers welding
fume a potential occupational carcinogen and recommends a reduction in exposure to welding fume to the
lowest feasible level (OSHA, 1997). Furthermore, the
American Conference of Governmental Industrial Hygienists (ACGIH) has currently withdrawn the threshold
limit value for total dust of 5 mg m3 (ACGIH, 2011). In
Germany, the Federal Ministry of Labor and Social Affairs has set an occupational exposure limit (OEL) for
inhalable (10 mg m3) and respirable (3 mg m3) particulate matter, which also applies for welding fume.
Welding is an important source of ultrafine particles
(UFP) and their agglomerates (Antonini, 2003). Threshold limits for UFP are currently under discussion, but
determining an appropriate exposure metric remains
challenging. Whereas weighing has been applied to assess the mass concentrations of respirable and inhalable
particles, particle counts are currently used to measure
UFP exposure. Whether a conversion between these different metrics is feasible for setting OELs has to be explored. However, UFP exposure of welders has not yet
been sufficiently described, and the physico-chemical
characterization of welding fume is under way (Elihn
and Berg, 2009; Buonanno et al., 2011).
The WELDOX study aimed to comprehensively assess the exposure of welders to welding fume and investigate the ensuing health effects. In this analysis, we
evaluate exposure to welding fume according to different size fractions and estimate the influence of potential
predictors on the concentrations in the breathing zone of
welders. Data on exposure to manganese and iron has
been published elsewhere (Pesch et al., 2012).
METHODS
Study population
Between May 2007 and October 2009, 241 welders
from 25 German companies (5 shipyards, 13 manufacturers of containers and vessels, 4 manufacturers of machines and tools) were recruited in the cross-sectional
WELDOX study as described by Pesch et al. (2012).
In brief, representatives of the German Social Accident
Insurance (DGUV) visited the companies to present
the study. Usually, 12 welders per company were selected by the production manager. Four welders in each
shift were equipped with personal samplers on Tuesday,
Wednesday, or Thursday. A trained team of the Institute
for Prevention and Social Medicine of the DGUV conducted the examination throughout the whole study period between 2 p.m. and 4 p.m. The survey included
a face-to-face interview, lung function measurements,
and the sampling of blood, urine, induced sputum, and
exhaled breath condensate for the determination of
various biomarkers. All participants provided written
informed consent. The study was approved by the Ethics
Committee of the Ruhr University Bochum and was conducted in accordance with the Helsinki Declaration.
Exposure data were gathered within the framework
of the measurement system for exposure assessment
of the DGUV, and documented in the MEGA database of measurements at workplaces complied at
the Institute for Occupational Safety and Health of
the DGUV (IFA) (Stamm, 2001; Gabriel et al.,
2010). In addition to the computer-assisted description of the workplaces, photos were taken. Five experts rated the efficiency of the LEV with regard to
the position of the nozzle in relation to plume and
breathing zone and assessed confined work spaces
as locations that strongly restricted air exchange, for
example the double bottom of a ship.
Sampling and determination of welding fume
All welders were equipped with two sampling systems in order to simultaneously determine exposure
to inhalable and respirable particles during a working
shift. Samplers were mounted in the breathing zone
and onto the welders’ face shield facing inward
through a hole as shown in Fig. 1. Personal sampling
of inhalable particles was performed with the German
sampler GSP 3.5, which is equipped with a cellulose
nitrate filter with a pore size of 8 lm and a diameter
of 37 mm and operates at a flow rate of 3.5 l min1.
The GSP sampler is commonly applied in monitoring
Exposure to inhalable, respirable, and ultrafine particles
Fig. 1. Welder equipped with the PGP-EA sampler on the
right side and the GSP sampler on the left side, both facing
inside the shield.
exposure to particles on behalf of the German Social
Accident Insurance (Breuer et al., 2011). For the personal collection of respirable particles, a PGP-EA sampler was applied with a similar cellulose nitrate filter
and flow rate, where a polyurethane filter preselected
particles larger than respirable (Moehlmann,
2006).The average duration of personal measurements
was 3.5 h, ranging from 2 to 5 h. Both samplers comply
with EN 481 respectively International Organization
for Standardization (ISO 7708) (CEN, 1993).
The area sampling of UFP and agglomerates was
performed close to the welder together with a sampling of respirable welding fume (n 5 31) at the
same position. Respirable particles were measured
with a FSP device, which is also equipped with a cellulose nitrate filter in combination with a cyclone
preselector and operates at a flow rate of 10 l min1.
The particle-loaded filters were shipped to the Institute for Occupational Safety and Health of the German
Social Accident Insurance (IFA) for particle and metal
analysis. Dust concentrations were determined by
weighing following the method described by Hahn
(2005) and Hebisch et al. (2005). The procedures complied with the requirements of ISO 15767 (ISO, 2006).
Before weighing, the unloaded and particle-loaded sampling media were equilibrated to the laboratory atmosphere for at least 1 day. Environmental conditions,
such as humidity, were considered by calibration. The
limit of deduction (LOD) was three times the standard
deviation of the weight difference (weights determined
before and after shipment) for a minimum of ten unloaded filters having undergone the complete procedure,
including transport to the measurement site and back.
Ninety personal measurements of respirable welding
559
fume and 33 measurements of inhalable particles, both
collected with PGP-EA, and 47 measurements of inhalable particles collected with GSP 3.5 were below LOD.
Manganese (Mn) was determined by inductively
coupled plasma mass spectrometry (ICP-MS) with
a Perkin Elmer Elan DRC II (Waltham, MA) as described by Pesch et al (2012). The sample preparation
corresponded to a German standard protocol (Hebisch
et al., 2005). In brief, the loaded cellulose nitrate filters
were digested with 10 ml of a mixture of nitric acid and
hydrochloric acid. This solution was heated for 2 h under reflux in a heating block at 130C. After cooling to
room temperature, the solution was diluted with 10 ml
of ultrapure water to dilute the viscous solution before
ICP-MS analysis was carried out. ICP mass spectrometer was calibrated with different multi-element standard solutions covering the range of analytes. The
isotopes 45Sc, 85Rb, and 165Ho were used as internal
standards. Five measurements of respirable manganese were below the limit of quantitation.
Particles of an electrical mobility diameter between 14 and 673 nm were counted by a Scanning
Mobility Particle Sizer (SMPS) (TSI, Aachen,
Germany) at 33 workplaces. We further refer to these
particles as UFP including their agglomerates (UFP).
The SMPS device measured the size distribution of
submicron particles in real time by determining their
mobility-equivalent diameters with an electrostatic
classifier (Brouwer et al., 2004). Their concentration
was measured with a condensation particle counter.
Data were evaluated and stored by TSI AIM 8.0 software, including diffusion correction. The median
duration of measurements was 3 h.
Statistical methods
All calculations were performed with the statistical
software SAS, version 9.2 (SAS Institute Inc., Cary,
NC). We presented the number of observations, the
number of measurements ,LOD, median, and interquartile range (IQR) to describe the distribution of
the exposure variables. Different LODs resulted from
the varying duration of measurements. A concentration .LOD measured during a longer sampling period
could be lower than a value ,LOD due to a shorter
sampling time. For these left-censored variables, the
summary statistics cannot be computed by common
methods. Instead, we calculated the percentiles by first
substituting values ,LOD by their corresponding
LOD. Then we estimated the percentiles. If the maximum of LODs was higher than the calculated percentile, we marked this percentile by a less-than (,) sign.
Associations between two exposure variables
were presented by Spearman rank correlation coefficients (rs) with 95% confidence intervals (CIs).
560
M. Lehnert et al.
Using scatter plots, we depicted values ,LOD with
2/3 LOD because multiple imputations, as applied in
modeling, would yield a set of estimates per value.
The variables were log-transformed due to their
skewed distributions for model building and parametric tests. Potential determinants of the weldingfume concentrations were explored with multiple
regression models. The regression coefficients were
presented with 95% CIs at the original scales,
exp(b). Concentrations ,LOD were imputed 100
times from the regression with the Mn concentrations where nearly all concentrations were measurable. This relation was fitted through a Tobit
regression model (Tobin, 1958) with welding fume
as dependent variable and Mn as independent variable. Afterward, the results of the Tobit regression
model were used to impute the mass concentrations
of respirable welding fume for measurements
,LOD for a 100 times according
to the following
ˆ
model:ywelding fume 5exp â xbMn . Finally, the imputed data were used in multiple linear regression
models and the combined effect estimates for potential predictors were presented (Rubin, 1987).
According to Harel (2009), estimations of adjusted
R2 were presented as measures of the model fit.
RESULTS
Study population
Characteristics of the 241 welders and their working conditions are shown in Table 1. The participants
were enrolled from shipyards (n 5 56), manufacture
of containers and vessels or related products (n 5
139), and machine or tool building (n 5 46). The
welding techniques comprised gas metal arc welding
with solid wire (GMAW) (n 5 95) or flux-cored wire
(FCAW) (n 5 47), tungsten inert gas welding (TIG)
(n 5 66), and shielded metal arc welding (SMAW)
with stick electrodes (n 5 20). Additionally, 13
welders performed more than one welding technique
during the shift. We took the consumable electrode
for classification of the material into account because the majority of the fume originates from the
electrodes. If no consumable material was applied,
we considered the base metal alloy for classification.
Steel, with a content of ,5% in mass of any metal
other than iron, was classified as ‘mild steel’ (n 5
83), and iron-based alloys with a content of at least
5% of chromium were classified as ‘stainless steel’
(n 5 148). The category ‘others’ comprised other alloys and the welding of different materials during the
shift (n 5 10). Special helmets with powered airpurifying respirators (PAPR) were used by 26 welders. These PAPRs were motorized systems that use
Table 1. Characteristics of 241 welders enrolled in the
WELDOX study.
Variable
Category
n (%)
Industry
Shipyard
Manufacture of
containers and vessels
56 (23.2%)
139 (57.7%)
Welding process
Material
Respiratory
protection
Machine and
tool building
46 (19.1%)
GMAW
95 (39.4%)
FCAW
47 (19.5%)
TIG
66 (27.4%)
SMAW
20 (8.3%)
Miscellaneous
13 (5.4%)
Stainless steel
148 (61.4%)
Mild steel
Others
83 (34.4%)
10 (4.1%)
PAPR
26 (10.8%)
Maintenance-free
particulate
respirator (dust mask)
49 (20.3%)
None
166 (68.9%)
Efficient LEV
Yes
54 (22.4%)
Confined space
No
Yes
187 (77.6%)
23 (9.5%)
No
218 (90.5%)
a filter to clean ambient air before it is delivered to
the breathing zone of the worker. Another 49 welders
used maintenance-free particulate respirators hereinafter referred to as dust masks. LEV was efficiently
used by 54 welders, and 23 welders worked in
confined spaces. The median age of the welders
was 41 years (range 19–61 years).
Exposure to respirable welding fume
The concentrations of respirable fume from personal measurements ranged from measurements
,LOD up to 21.5 mg m3. All but one welder using
PAPRs had concentrations of respirable welding
fume ,LOD (Fig. 2). PAPR was not used in TIG
welding. When excluding PAPR users, the median
concentration was 1.29 mg m3 and varied by welding process with higher concentrations for FCAW
(median 8.02 mg m3) and measurements frequently
below LOD for TIG (Table 2). Dust masks were
commonly used in settings with elevated fume
concentrations (Fig. 3).
Ninety (37%) measurements of the respirable
fume were below LOD. It is noteworthy that LODs
were inversely associated with the duration of sampling as shown in Fig. 4. We imputed welding-fume
Exposure to inhalable, respirable, and ultrafine particles
Fig.2. Respirable welding fume inside PAPRs and in welders
without PAPR (excluding workers applying TIG and
miscellaneous techniques during the shift).
values ,LOD as described by employing the strong
correlation between respirable Mn and welding fume
(rs 5 0.92, 95% CI 0.90–0.94, in the range of measurable data) (Fig. 5). The correlation with Mn was
stronger than the corresponding association with
iron (rs 5 0.88, 95% CI 0.84–0.91, in the range of
measurable data). Therefore, manganese was chosen
for imputation of welding-fume data ,LOD. The
Tobit regression equation for the log-transformed
concentrations
was
ywelding fume 5exp 3:06
2
0:73
xMn (pseudo R 5 0.91).
From this dataset of welding-fume concentrations,
we further excluded 26 welders with PAPRs that
were mostly below LOD in order to estimate the
potential determinants of exposure to respirable
welding fume. Table 3 presents the effect estimates
from multiple regression analysis. The mass concentrations of respirable particles were mainly predicted
by the welding process and modified by workplace
characteristics. TIG was associated with 0.18 (95%
CI 0.142–0.27) fold lower and FCAW with 2.25
(95% CI 1.52–3.32) fold higher concentrations than
GMAW. Welding of stainless steel was associated
with 0.55 fold lower concentrations in comparison
to mild steel. Working in confined spaces increased
exposure by a factor of 1.87 (95% CI 1.17–2.99).
Efficient LEV reduced the concentrations by a factor
of 0.43 (95% CI 0.29–0.6).The model fit, assessed as
R2, was 0.65. Two other statistical approaches (Tobit
regression, multiple imputations) dealing with values
below LOD revealed similar results (data not shown).
561
Exposure to inhalable welding fume
Figure 6 presents the association between respirable and inhalable fume collected with the same PGPEA sampler. The data pairs were highly correlated at
their log-transformed scales within the range of measurable fume (log10 y(respirable) 5 0.329 þ 1.061
log10 x(inhalable), adjusted R2 5 0.91). This strong
association also explains a similar pattern of determinants of the concentrations as shown in Tables 2
and 3.
Figure 7 shows the distributions of inhalable welding fume from side-by-side measurements with
two samplers (GSP 3.5 and PGP-EA). The logtransformed concentrations were highly correlated
within the range of measurable fume with a good
model fit (log10 y(PGP-EA) 5 0.209 þ 0.832 log10
x(GSP), adjusted R2 5 0.79). Concentrations determined by PGP-EA (median 2.48 mg m3) were systematically higher than those determined by GSP 3.5
(median 1.51 mg m3) (Table 2). When including
the 26 PAPR users, the concentrations were slightly
lower.
Area sampling of respirable and UFP
Area sampling revealed a median count of
120 000 cm3 (IQR 100 000–160 000) in the particle
size ranging from 14 to 673 nm comprising UFP and
their agglomerates over all five welding processes
(Table 2). Figure 8 shows the relation between particle diameters and number concentrations by welding
technique measured stationary at 33 worksites in
total, with .20 scans per worksite. TIG welding
generated smaller particles most of which were
,100 nm, whereas GMAW, FCAW, and SMAW
yielded larger particle agglomerates that were
mainly .100 nm.
Figure 9 depicts the association of particle counts
of UFP and their agglomerates with the mass concentration of respirable particles at the same sampling location. The Spearman rank correlation was
rs 5 0.42 (95% CI 0.04–0.69) within the range of
measurable data. The stationary sampling near
31 welders revealed a median concentration of
0.93 mg m3 for respirable particles (Table 2) and
showed no clear correlation with the corresponding
personal measurements (rs 5 0.31, 95% CI 0.18,
0.66, in the range of measurable data).
DISCUSSION
This investigation aimed to characterize exposure
to welding fume in different particle size distributions in 241 welders. In the past, welding fume has
562
Table 2. Exposure to respirable and inhalable welding fume and to UFP in welders (excluding users of PAPRs).
Personal measurements
Inhalable particles (GSP), mg m3
n
n,LODa
177
27
GMAW
62
FCAW
Inhalable particles (PGP-EA), mg m3
IQR
n
n,LODa
1.51
,0.65, 4.50
215
20
2.48
2
3.65
1.80, 5.69
78
1
22
0
8.02
2.83, 12.50
42
TIG
64
22
,0.58
,0.42, 0.93
SMAW
17
3
1.12
0.52, 3.55
Total
Median
UFP, number concentration (x1000 cm )
GMAW
FCAW
TIG
SMAW
a
14–673
14–100
n,LODa
1.10, 6.81
215
65
1.29
,0.45, 4.01
4.41
2.36, 6.36
78
9
2.08
1.20, 3.78
0
12.90
7.98, 15.50
42
0
7.11
66
17
,0.96
,0.77, 1.41
66
47
,0.42
,0.36, ,0.51
17
1
1.65
1.17, 2.93
17
8
,0.49
,0.45, 1.85
Respirable particles (mg m )
n,LODa
Median
IQR
n
33
33
—
—
124.6
67.2
100.8, 161.2
47.2, 96.6
31
4
0.93
13
0
10
14–673
13
—
126.8
108.5, 167.0
14–100
13
—
63.3
52.5, 88.2
14–673
10
—
122.3
97.5, 140.7
14–100
10
—
49.3
43.7, 77.7
14–673
6
—
151.3
124.6, 181.5
14–100
6
—
109.5
96.2, 156.8
14–673
14–100
4
4
—
—
91.3
53.6
56.6, 143.4
40.8, 76.5
n,LODa
Median
IQR
4.53, 10.10
Personal measurements
Respirable particles (mg m3)
3
n
N,LOD, number of observations below the limit of determination.
IQR
Stationary measurements
3
Total
n
Median
n
n,LODa
0.54, 1.60
31
12
1.37
,0.41, 5.58
1.00
0.74, 1.53
13
4
1.86
,0.43, 3.78
0
1.24
0.83, 7.42
10
2
6.23
1.37, 7.08
6
3
,0.21
,0.13, 0.75
6
4
,0.40
,0.34, 0.70
2
1
,0.91
,0.18, 1.63
2
2
,0.54
,0.49, ,0.58
Median
IQR
Median
IQR
M. Lehnert et al.
Subgroup with UFP
measurements
Particle size (nm) Stationary measurements
Respirable particles (PGP-EA), mg m3
Exposure to inhalable, respirable, and ultrafine particles
Fig. 3. Comparison of respirable welding fume concentrations
in the breathing zones of welders using dust masks and welders
not using dust masks (excluding welders using PAPR).
Fig. 4. Concentrations of respirable welding fume by
sampling duration.
been commonly measured as total dust or inhalable
particles (Hobson et al., 2011). However, respirable
particles reach the alveoli and are more specific with
regard to lung diseases and systemic metal exposure
from the welding fume. In this WELDOX study, we
measured respirable and inhalable welding fume in
parallel. A large fraction of respirable welding fume
measurements was ,LOD but concentrations were
also measured that were higher than 3 mg m3,
which is the German OEL for this particle size fraction [Federal Institute for Occupational Safety and
Health (BAuA), 2006]. The welding technique was
563
Fig. 5. Association between concentrations of respirable
welding fume and respirable manganese and the regression
line with 95% confidence intervals from the Tobit model.
a major determinant of the mass concentrations
when excluding PAPRs where concentrations were
mostly ,LOD. Mass concentrations were highest
using FCAW and lowest using TIG. Furthermore,
higher exposures occurred when welding mild steel
than stainless steel in confined spaces, or when
LEV was not efficiently used. Stationary measurements of UFP and their agglomerates near the welder
revealed smaller particles when applying TIG.
The side-by-side measurements at workplaces
showed that respirable particles comprised about
half of the mass of the inhalable particles in the
welding fume. The particles were measured simultaneously with the PGP-EA device where a foam layer
separated respirable particles from larger particles.
It has to be noted that this relationship between
respirable and inhalable particles may not hold for
certain metals in the welding fume. For example,
manganese occurs in welding fume mostly as respirable particles (Harris et al., 2005; Pesch et al.,
2012). It is important to note that our results were
derived in a large group of welders applying a variety
of welding techniques and not in an experimental
setting.
With regard to the variation of exposure by welding process, the mass concentration of inhalable
particles was on average ,1 mg m3 in TIG
welding, supporting previously published levels
(0.16–1.10 mg m3) (Burgess, 1995; Hobson et al.,
2011). High mass concentrations previously reported
for FCAW could be also confirmed (Kiefer et al.,
1998). The average concentrations of inhalable
particles collected in parallel with two samplers
564
M. Lehnert et al.
Table 3. Potential determinants of exposure to respirable and inhalable welding fume (excluding users of PAPRs).
Factor
Respirable, n 5 215
n
n,LODa
�3
Intercept (mg m )
Inhalable (GSP), n 5 177
Expðb̂Þ
95% CI
2.72
2.12–3.49
n
n,LODa
Expðb̂Þ
95% CI
4.02
3.11–5.20
Gas metal arc welding
78
9
1
62
2
1
FCAW
42
0
2.25
1.52–3.32
22
0
1.68
TIG
66
47
0.18
0.12–0.27
64
22
0.19
0.13–0.29
Shielded metal arc welding
Miscellaneous
17
12
8
1
0.68
1.13
0.37–1.26
0.61–2.10
17
12
3
0
0.78
1.93
0.46–1.34
0.53–1.62
1
Mild steel
1.10–2.58
83
5
59
0
Stainless steel
122
58
0.55
0.39–0.79
108
25
0.74
0.50–0.10
Miscellaneous
10
2
0.83
0.43–1.58
10
2
1.19
0.66–2.17
193
65
165
27
22
0
12
0
167
47
1
130
20
48
18
0.43
0.29–0.64
0.65b (0.57–0.73)
47
7
Nonconfined space
Confined space
Nonefficient LEV
Efficient LEV
R2 (95% CI)
a
1
1.87
1.17–2.99
1
1
1.37
0.81–2.29
1
0.45
0.32–0.64
0.59b (0.49–0.68)
N,LOD,
b 2
number of observations below the limit of determination.
R (Harel, 2009).
Fig. 6. Association between respirable and inhalable welding
fume (sampled with PGP-EA).
Fig. 7. Association between inhalable welding fume sampled
with different devices (PGP-EA and GSP).
(medians 8.0 and 12.9 mg m3) correspond to
mean concentrations reported from other studies
(6.3–24.2 mg m3) (Hobson et al., 2011). We determined somewhat higher concentrations for GMAW
(3.7–4.4 mg m3) when compared to literature
where means ranged from 1.0 to 2.9 mg m3
(Hobson et al., 2011). The improvement or application of welding techniques, with regard to a lowering
of emission rates, should be taken into account for
future reductions of the welders’ exposure.
About 30% of the welders used respiratory protection, mostly in high-exposure settings. A strong
reduction in exposure to welding fume was observed
inside of PAPRs where most measurements appeared
,LOD (see also Myers and Peach, 1983). However,
PAPRs may hinder the welder’s movement in confined spaces, for example in shipbuilding and the inside of vessels. The protective effect of the dust mask
could not be directly assessed because aerosol sampling behind the mask was not possible. However, it
was assessed indirectly by comparing the results of
Exposure to inhalable, respirable, and ultrafine particles
Fig. 8. Particle size distributions measured by SMPS (number
count versus particle diameter in the range 14–673 nm)
averaged over the worksites for different welding techniques
(GMAW, FCAW, TIG, and SMAW).
Fig. 9. Association between counts of UFP and their
agglomerates (14–673 nm) with mass concentration of
respirable particles from side-by-side area measurements.
biological monitoring for those with and without
dust masks (Pesch et al., 2012).
Significant reduction of exposure to welding fume
was observed when LEV was used efficiently.
Nearly 2-fold higher concentrations were measured
among welders working in confined spaces. Both
factors, efficient LEV and confined space, were assessed by an expert panel using various documentations of the workplaces, including photos and
computer-assisted descriptions from the German
MEGA database of measurements (Gabriel et al.,
2010). Efficiency of LEV was predominantly affected by proper handling of the device, for example
by positioning the nozzle inside the plume. Only one
in four welders used LEV efficiently. Further improvements could be, for example, the integration
of the device into the torch or the integration of
565
a lamp into the device for an indirect improvement
of the nozzle position due to better illumination of
the workplace. Accumulation of welding fume in
confined spaces can be better avoided by a forced
particle extraction rather than by dilution ventilation
(Wurzelbacher et al., 2002). Lower exposure to
welding fume in workers processing stainless steel
maybe attributed to different process parameters
(e.g. shielding gas mixture, operating speed, thickness of the wire, adjustment of the welding unit)
(Fiore, 2008). Furthermore, better ventilation in
stainless steel works could not be ruled out.
Few studies applied statistical modeling to explore
potential determinants of exposure, for example
analyses conducted by Hobson et al. (2011) or Flynn
and Susi (2010). Effects can be estimated with sufficient confidence in large and informative datasets. In
our study with detailed information on the welders’
working circumstances, the limited sensitivity of
weighing particles was challenging. A low particle–mass concentration in combination with a short
duration of sampling kept the sampled mass below
the analytical limits of detection. A substitution of
data ,LOD, commonly by LOD/2 or 2/3 LOD
yields biased estimates. Various methods have been
employed to avoid substantial bias by substitution
(Helsel, 2006). In this study, three different methods,
i.e. multiple imputation based on the regression between welding fume and Mn, Tobit regression, and
multiple imputation based on the distribution of measurable welding-fume concentrations, were applied to
deal with censored exposure data in the regression
models. These different statistical approaches yielded
similar effect estimates. Therefore, we presented the
results of multiple imputation based on the Mn regression only. All models fitted the data well and explained 60 to 70% of the variance of the exposure
to welding fume. The good model fit is in line with
an analysis of Hobson et al. (2011) and may be due
to the large difference in particle–mass concentrations
between the welding techniques.
Methodological issues also influence the performance of the devices for sampling according to a defined particle size distribution (Kenny et al., 1997).
Furthermore, the location of the sampler on the
welder’s body may also influence the collected fume
(Goller and Paik, 1985). Sampling of inhalable welding fume was performed simultaneously with two
different devices working at the same flow rate of
3.5 l min1. The PGP-EA sampler was predominately mounted on the right side of the welder and
the GSP 3.5 sampler on the left side, both within
the breathing zone of the welder and facing inwards
the shield. Slightly higher concentrations of inhalable
566
M. Lehnert et al.
particles were determined with the PGP-EA sampler
compared with the German standard device GSP 3.5.
UFP, i.e. particles with a mobility diameter
of 100 nm, have received growing attention due
to their possible health effects, and measurement
methods are under way for application at the workplace (ISO, 2006; Moehlmann, 2007). Few field
studies have been conducted in occupational settings
(Elihn and Berg, 2009; Buonanno et al., 2011). Due
to the size of the stationary SMPS device and the
spatial conditions at the workplace, area sampling
of UFP and its agglomerates was performed close
to the welder at 33 selected workplaces with varying
distances. In addition, an FSP device working at
a flow rate of 10 l min1, was positioned at the same
place to collect respirable particles. In comparison to
the personal samplers, that operated at 3.5 l min1,
area sampling revealed 40% lower concentrations
than personal sampling in the breathing zone.
Particle concentrations can be assessed by mass,
counts, or surface area. The alloy and fluxing
compounds may modify the fume characteristics
(Wurzelbacher et al., 2002; Zimmer, 2002). In addition, temperature, humidity, and air motion can also
influence, to an unknown extent, the agglomeration
of particles. A physicochemical characterization of
the welding fume revealed that particles ,50 nm
were mostly metal oxides in contrast to larger particles that also contained nonmetal components
(Berlinger et al., 2011). TIG, although having the
lowest level of exposure to welding fume in terms
of mass, generated a larger number proportion of
small particles than other techniques in this study,
as shown in more detail by Pelzer et al. (2011). It
is important to note that TIG is commonly applied
to stainless steel. The number of particles with
a diameter ,100 nm was about twice the number
produced by other techniques. According to our
knowledge, no other study has so far examined
UFP exposure by welding technique.
Harmonizing different exposure metrics has been
a methodological challenge in occupational epidemiology, for example in quartz research (Seixas
et al., 1997; Dahmann et al., 2008a, 2008b). Less
is known about the feasibility of a conversion
between mass and particle counts with regard to
welding fume. Our subset of side-by-side measurements of respirable particles expressed as mass concentration and UFP with agglomerates expressed as
particle counts show a weak correlation. The median
particle size and the respirable mass concentration
were stronger associated (Pelzer et al., 2011). Larger
datasets are needed to conclude whether a conversion
of both metrics is possible for welding fume.
CONCLUSION
The welding process is the major determinant of
the exposure to particles in different size fractions.
The highest mass concentrations were found in
FCAW, followed by GMAW and SMAW, whereas
mass concentrations determined during TIG were
frequently below LOD. Although TIG appeared with
the lowest concentrations in terms of particle mass,
we observed larger numbers of small-sized particles,
including UFP. An inefficient use of LEV or working
in confined spaces can increase the exposure of
welders. PAPRs reduced exposure considerably but
their use is less feasible in confined spaces. The
substitution of high-emission techniques and the introduction of automated welding technologies, in
addition to improvements of the ventilation and
respiratory protection may successfully reduce exposure to welding fume.
FUNDING
German Social Accident Insurance (DGUV) to the
WELDOX study.
Acknowledgements—We thank the staff working for the MGU
measurement system, and all welders having participated. We
gratefully acknowledge the field team, especially Sandra
Zilch-Schöneweis, Hans Berresheim, and Hannelore
Ramcke-Kruell.
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