Occupational exposure during production
of wood pellets in Sweden
Örebro Studies in Environmental Science 11
Katja Hagström
Occupational exposure during production
of wood pellets in Sweden
© Katja Hagström, 2008
Title: Occupational exposure during production
of wood pellets in Sweden
Publisher: Örebro University 2008
www.oru.se
Editor: Maria Alsbjer
[email protected]
Printer: Intellecta DocuSys, V Frölunda 01/2008
issn 165o-6278
isbn 978-91-7668-571-6
Abstract
Katja Hagström (2008): Occupational exposure during production of wood
pellets in Sweden. Örebro Studies in Environmental Science 11. 75 pp.
The aims of the studies underlying this thesis were to assess workers’ air exposure
to wood dust and various chemicals, and to evaluate the variability in exposure
and occupational dermal exposure to resin acids during the production of wood
pellets in Sweden. Personal air measurements of wood dust, monoterpenes, resin
acids and nitrogen dioxide (as a marker of diesel exhaust), accompanied by area
measurements of these substances, VOCs and carbon monoxide, were performed
at up to ten plants. Repeated measurements were also performed to evaluate
within- and between-worker variability, determinants of exposure, the probability
that a worker’s mean exposure exceeded the occupational exposure limit, OEL
(overexposure), and the bias in the exposure-response relationship (attenuation).
Dermal exposure was measured at the forehead, neck, forearm and hand using a
tape-stripping method, in which a strip of adhesive tape is applied to the skin and
then removed along with the outermost layer of the skin and chemicals adsorbed
to this layer.
The workers’ exposure to wood dust was high (mean: 2.4 mg/m3), with
35−42 % of the measurements above the Swedish OEL of 2 mg/m3. The exposure
is also classified as unacceptable due to the calculated levels of overexposure. Exposure to resin acids like 7-oxodehydroabietic acid and dehydroabietic acid was
identified, which has not been previously observed in the wood industry, with
mean sum levels of 2.4 Pg/m3. Levels of monoterpenes, nitrogen dioxide, VOCs
and carbon monoxide were all below their Swedish OELs. A factor that influenced the level of exposure to wood dust and resin acids was the nature of the
work done, notably cleaning operations, like sweeping, which increased the exposure slightly. The attenuation was high for the individual-based model, and at
least 12 repeated measurements were needed to yield a bias in the exposureresponse relationship of d10 %. The results also showed that dermal exposure to
resin acids occurs in these plants, which has not been shown before, and provided
indications of both increased exposure during a work shift and diffusion into the
skin. The main conclusion is that wood dust exposure at these levels is likely to
have implications for the workers’ health in the long run, and, therefore, it is important to reduce exposure to wood dust in this industry.
Keywords: Occupational hygiene, wood dust, resin acids, variance analyses,
determinant of exposure, overexposure, dermal exposure.
Publications
This thesis is based in the following papers, which are referred to in the text by
the corresponding Roman numerals:
I.
Edman K*, Löfstedt H, Berg P, Eriksson K, Axelsson S, Bryngelsson I,
Fedeli, C. (2003) Exposure assessment to alpha- and beta-pinene, delta(3)carene and wood dust in industrial production of wood pellets. Ann Occup Hyg; 47: 219−26.
II.
Hagström K, Axelsson S, Arvidsson H, Bryngelsson I, Lundholm C, Eriksson K. (2008) Exposure to wood dust, resin acids and volatile organic
compounds during production of wood pellets. JOEH, vol 5, in press.
III.
Hagström K, Lundholm C, Eriksson K, Liljelind I. (2008) Variability and
determinants of wood dust and resin acid exposure during wood pellet
production: measurement strategies and bias in assessing exposureresponse relationships. Ann Occup Hyg, submitted.
IV.
Eriksson K, Hagström K, Axelsson S, Nylander-French L. (2008) Tapestripping as a method for measuring dermal exposure to resin acids during
wood pellet production. J Environ Monit, DOI:10.1039/b719152a, in
press.
* Previous surname of Katja Hagström.
Abbreviations
7OXO
7-Oxodehydroabietic acid
AA
Abietic Acid
AM
Arithmetic Mean
ANOVA
Analysis Of Variance
DataRAM
Data-logging Real-time Aerosol Monitor
DHAA
Dehydroabietic Acid
DOEL
Dermal Occupational Exposure Limit
ESI
Electrospray Ionisation
FEV1
Forced Expiratory Volume in 1 second
FTIR
Fourier Transform Infrared spectroscopy
GC
Gas Chromatography
GM
Geometric Mean
GSD
Geometric Standard Deviation
HPLC
High Performance Liquid Chromatography
IARC
International Agency for Research on Cancer
LC
Liquid Chromatography
LOQ
Limit Of Quantification
MS
Mass Spectrometry
OEL
Occupational Exposure Limit
PA
Pimaric Acid
RPE
Respiratory Protective Equipment
SC
Stratum Corneum
SIM
Single Ion Monitoring
TWA
Time-Weighted Average
TVOC
Total amount of Volatile Organic Compounds
VOC
Volatile Organic Compounds
Content
1
2
INTRODUCTION ............................................................................................................. 13
BACKGROUND................................................................................................................. 15
2.1
WOOD PELLET PRODUCTION IN SWEDEN ...................................................................... 15
2.2
EXPOSURES DURING WOOD PELLET PRODUCTION .......................................................... 16
2.2.1
2.2.2
2.2.3
2.3
2.3.1
2.3.2
2.3.3
2.3.4
2.4
2.4.1
2.4.2
2.5
2.5.1
2.5.2
2.6
3
Wood dust.............................................................................................................. 17
Monoterpenes......................................................................................................... 20
Resin acids ............................................................................................................. 20
AIR EXPOSURE ASSESSMENTS ........................................................................................ 21
Dust monitoring..................................................................................................... 21
Diffusive sampling.................................................................................................. 22
Peak exposures....................................................................................................... 23
Variability in air exposure...................................................................................... 24
DERMAL EXPOSURE ASSESSMENTS ................................................................................. 25
General .................................................................................................................. 25
The tape-stripping method ..................................................................................... 26
THE PROJECTS ............................................................................................................. 26
Study I.................................................................................................................... 26
Study II .................................................................................................................. 27
OBJECTIVES ................................................................................................................. 27
STUDY DESIGN AND ANALYSIS .................................................................................... 29
3.1
WOOD PELLET PRODUCTION PLANTS ............................................................................ 29
3.2
SUBJECTS ..................................................................................................................... 29
3.3
AIR EXPOSURE MEASUREMENTS (PAPERS I-III)............................................................... 30
3.3.1
3.3.2
3.3.3
3.3.4
Study I.................................................................................................................... 30
Study II .................................................................................................................. 30
Wood dust.............................................................................................................. 31
Real-time monitoring of dust ................................................................................. 31
3.3.5
3.3.6
Monoterpenes......................................................................................................... 32
Resin acids, nitrogen dioxide, VOCs and carbon monoxide .................................. 32
3.4
DERMAL EXPOSURE MEASUREMENTS (PAPER IV)........................................................... 33
3.4.1
The tape-stripping method ..................................................................................... 33
3.4.2
In vivo study, recovery and stability tests ............................................................... 33
3.4.3
Field study.............................................................................................................. 34
3.5
ANALYSIS OF RESIN ACIDS (PAPERS II-IV) ...................................................................... 34
3.6
STATISTICS ................................................................................................................... 35
3.6.1
General .................................................................................................................. 35
3.6.2
3.6.3
3.6.4
3.6.5
Estimation of variance components (Paper III)...................................................... 36
Identification of determinants (Paper III)............................................................... 37
Overexposure (Paper III) ....................................................................................... 37
Attenuation (Paper III)........................................................................................... 38
4
RESULTS............................................................................................................................ 39
4.1
Wood dust.............................................................................................................. 39
Monoterpenes......................................................................................................... 41
Resin acids ............................................................................................................. 43
Nitrogen dioxide, VOCs and carbon monoxide..................................................... 43
4.1.5
4.1.6
4.1.7
Estimated variation components and determinants of exposure............................. 44
Overexposure......................................................................................................... 45
Attenuation ............................................................................................................ 46
4.2
5
AIR EXPOSURE (PAPERS I-III) ........................................................................................ 39
4.1.1
4.1.2
4.1.3
4.1.4
DERMAL EXPOSURE (PAPER IV) .................................................................................... 46
4.2.1
In vivo study, recovery and stability tests ............................................................... 46
4.2.2
Field study.............................................................................................................. 47
DISCUSSION...................................................................................................................... 51
5.1
AIR EXPOSURE (PAPERS I-III) ........................................................................................ 51
5.1.1
5.1.2
5.1.3
Wood dust.............................................................................................................. 51
Monoterpenes......................................................................................................... 52
Resin acids ............................................................................................................. 52
5.1.4
5.1.5
5.1.6
5.1.7
Nitrogen dioxide, VOCs and carbon monoxide..................................................... 53
Estimated variation components and determinants of exposure............................. 54
Overexposure......................................................................................................... 55
Attenuation ............................................................................................................ 56
5.2
DERMAL EXPOSURE (PAPER IV) .................................................................................... 57
5.2.1
In vivo study, recovery and stability tests ............................................................... 57
5.2.2
Field study.............................................................................................................. 57
5.2.3
5.3
Dermal occupational exposure limits ..................................................................... 59
GENERAL DISCUSSION .................................................................................................. 59
6
CONCLUSIONS................................................................................................................. 61
7
ACKNOWLEDGMENTS ................................................................................................... 63
8
REFERENCES .................................................................................................................... 65
1 Introduction
A major aim of environmental and energy policies in Sweden is to replace fossil
fuels with renewable sources, such as biofuels, e.g. wood pellets, which are produced in Sweden by compressing shavings and sawdust, mainly from pine and
spruce wood. Wood pellet production is an expanding industry in Sweden, other
European countries and North America. However, wood industry workers who
treat and handle pine and spruce are exposed to wood dust, as well as monoterpenes (Demers et al., 2000; Eriksson et al., 1997; Eriksson et al., 1996; Rosenberg et al., 2002) and resin acids (Demers et al., 2000; Fransman et al., 2003).
Additional substances that may be of concern in terms of exposure during this
process are volatile organic compounds (VOCs) emitted from sawdust (Svedberg
et al., 2004; Arshadi and Gref, 2005), carbon monoxide released via bacterial
breakdown of wood (Svedberg et al., 2004), and diesel exhaust emitted from
trucks used during production.
It is widely recognised that exposure levels vary within and between workers
(Rappaport, 1991) and in order to assess this variability, repeated measurements
are performed. The results from such measurements have been used to characterise determinants of exposure (Nylander-French et al., 1999; McClean et al.,
2004), establish uniformly exposed groups, (Rappaport, 1991), and calculate
overexposure (Rappaport et al., 1995) and the underestimation in potential exposure-response relationships (Kromhout et al., 1996). Exposure can occur not only
by inhalation but also by dermal contact. Since resin acids, for example, can
cause contact dermatitis (Sadhra et al., 1994; Keira et al., 1997), dermal exposure
to these substances is of particular interest. One method that is used to assess
dermal exposure is a tape-stripping technique, in which a strip of adhesive tape is
applied to an area of the skin and then removed, along with the skin’s outermost
layer and any chemicals adsorbed to it (Chao and Nylander-French, 2004). The
aims of the studies described in this thesis were to investigate air exposure to
wood dust and various chemicals, such as monoterpenes, resin acids, nitrogen
dioxide, VOCs and carbon monoxide, as well as dermal exposure to resin acids,
during the production of wood pellets, and to evaluate the variability in the data.
13
2 Background
2.1 Wood pellet production in Sweden
Biofuels are being used increasingly in Sweden, since they are renewable sources
of energy and do not contribute to the increased greenhouse effect, and in 2005
they supplied 22 % of the country’s energy needs (SCB, 2006). Biofuels can be
dried and compressed, processes that increase their quality and energy efficiency,
and facilitate their handling and storage (Olsson, 2006). Wood pellets (Figure 1)
are a kind of processed biofuel, and their production in Sweden began in the early
1980’s. Today, Sweden is the second largest producer in the world (PIR, 2007).
In a five-year period production has doubled and in 2006 around 1.5 million
tonnes of wood pellets were produced in Sweden. Most of the wood pellets produced are used in industry and district heating plants, although some 36 % are
used by private households (PIR, 2007). Around 20 plants across the country
produce wood pellets.
(a)
(b)
Figure 1. Wood pellets (a) and the pellets matrix (b) in which the raw material is pressed.
As stated previously, wood pellets are produced by compressing wood shavings
and sawdust obtained from planning mills and sawmills, and Scotch pine (Pinus
sylvestris) and European spruce (Picea abies) are the raw materials most commonly used in Sweden. Wood pellets have diameters of 5 to 12 mm and moisture
contents of 6-10 % (w/w). During the production of wood pellets, sawdust is
dried before grinding but shavings can be ground directly (Figure 2). The raw
material is then pressed through cylindrical holes in a pellet matrix (Figure 1),
15
where the temperature reaches 100 °C due to friction. At this point a binding
agent such as potato starch or “Wafolin S” can be added. “Wafolin S” is a byproduct of pulp production, and contains mostly residues of lignin with small
amounts of sulphur. However, most plants press the wood pellets in the matrix
with steam, a process that increases the natural binding of the lignin in the raw
material and makes the use of any binding agent unnecessary. After pressing, the
wood pellets are passed through a cooling tower, stored, then bagged for sale to
private households or transported via trucks to industrial plants or district heating plants.
Sawdust
Drying
Grinding
Pressing
Cooling
Storage
Shavings
Figure 2. Flow chart of wood pellet production.
2.2 Exposures during wood pellet production
The main components of wood are cellulose, hemicelluloses and lignin. In addition, so-called extractives comprise 0.1-1 % of the mass in spruce and pine wood.
Some of these components protect against injury or attack from insects, bacteria
and fungi, but they may also have toxic, irritant or sensitising effects on humans.
The main extractives are monoterpenes, terpenoids, aliphatic components, fats
and waxes, and phenolic compounds. Monoterpenes are hydrocarbons and include compounds such as D-pinene, E-pinene and '3-carene, whereas terpenoids
are derivatives of terpenes and include acids, such as resin acids, and alcohols
(IARC, 1995).
Workers involved in wood pellet production can also be exposed to volatile organic compounds (VOCs) emitted from sawdust (Svedberg et al., 2004; Arshadi
and Gref, 2005), carbon monoxide released as bacteria break down the wood
(Svedberg et al., 2004) and diesel exhaust emitted from trucks during transportation of raw material and wood pellets within the premises. VOCs include aldehydes that can be produced during the oxidation of unsaturated fatty acids in the
wood (Arshadi and Gref, 2005), and generally they have an irritating effect on
the upper airways. Since carbon monoxide has a higher affinity for red blood cells
16
than oxygen, exposure to it can lead to breathing difficulties, headaches and, at
high exposure levels, death. Nitrogen dioxide can cause coughing, dizziness and
nausea, while exposure to diesel exhaust is associated with various symptoms
including irritation and inflammation of the airways (Montelius, 2003). Although
mould often grows on sawdust when it is stored in damp conditions, this does not
seem to be a problem during the production of wood pellets since turnover is very
fast and the process requires fairly dry raw material.
Data on exposure during the production of wood pellets are sparse, but numerous studies have examined exposure in other wood industries. Examples of the
extent of exposure to wood dust and other substances in different industries that
handle softwood are presented in tables 1 and 2, respectively.
2.2.1 Wood dust
Many people are currently exposed to wood dust and in 2002-2003, around 3.6
million workers (ca. 2 % of the workforce) were exposed in the EU (Kauppinen
et al., 2006). The size and shape of wood particles varies depending on the type
of wood, water content and the processing method involved (Eriksson and Liljelind, 2000). Wood dust is mainly composed of particles with a median aerodynamic diameter >10 Pm (Davies et al., 1999).
Exposure to wood dust may cause symptoms in skin, eyes, nose, and airways.
Dermal symptoms include both irritant and allergic contact eczema caused by
direct contact (Färm, 1997; Hausen, 1986; Estlander et al., 2001), and consequently woodworkers have a higher risk of developing hand eczema (Meding et
al., 1996). Occular symptoms include mainly irritation (Halpin et al., 1994;
Eriksson et al., 1997; Eriksson et al., 1996). Nasal symptoms include hypersecretion (Eriksson and Liljelind, 2000), rhinitis, itching (Åhman and Söderman,
1996), inflammatory reactions (Dahlqvist et al., 1996), irritation (Åhman and
Söderman, 1996; Halpin et al., 1994) and general problems (Åhman et al., 1995;
Shamssain, 1992). Airway symptoms include increased bronchial sensitivity
(Halpin et al., 1994; Malmberg et al., 1996), asthma (Malo et al., 1986; Schlünssen et al., 2002; Douwes et al., 2001), irritation (Hessel et al., 1995; Lindberg,
1979), coughing (Shamssain, 1992; Schlünssen et al., 2002), and altered respiratory functions (Hessel et al., 1995; Shamssain, 1992; Eriksson et al., 1997).
17
18
Inhalable dust
Inhalable dust
Inhalable dust
Respirable dust
Total dust
Total dust
Total dust
Total dust
Total dust
Respirable dust
Total dust
Plywood mill
Sawmills/lumber mills
Woodwork teachers
8
8
7-8
8
7-8
n.r
n.r
8
6-8
7-8
8
n.r
8
n.r
A
range
Measurement time (h)
8
8
8
8
4
4
8
8
8
Inhalable dust
8
Inhalable dust
8
Inhalable dust
n.r
Inhalable dust
8
Inhalable dust
8
Inhalable dust
6-8
n.r. - not recorded
GM - geometric mean
N - number of measurements
AM - arithmetic mean
Respirable dust
Total dust
Total dust
Joinery shops
Woodworker
Dust fraction
Inhalable dust
Inhalable dust
Inhalable dust
Respirable dust
Total dust
Total dust
Total dust
Total dust
Total dust
Industry
Furniture industry
n.r.
37
140
199
141
170
39
39
220
178
230
1237
16
28
48
237
60
50
38
50
N
1025
2217
1685
18
28
89
752
1685
18
2.1
2.9
2.1
2.7
-
1.0
0.4-2.2
0.72
0.3
0.12
0.7
0.4
-
GM (mg/m3)
1.2
0.96
0.94
0.9
0.60
-
3.3
0.3-55 A
1.2
2.95
4.5
5.7
0.10
0.57
1.8
0.6-3.6
<0.08-0.20 A
3.0
0.25
0.26
0.3
0.51
-
0.29
0.6
1.8
AM (mg/m3)
0.28
2
1.65
2.2
(Pisaniello et al., 1992)
(Hamill et al., 1991)
(Schlünssen et al., 2002)
(Scheeper et al., 1995)
(Brosseau et al., 2001)
(Alwis et al., 1999)
(Åhman et al., 1996)
(Åhman et al., 1996)
(Demers et al., 2000)
(Rosenberg et al., 2002)
(Teschke et al., 1994)
(Hall et al., 2002)
(Johard et al., 1992)
(Dahlqvist et al., 1992)
(Eriksson et al., 1996)
(Teschke et al., 1994)
(Fransman et al., 2003)
(Holness et al., 1985)
(Eriksson et al., 1997)
(Holness et al., 1985)
Reference
(Mikkelsen et al., 2002)
(Schlünssen et al., 2004)
(Schlünssen et al., 2001)
(Sass-Kortsak et al., 1986)
(Wilhelmsson et al., 1984)
(Holmström et al., 1989)
(Vinzents and Laursen, 1993)
(Schlünssen et al., 2001)
(Sass-Kortsak et al., 1986)
Table 1. Examples of exposure levels from personal monitoring of wood dust in different wood industries in which soft wood is handled.
19
Personal (7-8 h)
Personal (n.r)
Personal (7-8 h)
Personal (7-8 h)
Personal (n.r)
Personal (8 h)
Personal (8 h)
Personal (6-8 h)
Personal (7-8 h)
Personal (8 h)
Personal (n.r.)
Stationary (8 h)
Stationary (n.r)
Stationary (18 h)
Stationary (8 h)
Stationary (18 h)
Stationary (18 h)
Formaldehyde
Formaldehyde
Formaldehyde
Monoterpenes A
Formaldehyde
Abietic acid
Monoterpenes B
Formaldehyde
Abietic acid
Pimaric acid
Monoterpenes A
Monoterpenes A
Monoterpenes A, C
Monoterpenes A
Monoterpenes B
Monoterpenes D
Monoterpenes E
Monoterpenes D
Monoterpenes A
Monoterpenes F
Formaldehyde
Aldehydes F
Carbon monoxide F
Furniture industry
Joinery shops
Plywood mill
Sawmills/lumber mills
Wood pellet production
1
1
220
220
48
57
159
48
220
174
85
82
6
2
25
20
20
22
38
50
68
18
89
N
111
56
16-193
-
-
0.021
0.002
254
35-444
34-143
73
0.3-0.5
5.7-148
20-130
16-201
0.4-23G
160-170
0.006-0.011
-
60
0.08
AM
(mg/m3)
0.12
0.25
(Svedberg et al., 2004)
(Svedberg et al., 2004)
(Demers et al., 2000)
(Demers et al., 2000)
(Hedenstierna et al., 1983)
(Liljelind et al., 2001)
(Liljelind et al., 2001)
(Eriksson et al., 1996)
(Demers et al., 2000)
(Rosenberg et al., 2002)
(Lindberg, 1979)
(Rosenberg et al., 2002)
(Dahlqvist et al., 1992)
(Svedberg and Galle, 2000)
(Rosenberg et al., 2002)
(Fransman et al., 2003)
(Fransman et al., 2003)
(Fransman et al., 2003)
(Eriksson et al., 1997)
(Holness et al., 1985)
(Vinzents and Laursen, 1993)
(Sass-Kortsak et al., 1986)
(Holmström et al., 1989)
Reference
0.68
(Åhman et al., 1996)
sum of D-pinene and '3-carene
F
FTIR - Fourier transform infrared spectroscopy
G
range
E
0.0072
0.0006
60
0.1-0.3
2.0-138
0.0007
0.1-1.5
0.08
43
-
GM
(mg/m3)
0.15
-
Woodwork teachers
Monoterpenes A
Stationary (8 h)
39
A
n.r. - not recorded
sum of D-, E-pinene and '3-carene
B
N - number of measurements
D-, E-pinene and '3-carene presented individually
C
GM - geometric mean
self assessments
D
AM - arithmetic mean
sum of D-, E-pinene, '3-carene and limonene
Personal (8 h)
Personal (8 h)
Personal (15 min)
Personal or stationary
(measurement time)
Personal (15 min)
Personal (3-8.5 h)
Personal (1-2 h)
Agent
Industry
Table 2. Examples of levels of exposure to formaldehyde, monoterpenes, resin acids, aldehydes and carbon monoxide in different
wood industries in which soft wood is handled.
The IARC has classified wood dust as carcinogenic, particularly for cancers of the
nasal cavities and paranasal sinuses, but mainly as a result of exposure to hardwoods (IARC, 1995; SCOEL, 2003; Demers et al., 1997). Wood dust from pine
and spruce has reportedly caused irritation in the eyes and upper airways at air
levels between 0.1 and 6.3 mg/m3. There are also indications that wood dust levels around 1 mg/m3 could reduce lung function (Eriksson and Liljelind, 2000).
2.2.2 Monoterpenes
The most abundant monoterpenes in softwood are D-pinene, E-pinene and '3carene (Fengel and Wegener, 1983), and they are usually monitored in industries
handling softwood (Figure 3). Monoterpenes are irritating to skin, eyes and mucous membranes, and can cause both non-allergic and allergic contact dermatitis
(Eriksson and Levin, 1990; Falk Filipsson, 1995). They can be taken up through
the lungs, the gastro-intestinal tract and intact skin (Cavender, 1994; Falk Filipsson, 1995). Results from animal studies have suggested that high concentrations of '3-carene might lead to asthma (Låstbom et al., 1995), and that skin sensitisation can increase lung reactivity (Låstbom et al., 2000).
(a)
(b)
(c)
Figure 3. Molecular structures of D-pinene (a), E-pinene (b) and '3-carene (c).
2.2.3 Resin acids
Several studies have shown that resin acids are released during the handling and
treatment of logs and boards produced from softwood used in sawmills (Eriksson
et al., 2004; Demers et al., 2000), lumber mill (Teschke et al., 1999), carpentries
(Eriksson et al., 2004) and plywood mills (Fransman et al., 2003). Resin acids
usually found in pine and spruce are abietic acid (AA), dehydroabietic acid
(DHAA) and pimaric acid (PA) (Fengel and Wegener, 1983). These chemicals are
also the main components of colophony, a technical product that has been associated with occupational asthma, contact dermatitis (Färm, 1996; Färm, 1997;
Sadhra et al., 1994; Keira et al., 1997; Downs and Sansom, 1999), and decreased
20
FEV1 (Forced Expiratory Volume in one second) after acute exposure (Burge et
al., 1980). Animal studies have shown that resin acids, especially oxidised acids
such as 7-oxodehydroabietic acid (7OXO), can act as allergens on the skin (Hausen et al., 1990; Hausen et al., 1989; Hausen et al., 1993; Karlberg and
Wahlberg, 1988). Other animal studies have shown that AA can damage alveolar,
tracheal and bronchial epithelial cells (Ayars et al., 1989). The resin acids considered in this thesis are 7OXO, DHAA, AA and PA, illustrated in figure 4.
(a)
(b)
(c)
(d)
Figure 4. Molecular structures of 7OXO (a), DHAA (b), AA (c) and PA (d).
2.3 Air exposure assessments
Exposure assessments can be used to compare a chemical’s levels with the occupational exposure level (OEL), or to investigate exposure-response relationships
in epidemiological studies. Pumped or diffusion sampling, in which samples are
collected on a filter, a chemosorbent or an adsorbent, can be used for monitoring.
Filters are used to sample dust, such as wood, paper or silica dust, while vapours
are sampled on chemosorbents or adsorbents.
2.3.1 Dust monitoring
In the past, dust was measured as total dust (Figure 5), but this does not represent
either the inhalable fraction of particles or the total complement of particles in
the air. In particular, it is not considered a reliable method for measuring particles
with aerodynamic diameters from 10 to 100 Pm, which is the fraction of greatest
concern when investigating health effects in the upper airways (Davies et al.,
1999). For greater relevance to the uptake of particles by humans, dust is divided
into three fractions: inhalable, thoracic and respirable (Table 3).
21
Table 3. Definitions and particle sizes for inhalable, thoracic and respirable dust fractions.
Dust fraction
Inhalable
Thoracic
Respirable
Definition
Particles that can be inhaled by the mouth and nose
Particles that pass the larynx
Particles that penetrate to the parts of the respiratory passage
that lack cilia
Particle size
< 50-100 Pm
< 10 Pm
< 4 Pm
It is current praxis to measure dust as either inhalable (Figure 5) or respirable
dust. For testing compliance with regulations, dust can still be measured as total
dust since not all OELs have changed from total dust to inhalable dust in Sweden.
The Swedish OEL for wood dust was, however, changed in October 2005, from
2 mg/m3 measured as total dust to 2 mg/m3 measured as inhalable dust (AFS,
2005). In practice, this change means that the OEL is now lower, since inhalable
wood dust concentrations are on average 1.6 to 4 times higher than total wood
dust concentrations (Davies et al., 1999; Lidén et al., 2000; Harper and Muhler,
2002; Tatum et al., 2001).
(a)
(b)
Figure 5. Total dust sampler with a diameter of 25 mm (a) and IOM sampler (b) for
sampling inhalable dust fractions.
2.3.2 Diffusive sampling
In diffusive sampling, substances of interest are normally collected on a chemosorbent that chemically binds them, or an adsorbent that binds them on its surface. Chemosorbents and adsorbents can be stored in tubes or badge-type samplers (see figure 6 for examples). Diffusive sampling is based on Fick’s law of diffusion, which is as follows:
22
J= -D (dc/dx)
where J is the uptake rate, D is the diffusion coefficient and dc/dx is the concentration gradient. The uptake rates for the substances and samplers of interest are
specified in advance. One advantage of diffusive sampling is that relatively small
samplers are used, which can be attached to the workers’ clothes easily, with no
need for pumps. However, the accuracy of uptake rates can be uncertain, a disadvantage that results from changes in the concentration gradient and the temperature dependency of the diffusion process.
(a)
(b)
Figure 6. A tube for sampling VOCs (a) and a badge-type sampler for nitrogen dioxide
(b).
2.3.3 Peak exposures
Standard methods for measuring both inhalable and total dust describe average
levels of dust in the air during a shift, but not peaks in exposure during the workday. High exposures of a short duration are of concern because they can lead to
high dose rates in the body or target tissues, which can: (i) alter metabolism, (ii)
overload the body’s repair and protective mechanisms, and (iii) cause amplified
responses by the tissues. Consequently, the same dose given with less intense exposures over a longer period can have different effects from a dose given in a
peak exposure (Smith, 2001). Peak exposures are also associated with acute respiratory effects, which warrant medical attention since they can cause discomfort or
precede chronic diseases (Eisen et al., 1991; Wegman et al., 1992). Reaching a
consensus on what constitutes a toxicologically relevant peak exposure is one of
the difficulties in this area (Preller et al., 2004). Personal real-time monitors can
be used to monitor variations in exposure during the day (Eisen et al., 1991;
23
Woskie et al., 1994; Wegman et al., 1994). These are mainly light-scattering
monitors, in which the intensity of the light scattered is proportional to the concentration of dust (Thorpe, 2007).
2.3.4 Variability in air exposure
Interest in air exposure variability has increased dramatically since the beginning
of the 1990s, when the importance was recognised of considering not only the
overall variability in exposure, but also the variability within- and betweenworkers (Rappaport, 1991). One-way random-effects ANOVA models (Kromhout and Heederik, 1995; Kromhout et al., 1993; Kumagai et al., 1996; Symanski
et al., 2000; Nieuwenhuijsen, 1997) and two-way ANOVA models (Kromhout
and Heederik, 1995; Vinzents et al., 2001; Kromhout et al., 1996; Nieuwenhuijsen, 1997) are often used to evaluate the variability of the exposure. Withinworker variance is often associated with factors regarding organisation of the
work and the layout of the facility while the between-worker variance often is
linked to the individual workers’ work practises. In addition to measures of
within- and between-worker variability, a 95 % fold range of measurements can
be used, which corresponds to the ratio of the 97.5th to the 2.5th percentile of exposures in a lognormal distribution. A between-worker fold range of two suggests
that 95 % of the mean exposure for a group lies within a two-fold range, and a
group with a between-worker fold range d2 has been defined as an uniformly
exposed group (Rappaport, 1991; Rappaport et al., 1993). Variability measurements can also be used to identify factors that increase or decrease the exposure;
so-called determinants of exposure. Factors that can affect exposure include work
environment characteristics (Peretz et al., 2002; Vermeulen et al., 2004), work
practices (Blanco et al., 2005; McClean et al., 2004), specific work procedures
(Houba et al., 1997), and types of material used (McClean et al., 2004; NylanderFrench et al., 1999).
The variation estimates can also be used to calculate overexposure, by testing
whether the long-term mean of exposure for a randomly selected worker is acceptable compared with the agent’s OEL (Rappaport et al., 1995; Lyles and Kupper, 1996; Tornero-Velez et al., 1997). Variability analyses have also been used in
epidemiological studies to calculate bias in the exposure-response relationship
(Vinzents et al., 2001; Kromhout et al., 1996; Liljelind et al., 2003). In occupational epidemiology, linear regression models are applied to describe dose-
24
response relationships. One of the underlying assumptions in linear regression
analysis is that the independent variable, in this case the exposure, should not
vary for a specific subject (Gujarati, 1995). However, this condition cannot be
fulfilled when the exposure varies, which leads to underestimation of the regression coefficient, i.e. attenuation of the relationship (Nieuwenhuijsen et al., 1995;
Vinzents et al., 2001; Kromhout and Heederik, 1995; Kromhout et al., 1996;
Nieuwenhuijsen, 1997). The bias is expressed as a ratio, between the estimated
exposure effect and the true effect. Therefore, it can take any value between zero
and one, where values close to zero indicate low bias and values close to one indicate high bias.
2.4 Dermal exposure assessments
2.4.1 General
Dermal exposure to chemicals can lead to systemic effects if the substances pass
through the skin, but can also cause local effects ranging from irritation to burns,
as well as allergic reactions. Assessments of dermal exposure are often complex,
as Schneider et al. (1999, 2000) illustrate in their conceptual model, in which exposure is described as the result of material moving between compartments by
different transport processes. These may include deposition or absorption of a
substance directly from the air, by parts of the body being submerged in the substance, or by the body coming into contact with contaminated surfaces (Schneider
et al., 1999; Schneider et al., 2000).
Only a few dermal exposure assessment methods have been validated, which
makes it difficult to compare results from different studies (Benford et al., 1999).
Most methods measure the quantity of material on the skin, even though the concentration might be more relevant with respect to dermal uptake of a substance
(Cherrie and Robertson, 1995). Dermal exposure assessment techniques can be
divided into three categories (Brouwer et al., 1998; Cherrie et al., 2000): (i) removal techniques, (ii) surrogate skin techniques, and (iii) visualisation techniques.
In removal techniques, the chemical is taken off the skin and then analysed. These
include the tape-stripping method (see below), hand washing (Brouwer et al.,
2000; Lind et al., 2004) or suction (Lundgren et al., 2006). Examples of surrogate
skin techniques include use of patches (Eriksson et al., 2004; Soutar et al., 2000)
and whole-body sampling (Soutar et al., 2000), which act as a collection medium
for the substance of interest. Fluorescent tracers are often used in visualisation
25
techniques, for both qualitative and quantitative assessments (Cherrie et al.,
2000). Consequently, most methods measure potential exposure, rather than the
actual dermal exposure.
2.4.2 The tape-stripping method
The tape-stripping technique has been used to measure exposure to substances
such as acrylates (Nylander-French, 2000; Surakka et al., 1999), isocyanates
(Fent et al., 2006) and jet-fuel (Chao et al., 2005; Chao et al., 2006; Mattorano et
al., 2004) in the work environment, in pharmacological studies (Liljelind et al.,
2007) and in penetration studies in animals (Tojo and Lee, 1989; Wilhelm et al.,
1991). In this technique a tape-strip is placed on an area of the skin for a specified period of time, and when it is removed, chemicals adsorbed to the skin are
removed, together with a few μm of the outermost layer of the skin, the stratum
corneum (SC) (Chao and Nylander-French, 2004; Marttin et al., 1996). Repeated
stripping of a skin section can thus provide information about possible percutaneous penetration of a substance, and is believed to provide better indications of
actual dermal exposure. The substance of interest is then analysed after desorption from the tape, often in relation to the amount of SC also sampled. Quantitative methods for analysing SC include weighing (Bommannan et al., 1990), spectrophotometric examination (Marttin et al., 1996), and colorimetric methods that
determine keratin in the tape-sample (Chao and Nylander-French, 2004; Dreher
et al., 1998).
2.5 The projects
2.5.1 Study I
In 2001, a project was initiated in order to study air exposure to wood dust and
monoterpenes, as well as to conduct real-time monitoring of wood dust during
the production of wood pellets (Paper I). The health effects studied were lung
function using spirometry, symptoms of the upper and lower airways using a
questionnaire, nasal obstruction using a nasal peak expiratory flow meter and
allergy occurrence using the Phadiatop“-test.
26
2.5.2 Study II
A larger study began in 2004, and the air exposures monitored were wood dust,
monoterpenes, resin acids, nitrogen dioxide as a marker for diesel exhaust,
VOCs, carbon monoxide (Paper II) and aldehydes. Real-time monitoring of wood
dust was also performed. Repeated measurements were taken to examine the
variance in the exposure data (Paper III). Dermal exposure to resin acids was also
monitored using both the tape-stripping method (Paper IV) and the patch
method. Urine samples were collected from the participants, in order to monitor
biomarkers of exposure. A number of health effects were examined: allergy occurrence with the Phadiatop“-test; lung function; and symptoms of the upper and
lower airways, as in study I. In addition, skin symptoms based on a questionnaire, effects on the airways, skin, nose and eyes on the day of measurement, nitrogen dioxide as a marker of inflammation in the airways, nasal lavage as a
marker of inflammation in the nose, and skin condition were also included. The
remaining results will be presented in forthcoming papers.
2.6 Objectives
The objectives of the studies underlying this thesis were:
x
To determine the workers’ air exposure to wood dust, monoterpenes, resin
acids, and nitrogen dioxide as a marker of diesel exhaust.
x
To describe the background exposure to wood dust, monoterpenes, resin acids, diesel exhaust (nitrogen dioxide), VOCs and carbon monoxide.
x
To assess the within- and between-worker variation in air exposures, and
evaluate determinants of exposure for wood dust and resin acids.
x
To estimate the extent of overexposure and attenuation for air exposures to
wood dust and resin acids.
x
To quantify and evaluate dermal exposure to resin acids.
27
3 Study design and analysis
3.1 Wood pellet production plants
Six plants located in central Sweden participated in study I (Paper I). The plants,
chosen based on their proximity to our research group, all produced wood pellets, and two also manufactured briquettes. The plants’ production of wood pellets ranged from 12 000 to 40 000 tonnes/year. All plants had general ventilation,
but some also had local ventilation at specific sites, for example bagging and briquette production stations.
Four production plants located in Sweden were included in study II (Papers II-IV).
The criteria for inclusion were non-participation in study I, and having more than
10 people involved in the production of wood pellets. All the plants produced
wood pellets (12 000-120 000 tonnes/year), and one also manufactured briquettes. All plants had general ventilation, but some also had local ventilation at
specific sites, for example bagging stations.
3.2 Subjects
In the plants included in study I (Paper I), a total of 39 workers were actively involved in the production of wood pellets, 24 of whom (all men) were present on
the day of the study, all of whom were invited to participate, and all accepted.
Repeated measurements were performed for two of the workers. The participants
worked as shift, daytime or bagging operators. They had hearing protectors and
respirators as protective equipment, but their respirators were seldom used while
measurements were taken. The methodology used in this study was approved by
the Ethical Committee of Örebro County Council (D-no. 500:161012/00).
In the plants examined in study II (Papers II-IV), 65 workers were employed in
wood pellet production, 47 of whom were invited to participate, and 44 accepted
(43 men and one woman). Between one and three measurements were taken for
each participant. The operators who were invited to participate were those who
were due to work on measurement dates, and they represented several working
categories: shift, daytime and bagging operators. The personal protective equipment available for the workers included hearing protectors and respirators, but
none of the participants reported any use of respirators while the measurements
29
were carried out. The methodology was approved by the Ethical Committee of
Umeå University (D-no. 03-335).
Examples of tasks performed by the workers included: maintenance and monitoring from the control room for shift operators, welding and repairs for daytime
operators, and bagging and truck driving for bagging operators.
3.3 Air exposure measurements (Papers I-III)
3.3.1 Study I
Twenty-six personal exposure measurements of wood dust (as total dust) and
monoterpenes were taken during shifts covering an afternoon and the morning of
the following day. For 18 of the participants, real-time monitoring of dust was
also performed using a DataRAM monitor. At three of the six plants, measurements were taken during the daytime shift as well as the morning shifts on the
same day. The sampling time was approximately eight hours. Filter cassette and
monoterpene samplers were placed within the breathing zone by attaching them
to the workers’ overall or shirt collars. The DataRAM was placed on each of the
18 selected workers’ belts. During the measurement period, the participants were
asked to keep a log of their work and register the time as well as the duration of
different tasks. Area samplings were performed at three to five different positions
over 8 hours, while the personal exposure measurements were done in each plant.
The samplers were positioned at strategic places where high wood dust and/or
monoterpene exposure was expected, like the sawdust and shaving storage sites,
or where the workers spent a lot of time, e.g. the control room.
3.3.2 Study II
A total of 68 personal measurements of inhalable and total dust, resin acids,
monoterpenes and nitrogen dioxide were carried out. In 63 of these cases, dust
was also monitored with a DataRAM. The sampling time was between 4.5 and
10 hours, with an arithmetic mean (AM) of 7 hours. In all cases, monitors were
attached to a vest worn by the worker. The wood dust samplers were placed
within the breathing zone: an inhalable dust sampler on the right shoulder and a
total dust sampler on the left shoulder. The DataRAM was placed on the left side
of the worker’s chest, and a monoterpene and nitrogen dioxide sampler were
placed on the right side of the chest. During measurement, the participants were
30
asked to keep a log of their work. Seventy-one area measurements were taken for
total dust, resin acids, monoterpenes, VOCs and carbon monoxide, along with 42
measurements of nitrogen dioxide, in each case during an 8-hour shift, except for
carbon monoxide, which was sampled over approximately 35 hours. Area sampling were performed at three to six sites per plant and corresponded to those
used in study I.
3.3.3 Wood dust
Inhalable and total dust were pump-sampled (2 L/minute) using an IOM-sampler
(225-70A, SKC Inc, Eighty Four, USA) and a 25 mm open-faced antistatic cassette (A-002550-SAC, Omega Specialty Instrument Co, Chelmsford, USA), respectively. Both had filters with 5 μm pores. A cellulose acetate filter (Millipore,
Ireland) for total dust was used in study I, and a PVC-membrane filter (GLA5000, Pall Corporation, Michigan, USA) for both inhalable and total dust was
used in study II. The IOM-cassettes and total dust filters used in study I were
conditioned for 48 hours (temperature, 20 r 1 ºC; relative humidity, 50 r 3 %)
before and after the sampling. The PVC filters used for the total dust sampling in
study II were conditioned before sampling to minimize their static electric charge.
However, since they are non-hygroscopic, and to prevent oxidation of the resin
acids in the collected dust, they were not conditioned after sampling. The dust
collected on the filters was gravimetrically determined (detection limit: 0.001 mg).
3.3.4 Real-time monitoring of dust
Continuous monitoring of dust concentrations was carried out using a personal
data-logging real-time aerosol monitor (DataRAM; MIE, Inc, Bedford USA). The
DataRAM is a photometric monitor that measures particles with diameters between 0.1 and 10 Pm at concentrations ranging from 0.001 to 400 mg/m3. The
DataRAM relies on the diffusion of ambient air into a sensing chamber, and the
sensitivity is, according to the manufacturer, optimal for the respirable fraction of
dust. The DataRAM was calibrated, by the manufacturer, against SAE Fine (ISO
Fine; Powder Technology, Inc.) test dust. Dust concentrations were recorded
every 20th second, stored in the instrument’s data-logger and then transferred to a
PC. A peak was defined as successive recordings exceeding a threshold value of
0.4 mg/m3.
31
3.3.5 Monoterpenes
Monoterpenes in air were sampled by diffusive sampling using Perkin-Elmer
tubes with Chromosorb 106 (Markes International) as the adsorbent (Sunesson et
al., 1999), and desorbed using an automatic thermal desorber (Perkin Elmer 400)
connected to a GC (Hewlett Packard 6890, Germany) with a MS detector (Hewlett Packard 5972, Germany). The samples were desorbed at 200 °C for 5 minutes at a desorption flow of 70 ml/min of helium (He) without inlet split, and
were collected in a TENAX™ TA-filled trap at -30 °C. The analytes were desorbed
from the trap at 200 °C for 10 minutes with an outlet split flow of 50 ml/min. In
the GC a 50 m * 0.32 mm crosslinked methylsiloxane (HP-1) capillary column,
with 1.05 μm film thickness, was used. The temperature of the GC oven was programmed as follows: 50 °C for 1 minute followed by 5°/min to 120 °C and
35°/min to 290 °C, which was held for 10 minutes. To identify the monoterpenes,
the MS was run in full scan mode (29-550 amu) after a solvent delay of 8 minutes. The monoterpenes were identified by comparing the spectra obtained
against those in a NIST/EPA/NIH mass spectral database (HP G1033A revision
C.00.00 1992), and the total ion chromatograms obtained were used for quantification, by comparison to calibration graphs covering concentrations ranging from
50 ng to 2 μg per sample. The calibration points were prepared by injecting 3 μl
portions of standard solutions of monoterpenes in methanol onto a TENAX™ TA
tube under a flow of 100 ml/min of He for one minute, and analysing them in the
same way as the field samples. The limit of quantification (LOQ) was estimated
at 7 ng, calculated as three times the standard deviation of the signal at the lowest
calibrated concentration. The coefficient of variation was 5 % at the 50 ng/sample level (n=12). The highest concentration on the calibration curve was
2 Pg/sample for each of the examined monoterpenes. However, previous experience suggests that the curve is linear at higher concentrations, therefore extrapolation was used to determine higher concentrations (personal communication: K.
Eriksson, University Hospital of Umeå, Sweden).
3.3.6 Resin acids, nitrogen dioxide, VOCs and carbon monoxide
The analysis of total dust filters used to sample resin acids was performed as described in section 3.5. Recovery from the filters was evaluated using a total of 18
samples for each acid, in which 1 μg of 7OXO, DHAA, AA or PA in 10 μl of
methanol was applied three times onto a set of six filters. Nitrogen dioxide was
diffusively sampled using an Advantec-filter, then analysed with a colorimetric
32
method (Yanagisawa and Nishimura, 1982). VOCs were collected by diffusive
sampling on TENAX™ TA-adsorbent (Supelco and Perkin Elmer) and then analysed by GC-MS after thermal desorption (SIS, 2003) and the uptake rate of
decane, 0.39 ml/min, was used to calculate their concentrations (HSE, 2001). A
colorimetric tube (Dräger Safety, Lüebeck, Germany) was used to monitor carbon
monoxide, again by diffusive sampling.
3.4 Dermal exposure measurements (Paper IV)
3.4.1 The tape-stripping method
Tape-stripping of resin acids was performed using Leukosilk® tape (BSN-medical
GmbH & Co., Germany), which was chosen since it does not contain any background levels of resin acids. For the recovery studies and field study, pre-cut
4.0 cm x 2.5 cm pieces of tape were applied to exposed areas of skin or a glass
surface, then removed after 2-3 minutes using forceps washed in methanol. The
exposed area was tape-stripped three times with fresh tape, with new gloves being
used each time a tape was applied or removed from the site, in order to avoid
cross-contamination. The position of the first tape was marked in ink on the surface of the skin, close to each corner of the tape, in order to facilitate replication
of the sampling. During the assessment of recovery from the glass plate, a white
paper with a rectangle (4.0 cm x 2.5 cm) drawn on it was applied to the rear of
the glass plate.
3.4.2 In vivo study, recovery and stability tests
In order to test the recovery from human skin in vivo, ten volunteers were exposed at room temperature to solution 1: 13 800 ng 7OXO , 17 550 ng DHAA
and 16 200 ng AA; and solution 2: 1 500 ng 7OXO , 1 800 ng DHAA and 1 500
ng AA. Both solutions were dispersed in methanol (10 μl), and applied to the
frontal side of both forearms. A recovery study from human skin was not performed for PA, because this was not included in the application for ethical approval to perform the in vivo test. Sample recovery from tapes was analysed by
applying solutions of 7OXO, DHAA, AA or PA, (1 000 ng in 10 μl of methanol)
to separate sets of 18 tape strips, giving a total of 72 spiked tape strips.
Sample stability was determined by spiking separate sets of three tapes with solutions of 7OXO, DHAA, AA and PA (1 000 ng in 10 μl of methanol). These tapes
33
were kept in airtight containers for 28 days at 20 ºC, or 48 days at -20 ºC. To
estimate the extraction capacity of the tape for the resin acids, sample recovery
from glass plates was evaluated using solutions of 7OXO (15 000 ng or 1 000
ng), DHAA or AA (16 000 ng or 1 600 ng) in 10 Pl of methanol. Each was applied to a glass plate, and six parallel analyses were performed for the individual
resin acids at each concentration.
3.4.3 Field study
Dermal exposure was measured at four different parts of the body: the forehead,
the front of the neck, the frontal side of the forearm and the dorsal side of the
hand. The left-hand skin areas were measured before a shift, and the right-hand
areas were sampled post-shift. A field blank, non-sampled tape, was collected
before and after shifts. Duplicate samples were taken from 21 individuals within
3-16 weeks of the first sample, resulting in a total of 1 472 tape strips (735 preshift samples and 737 post-shift samples). A few of the workers put on their
working clothes and entered the production area for a process update 5-30 minutes before pre-shift sampling. Tape-strip sampling was carried out within the
factory premises, but in a room away from the wood pellet production area.
3.5 Analysis of resin acids (Papers II-IV)
The total dust filter and tape samples were stored in airtight containers at -20 ºC
until analysis, when they were each extracted with 3 ml methanol. The dermal
samples were then shaken for 25 minutes, filtered using a Titan 17 mm, 0.22 μm
nylon syringe filter (SUN SRI, US) and 3 μl portions were analysed for 7OXO,
DHAA, AA and PA by HPLC-ESI-MS (Agilent technologies, USA), as follows.
The analytes were separated using a PRISM RP™ column (100*2.1 mm, 3 μm
film, Thermo Electron Corporation, Waltham, USA) fitted with a corresponding
guard column, and an isocratic mobile phase of 60:40:0.05 (v/v) acetonitrile:water:formic acid at a flow rate of 0.3 ml/min. The eluted analytes were introduced into the MS using an electrospray interface in positive mode, and the
analytes were detected and quantified in single ion monitoring (SIM) mode, collecting fragments with m/z ratios of 315.40, 301.40, 303.40 and 303.40, corresponding to the (M+H)+ ions of 7OXO, DHAA, AA and PA, respectively. Data
generated by the HPLC-MS system were collected and evaluated using Chemstation LC/MSD software (Agilent technologies, Waldbronn, Germany).
34
3.6 Statistics
3.6.1 General
The concentrations of each of the substances detected were described with three
statistics: the GM, AM, and range. Each air measurement was considered to be
representative of the corresponding 8-h time-weighted average (8-hour TWA)
exposure, even if the measurement time was shorter than 8 hours. All analyses of
the exposure measurements were performed after logarithmic transformation of
the data. Measurements under the limit of quantification (LOQ) were recorded as
LOQ/—2 if the geometric standard deviation (GSD) was larger than three, and
otherwise as LOQ/2 (Hornung and Reed, 1990). If half or more of the values for
a substance were under the LOQ, GM and AM values were not calculated and
the substance was excluded from aggregate concentrations (Papers I-III). The
within-day and between-day variation of recovery from the filters was estimated
by applying a random coefficient model using restricted maximum likelihood estimation. Pearson’s correlation coefficients (r) were used as measures of the correlation between total dust and resin acids (Paper II).
The paired Student’s t-test was used to determine whether there were any statistically significant differences in the recovery of 7OXO, DHAA, or AA from human
skin. Dichotomisation (tLOQ = 1 and <LOQ = 0) was performed for each person, sample site, sample time (pre- or post-shift) and date of measurement for
both 7OXO and DHAA. An individual was assigned an exposure (value = 1) if
any of the three tapes had a detectable level of 7OXO or DHAA. The McNemar
test was used to compare skin exposure between pre-shift and post-shift samples
for each sample site. Since repeated measurements were taken for some individuals, this statistical test was performed separately for each worker’s first and second sampling event (Paper IV).
35
3.6.2 Estimation of variance components (Paper III)
Components of variance within- and between-workers were estimated by a oneway random effect analysis of variance model, with restricted maximum likelihood estimation, with worker as the random factor (Rappaport, 1991; Rappaport et al., 1993). Thus, after denoting the exposure of the ith worker (i = 1, …,
N) at the jth measurement occasion (j = 1, …, n) Xij, our model for the exposure
assessment is:
ln (Xij)
Yij
P y E i H ij
(1)
where Py is the true unknown mean of the logged level of exposure, Ei is the random effect of the ith worker and Hij is the random error of the jth measurement of
the ith worker. Since the model assumes that both Ei and Hij are normally distributed and that Hij is homoscedastic the Xij values were log transformed. The vari2
, and the random error variance
ance of Ei is the between-worker variance, V BW
2
2
2
. The total variability is: V BW
+ V WW
.
(Hij) is the within-worker variance, V WW
To estimate the variance between production plants, a between-group variability,
the model was expanded to a two-way random effect ANOVA with workers
nested within each plant. Thus, the model for exposure of the hth group (h = 1,
…, g), the ith worker (i = 1, …, N) and the jth measurement occasion (j=1,…..n)
Xhij is:
ln (Xhij)
Yhij
P y D h E hi H hij
(2)
where Dh is the random effect of the hth group (production plant) and is assumed
2
, the between-group variability. The
to be normally distributed with variance, V BG
2
2
2
+ V BW
+ V WW
.
total variability in model (2) is: V BG
To characterise the between- worker variance components the fold range can be
used. This is calculated from the variance estimates according to the formula
given by Rappaport (1991):
R0.95, BW = exp(3.92 V BW )
36
(3)
3.6.3 Identification of determinants (Paper III)
The effects of determinants on the exposure level were estimated with the mixed
effect model:
ln (Xij)
Yij
P y E i F 1 R1 j F m Rmj H ij
(4)
which is an expansion of the model (1) to include the regression coefficients F1,
…, Fm corresponding to the fixed effects of the determinants, R1j, …,Rmj. The
determinants of interest are the proportions of the day spent cleaning and cleaning with compressed air, and in the control room, respectively. Thus, each determinant has a value between 0 and 100 %.
In the same manner, model (2) was expanded to include fixed effects for the determinants, while accounting for the correlation between workers within the same
plant:
ln (Xhij)
Yhij
P y D h E hi F 1 R1 j F m Rmj H hij
(5)
Since the determinants of interest are continuous variables, which vary from day
to day for the workers, they cannot be used to decrease variance to form uniformly exposed groups, to test risk of overexposure or to estimate attenuation.
Therefore, the determinants were disregarded in our further calculations.
3.6.4 Overexposure (Paper III)
To test if exposure levels were acceptable the procedure for overexposure assessments suggested by Weaver et al. (2001) was used, in which the probability that a
randomly selected worker’s mean exposure will exceed the OEL is tested, deeming the risk of overexposure to be acceptable if this probability is d10 %. In conjunction with this, a rough estimate of the probability of overexposure (T) was
calculated according to Lyles et al. (1997). The levels used for comparison were:
the Swedish OEL for wood dust as inhalable dust, 2 mg/m3 (AFS, 2005); the old
Swedish OEL for wood dust as total dust, 2 mg/m3 (AFS, 2000); and the British
37
OEL for colophony, 50 Pg/m3, for comparison to the levels of resin acids
(COSHH, 2005).
3.6.5 Attenuation (Paper III)
The attenuation is related to the ratio of the within-worker and between-worker
2
2
V Bw
) and number of repeated measurements per worker (n)
variation ( O V ww
applying following equation (Kromhout et al., 1996):
Attenuation = 1-
B̂
1
= 1O
B
1
n
(6)
where B̂ is the estimated regression coefficient and B the true regression coefficient. The group-based approach was also used for estimation of the attenuation
as follows (Tielemans et al., 1998):
Attenuation = 1-
2
2
V BG
)/k
(V BW
B̂
= 1- 2
2
2
B
V BG (V BW ) / k (V WW
) / kn
(7)
where k is the number of subjects per group. In the calculations k was set to 9,
since the numbers of workers monitored at the plants varied between 9 and 12.
Equations (6) and (7) were also used to estimate the number of measurements, n,
needed for an attenuation of 10 %, and in equation (7) k values of 2, 5 and 9
were used. All variance components and determinant analysis were estimated using PROC MIXED in SAS Release 9.1.
38
4 Results
4.1 Air exposure (Papers I-III)
4.1.1 Wood dust
A total of 93 personal measurements of total dust were taken, with levels ranging
from <0.10 to 19 mg/m3 (Table 4). The GM for the three operations ranged between 0.77-0.84 mg/m3. The proportions of measurements that exceeded
2 mg/m3, the previous OEL for wood dust as total dust, were 25 %, 18 % and
20 % for shift, daytime and bagging operators, respectively. The mean area
measurement for total dust was 1.8 mg/m3 (<0.10 and 34 mg/m3), and detected
levels were highest at sites where raw materials were stored (Table 5). The personal monitoring of inhalable dust revealed levels of <0.60 to 12 mg/m3. The
OEL was exceeded in 28 %, 55 % and 25 % of the measurements for shift, daytime and bagging operators, respectively. The personal exposure ratios of inhalable dust to total dust ranged from 0.67 to 17, with an average of 3.2 (Figure 7).
Figure 7. Ratios of inhalable dust to total dust, plotted against levels of inhalable dust,
based on personal exposure measurements of wood dust at the four wood pellet production plants in study II (n=68). Both axes are in logarithmic scale.
39
Table 4. Air concentrations of total dust, D-pinene, E-pinene and '3-carene from studies I
and II; air concentrations of inhalable dust, resin acids (sum of 7OXO an DHAA) and
nitrogen dioxide from study II obtained from personal monitoring during wood pellet
production, for three different work operations.
Study
Substance
Studies I & II Total dust
(mg/m3)
N
n <LOQ
n >2 mg/m3
GM
AM
Min
Max
Shift
61
1
15
0.80
1.9
<0.10
19
Daytime
22
0
4
0.77
1.1
0.13
3.5
Bagging
10
0
2
0.84
1.2
0.20
3.4
Total
93
1
21
0.80
1.6
<0.10
19
N
n <LOQ
GM
AM
Min
Max
60
11
1.3
3.0
<0.28
25
22
10
0.62
1.2
<0.23
5.4
10
5
-A
-A
<0.29
0.64
92
26
0.94
2.3
<0.23
25
n <LOQ
GM
AM
Min
Max
42
-A
-A
<0.23
2.3
20
-A
-A
<0.24
0.47
10
-A
-A
<0.29
<0.48
72
-A
-A
<0.23
2.3
n <LOQ
GM
AM
Min
Max
N
Inhalable dust n <LOQ
n >OEL
(mg/m3)
GM
AM
Min
Max
18
0.76
1.5
<0.31
8.0
40
6
11
1.5
2.3
<0.60
12
11
-A
-A
<0.26
1.8
20
2
11
1.9
3.1
<0.60
8.7
9
-A
-A
<0.29
0.35
8
1
2
1.1
1.5
<0.60
3.3
38
0.58
1.1
<0.26
8.0
68
9
24
1.5
2.4
<0.60
12
Resin acids
(Pg/m3)
0.99
2.6
<0.33
25
1.2
2.2
<0.33
10
1.1
2.1
<0.35
8.5
1.1
2.4
<0.33
25
D-pinene
(mg/m3)
E-pinene
(mg/m3)
'3-carene
(mg/m3)
Study II
GM
AM
Min
Max
NO2
(Pg/m3)
N
39
20
8
67
GM
22
27
52
26
AM
25
31
84
34
Min
4.0
9.0
19
4.0
Max
63
65
370
370
N - number of measurements
AM - arithmetic mean
LOQ - limit of quantification
OEL - occupational exposure limit
A
50 % or more of the measurements were below the LOQ
GM - geometric mean
The dust measurements obtained with the DataRAM showed large variations in
peak exposures between workers, within as well as between plants. The number
of peaks (>0.4 mg/m3) recorded for each worker varied between 1 and 73 over an
8-hour working day, with an average of 17 peaks. They also varied amongst the
40
different work operations. Peak values were observed at several work operations,
for example in the management of machines, bagging of wood pellets, loading of
raw material, sweeping and cleaning with compressed air (Figure 8). According to
the work record sheet the participants spent on average 6 % (0-50 %) of their
workday cleaning, 0.7 % (0-26 %) cleaning with compressed air and 15 % (0-87
%) working in the control room in Study II.
Peaks up to 190 mg/m3 3
Toppar upp till 190 mg/m
40
Cleaning with
compressed air
3
Levels of wood dust(mg/m )
35
30
Sacking of
briquettes
25
20
15
Truck driving
Break
Brake
Truck
driving
Sacking of
briquettes
10
5
0
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
Time
Figure 8. Illustrative example of dust measurements logged by a DataRAM in combination with the work record sheet during the course of a shift.
4.1.2 Monoterpenes
The monoterpene detected at the highest levels in the personal monitoring was Dpinene (GM, 0.94 mg/m3) followed by '3-carene (GM, 0.58 mg/m3; Table 4), and
levels were highest during shift operations for both of these compounds (GM, 1.3
mg/m3 and 0.76 mg/m3, respectively). Levels of E-pinene were below the LOQ in
most of the samples, for both personal and area measurements (77 and 58 %,
respectively). The highest average level of monoterpenes was found by the kiln in
the area measurements, and the lowest in the control room (Table 5). Levels of Dpinene varied between <0.21 and 54 mg/m3 (GM, 1.1 mg/m3) and those of '3carene from <0.18 to 22 mg/m3 (GM, 0.71 mg/m3).
41
42
N
10
20
GM
AM
22
Min
12
Max
41
GM - geometric mean
AM - arithmetic mean
NO2
(Pg/m3)
0.49
1.9
0.18
30
GM
AM
Min
Max
33
-B
-B
<0.18
1.9
21
-B
-B
<0.28
3.2
TVOC
(mg/m3) A
Resin acids
(Pg/m3)
n <LOQ
GM
AM
Min
Max
N
GM
AM
Min
Max
'3-carene
(mg/m3)
31
-B
-B
<0.18
0.32
33
21
-B
-B
<0.21
2.6
N
n <LOQ
GM
AM
Min
Max
n <LOQ
GM
AM
Min
Max
34
33
-B
-B
<0.10
0.18
Control room
N
n <LOQ
GM
AM
Min
Max
E-pinene
(mg/m3)
N - number of measurements
LOQ - limit of quantification
Study II
Total dust
(mg/m3)
Studies I & II
D-pinene
(mg/m3)
Substance
Study
1.2
1.7
0.26
4.8
9
-B
-B
<0.28
0.95
12
0.35
0.42
<0.28
1.1
10
-B
-B
<0.21
0.62
14
6
0.62
1.0
<0.25
2.7
15
4
0.23
0.32
<0.10
1.1
Bagging
0.59
0.79
0.18
1.8
12
-B
-B
<0.18
1.9
12
1.2
2.5
<0.28
12
18
-B
-B
<0.20
1.9
21
11
-B
-B
<0.21
19
22
1
0.83
1.7
<0.10
9.9
Storage
1.0
1.2
0.46
1.8
2
0.87
3.6
<0.30
16
4
1.1
1.1
<0.71
1.7
3
-B
-B
<0.27
2.0
5
1
1.3
5.5
<0.28
24
5
0
0.36
0.44
0.10
0.70
Briquette machine
11
10
6
2
35
40
30
20
40
47
33
25
13
21
16
10
83
94
62
40
A
decane equivalents
B
50 % or more of the measurements were below the LOQ
3.7
6.1
0.67
23
4
1.7
4.3
<0.18
22
18
1.0
4.9
<0.29
59
8
0.81
1.8
<0.21
9.0
30
3
3.5
9.4
<0.21
54
31
8
0.36
1.9
<0.10
28
Pellet press
3
39
41
27
62
23
24
18
30
0
6.1
11
0.70
20
4
1.1
1.6
0.40
3.4
0
2.7
3.2
0.64
4.3
6
0
15
26
2.2
45
6
4
-B
-B
<0.10
4.5
Kiln
-
-
0
3.7
4.7
1.1
12
-
0
0.85
0.95
0.50
1.7
8
0
7.7
9.6
2.9
22
8
0
7.3
13
1.6
34
Raw material
-
-
1
-B
-B
<0.18
0.92
-
2
-B
-B
<0.21
<0.21
2
1
-B
-B
<0.21
1.3
2
0
0.71
0.72
0.64
0.79
Grinder
42
31
36
10
94
1.3
3.9
0.18
30
51
0.71
2.5
<0.18
22
71
0.62
2.0
<0.28
59
72
-B
-B
<0.21
9.0
119
43
1.1
5.2
<0.21
54
123
50
0.31
1.8
<0.10
34
Total
Table 5. Air concentrations of total dust, D-pinene, E-pinene and '3-carene from studies I and II; air concentrations resin acids, the total level of
VOCs (TVOC) and nitrogen dioxide from study II obtained in area measurements during wood pellet production at eight different sites.
4.1.3 Resin acids
The mean recoveries of the resin acids from filters were 99 %, 100 %, 99 % and
100 % for 7OXO, DHAA, AA and PA, respectively. For the measurements of
resin acids, levels of 7OXO and DHAA were included in the sum (personal monitoring, 12 % and 34 % below LOQ, respectively; area measurements, 28 % and
46 % below LOQ, respectively), but not AA and PA since most of their samples
had levels of these substances that were below the LOQ, for both personal monitoring (85 % and 82 %, respectively) and area measurements (87 % and 88 %,
respectively). The GMs for personal exposure to resin acids varied between 0.99
and 1.2 Pg/m3 during the three working operations (Table 4). However, personal
exposures to AA and PA levels were found to be higher when quantified (0.55-5.3
and 1.4-37 Pg/m3, respectively) than those of 7OXO and DHAA (data not
shown). For the area measurements of resin acids the GMs for the different sites
varied from 0.35 to 1.2 Pg/m3, with the highest level at the pellet press (Table 5).
The Pearson correlation coefficients between levels of resin acids and total dust
were 0.76 (p <0.001) for personal exposure and 0.71 (p <0.001) for area measurements. However, the correlations varied substantially between the different
sites of area measurement. For instance, the correlation coefficients for the pellet
press, storage and bagging sites were 0.90, 0.60 and 0.16, respectively. For the
other sites the levels of total dust were all under the LOQ, or there were too few
measurements to calculate meaningful correlation statistics.
4.1.4 Nitrogen dioxide, VOCs and carbon
bon monox
monoxide
For personal monitoring of nitrogen dioxide the levels ranged between 4.0 and
370 Pg/m3 (Table 4) and in the area measurements an average level of 36 Pg/m3
was obtained, with the highest average level detected at the bagging site followed
by the kiln (Table 5). The total amounts of volatile organic compounds (TVOC)
in the area measurements varied between 0.18 and 30 mg/m3 decane equivalents
(Table 5) and identified substances included terpenes (e.g. D-pinene, E-pinene, '3carene and limonene), aldehydes (C6-C11, e.g. hexanal, heptanal and nonanal),
hydrocarbons (e.g. ethylacetate, propionic acid and 1-pentanol) and 2-butanone,
which respectively accounted for 3 %, 15 %, 2 % and 5 % of the TVOC. During
the carbon monoxide measurements, no changes in colour were seen in the colorimetric tubes during the first day in any sample, so the tubes were kept in situ
overnight and during the following day. This resulted in a measurement period of
43
around 35 hours and a detection limit of 1.6 mg/m3. No changes in colour were
seen during the second day of monitoring either.
4.1.5 Estimated variation components and determinants of exposure
All determinants were tested simultaneously in the models, and in model 4 significant regression coefficients were: all determinants for inhalable dust; cleaning
and work in the control room for total dust; and cleaning for resin acids (data not
shown). After adjusting for the average exposure level at each plant (model 5),
significant determinants were the proportion of the day spent cleaning and time
cleaning with compressed air for inhalable dust, while for total dust and resin
acids only the amount of time spent cleaning was significant (data not shown). As
expected, cleaning and cleaning with compressed air were positively correlated
with exposure, while time spent in the control room was negatively correlated.
The within-worker variance accounted for 57 %, 91 % and 99 % of the total
2
2
2
variance of exposure ( V WW
/[ V BW
+ V WW
]) to inhalable dust, total dust and resin
acids, respectively, according to model (1) with worker as random effect (Table
6). When between-group variance was also accounted for (model 2), the betweenworker variance were zero for total dust and resin acids. The between-group
variance estimates accounted for 34 %, 30 % and 15 % of the total variance
2
2
2
2
( V BG
/[ V BG
+ V BW
+ V WW
]) for inhalable dust, total dust and resin acids, respectively,
and the corresponding values for within-worker variance were 50 %, 70 % and
2
2
2
2
/[ V BG
+ V BW
+ V WW
]). Several uniformly-exposed groups (R0.95, BW d2)
85 % ( V WW
were identified: one comprising the whole group with respect to resin acids (Table
6); one consisting of the workers in one plant with respect to inhalable dust; and
two consisting of workers in each of two plants with respect to total dust and
resin acids (Table 7).
44
Table 6. The between-groupe, between- and within-worker variability, variance ratio,
between-worker fold range and attenuation for inhalable dust, total dust and resin acids
(sum of 7OXO and DHAA) during wood pellet production with worker (model 1) or
plant and worker (model 2) as random effects.
Random effects
Worker
Inhalable dust
2
Vˆ BW
2
VˆWW
O
R0.95, BW
Attenuation A
nB
Plant and worker
2
Vˆ BG
2
Vˆ BW
2
VˆWW
Attenuation C
k=2
nD
k=5
k=9
2
- estimated between-worker variance
Vˆ BW
Total dust
0.41
0.55
1.3
12
0.40
12
0.08
0.78
9.8
3.0
0.83
88
0.36
0.17
0.52
0.07
6
3
2
0.27
0.00
0.65
-E
-E
-E
-E
Resin acids
0.0077
1.4
180
1.4
0.98
1 600
0.22
0.00
-E
-E
-E
-E
-E
2
- estimated within-worker variance
VˆWW
2
Vˆ BG - estimated between-group variance
2
2
/ Vˆ BW
O = VˆWW
R0.95, BW - fold range
A
the attenuation with two repeated measurements according to equation (6)
B
number of measurements needed to yield an attenuation of 10 % according to equation (6)
C
the attenuation with two repeated measurements according to equation (7)
D
number of measurements needed to yield an attenuation of 10 % according to equation (7)
with k as the number of subjects per group
E
2
undefined since Vˆ BW
=0
4.1.6 Overexposure
The likelihood ratio test of equal within-worker variance between plants yielded a
non-significant result for exposure to all examined substances. Consequently,
data acquired at the different plants were pooled, in accordance with the test procedure of Weaver et al., 2001 (Table 7). The tests of the probability of overexposure showed that it could not be inferred to be below 10 % at any of the four
plants for inhalable dust, and thus exposure to this substance was classified as
unacceptable (Table 7). Crude estimates of this probability varied between 1397 % for plants 1-3 (undefined for plant 4). For total dust the exposure was
classed as acceptable in plant 3 and unacceptable at the other plants, while the
exposure to resin acids was found to be acceptable at all plants.
45
Table 7. The estimated mean concentration, between- and within-worker variance, probability of overexposure, and classification of the exposure for inhalable dust, total dust
and resin acids (sum of 7OXO and DHAA) during wood pellet production.
Exposure
Inhalable dust
Total dust
Resin acids
Tˆ
Plant
Plant 1
P̂ x
2
VˆWW
2
Vˆ BW
3.8
0.45
0.21
Plant 2
1.7
0.45
0.43
Plant 3
0.83
0.45
0.34
9.8
13
Unacceptable
Plant 4
1.2
0.45
0.00
1.0
-A
Unacceptable
Plant 1
1.2
0.58
0.00
1.0
-A
Unacceptable
Plant 2
0.73
0.58
0.12
3.9
12
Unacceptable
Plant 3
0.29
0.58
0.28
8.0
2
Plant 4
0.64
0.58
0.00
1.0
-A
Plant 1
2.0
1.2
0.08
3.0
Appr. 0
Acceptable
Plant 2
0.88
1.2
0.00
1.0
-A
Acceptable
Plant 3
0.58
1.2
0.00
1.0
-A
Acceptable
Plant 4
1.4
1.2
Acceptable
0.25
7.1
Appr. 0
Tˆ - estimated probability of overexposure
P̂ x - estimated mean concentration
2
- estimated within-worker variance
VˆWW
2
- estimated between-worker variance
Vˆ BW
R0.95, BW - fold range
R0.95 BW
6.0
13
97
Conclusion
Unacceptable
55
Unacceptable
Acceptable
Unacceptable
Appr. - approximately
A
2
undefined since Vˆ BW
=0
4.1.7 Attenuation
Based on the estimated variance components the attenuation according to the
individual-based model varied between 40-98 % with two repeated measurements (equation 6) and the numbers of measurements needed per participant to
obtain an attenuation of 10 % was 12 to 1 600 (Table 6). For the group-based
model, attenuation could only be estimated for inhalable dust and was 7 % for
two repeated measurements (equation 7). The numbers of measurements needed
for an attenuation of 10 % were 2 to 6 with 2 to 9 participants per group.
4.2 Dermal exposure (Paper
(P
IV)
4.2.1 In vivo study,
y, recovery and stability tests
The amounts of 7OXO, DHAA and AA recovered in the human in vivo tests
(sums of the three tapes), after two minutes residence time were 28-32 % and
after 30 minutes of exposure 20-25 % for solution 1 (Table 8). For the solution
with a lower concentration (solution 2), the recovery was 34-36 % for 7OXO
and AA at both 2 and 30 minutes residence time. However, DHAA had a higher
46
recover for solution 2 then the other substances and then DHAA at solution 1
(64-67 %). The recoveries from tapes were t99 %, for all four substances. All
results presented are related to a 100 % recovery from the tapes. The mean recoveries from the glass plate were only 48 % for AA at the low applied amount,
while for 7OXO and DHAA they were t85 %. For the higher applied amount the
recoveries were t92 % for all three substances. All four substances were highly
stable, since recoveries after 28 days at 20 ºC and 48 days at -20 ºC amounted to
93-101 % and 95-103 %, respectively (Table 8).
Table 8. Results from tests of recovery in vivo (sums of the three tapes), from tapes and
glass plates, as well as stability tests for 7OXO, DHAA, AA and PA.
Test
7OXO
DHAA
Recovery
- in vivo, solution 1 A, 2 min
AM (%)
32
33
AM (%)
24
25
- in vivo, solution 1 A, 30 min
AM (%)
34
67
- in vivo, solution 2 B, 2 min
AM (%)
34
64
- in vivo, solution 2 B, 30 min
- tape
AM (%)
99
100
AM (%)
85
101
- glass plate, 1 600 ng C
AM (%)
99
92
- glass plate, 16 000 ng D
Stability
28 days at 20 ºC
Recovery (%)
101
101
48 days at -20 ºC
Recovery (%)
102
102
AM - arithmetic mean
A
applied amount: 13 800 ng 7OXO, 17 550 ng DHAA and 16 200 ng AA
B
applied amount: 1 500 ng 7OXO , 1 800 ng DHAA and 1 500 ng AA
C
applied amount of 7OXO was 1 000 ng
D
applied amount of 7OXO was 15 000 ng
E
not analyzed
AA
PA
28
20
36
35
100
48
93
-E
-E
-E
-E
100
-E
-E
93
103
100
95
4.2.2 Field study
The GMs of 7OXO in the dermal exposure tests varied between 7.9 and
9.6 ng/tape in the pre-shift samples, and from 15 to 20 ng/tape in the post-shift
samples (Table 9). Percentages of forehead, neck, forearm and hand samples with
7OXO levels exceeding the LOQ amounted to 14 %, 4 %, 7 % and 17 %, respectively, amongst the pre-shift samples, and 41 %, 34 %, 32 % and 44 %
amongst the post-shift samples. For DHAA the pre- and post-shift GMs were
140-150 ng/tape and 160-180 ng/tape, respectively (Table 9). Higher proportions
of post-shift samples had quantifiable levels of DHAA (21 %, 16 %, 16 % and
19 % of forehead, neck, forearm and hand samples, respectively) than corre-
47
sponding pre-shift samples (5 %, 8 %, 8 % and 9 %, respectively). In addition,
few samples contained higher than LOQ levels of AA and PA (data not shown).
Table 9. Dermal exposure to 7OXO and DHAA on three consecutive tapes applied to
each sampling spot in pre- and post-shift sampling during wood pellet production. The
limits of quantification were 7.5 ng/tape and 125 ng/tape for 7OXO and DHAA, respectively.
Amount (ng/tape)
Forehead
Substance
Time
N
n >LOQ
GM
AM
Max
7OXO
Pre-shift
183 A
26
9.1
11
140
DHAA
Post-shift
186 B
76
18
43
520
Pre-shift
183 A
10
140
170
2 800
Post-shift
186 B
39
180
270
2 800
Neck
n >LOQ
GM
AM
Max
7
7.9
8.7
14
63
15
30
390
14
140
190
2 600
29
160
250
3 700
Forearm
n >LOQ
GM
AM
Max
12
8.2
9.0
30
59
16
41
840
15
140
190
3 100
29
160
260
9 700
Hand
n >LOQ
31
GM
9.6
AM
12
Max
100
N - number of measurements
LOQ - limit of quantification
GM - geometric mean
80
18
34
20
150
170
48
190
250
700
2 400
3 500
AM - arithmetic mean
A
N was 186 for the forearm
B
N was 185 for the forearm and 180 for the hand
During the first sampling occasion for each individual statistically significant increases in exposure were detected during the work shift at all skin locations for
7OXO (pd0.003) and DHAA (pd0.012), while during the second sampling occasion the only significant increases in exposure over the shift were seen for 7OXO
at the forehead (p = 0.031) and the front of the neck (p = 0.016). Higher levels of
7OXO and DHAA were also observed in tapes 2 and 3 compared to the preceding tapes in 10 and 30 cases in the pre-shift monitoring, respectively, and in 31
and 29 cases in the post-shift sampling, respectively (Figure 9).
48
49
DHAA (ng/tape)
3
3
1
2
0
Tape
1 000
1 000
0
500
1 500
1 500
500
2 000
2 000
2 500
0
2 500
(c)
2
3 000
1
500
1 000
1 500
3 000
0
500
1000
1500
3 000
3 500
2500
2000
4 000
(a)
3000
1
1
Tape
2
(d)
2
(b)
3
3
Figure 9. Increases in DHAA levels in the series of tapes before (. . .) and after (___) work shifts applied to the forehead (a), neck (b),
forearm (c) and hand (d).
DHAA (ng/tape)
5 Discussion
5.1 Air exposure (Papers I-III)
5.1.1 Wood dust
The levels of wood dust found in the wood pellet plants were somewhat higher
than generally reported in joinery shops, saw-, lumber and plywood mills, and for
woodwork teachers, and similar to those found previously in the furniture and
wood-handling industries (Table 1). Since the upper respiratory system is the
main site affected by wood dust ideally inhalable dust should be measured, rather
than total dust in exposure assessments. However, the latter was measured in
study I, since the OEL for wood dust in Sweden at the time of the study referred
to total dust.
The results show that levels of exposure to wood dust in this industry are high,
and 35 % of the inhalable dust measurements exceeded the Swedish OEL. The
exposure was also classified as unacceptable for inhalable dust for all four plants
since it could not be inferred with certainty that the probability of overexposure
was d10 %. These findings indicate that the levels of wood dust are likely to have
implications for the workers’ health. For example, several studies have found indications that exposure to around 1 mg/m3 of wood dust, measured as total dust,
may lead to reductions in lung function (Eriksson and Liljelind, 2000; IARC,
1995), and levels over 0.5 mg/m3 (total dust) should be avoided since they can
induce adverse pulmonary effects (SCOEL, 2003).
In this study, inhalable dust levels were on average 3.2 times higher than total
dust levels, in accordance with previous studies where levels have been on average
1.6-4 times higher (Davies et al., 1999; Lidén et al., 2000; Harper and Muhler,
2002; Tatum et al., 2001). However, it should be noted that the inhalable fraction was always monitored on the right side and total dust on the left side of the
workers’ chests, which could have biased the results. This bias could have been
avoided by randomizing the monitoring between the two sides of the chest or by
side-by side sampling, which is often used (Lidén et al., 2000; Davies et al., 1999;
Harper and Muhler, 2002). However, the fact that the ratios of inhalable dust to
total dust levels found in this study were consistent with ratios found in previous
studies indicates that the bias was not unacceptably high.
51
5.1.2 Monoterpenes
Personal exposure to monoterpenes did not exceed the present Swedish OEL of
150 mg/m3 (AFS, 2005) in any of the studied production plants and the detected
levels were lower than those previously found in joinery shops (Eriksson et al.,
1997) and in saw- and lumber mills (Liljelind et al., 2001; Hedenstierna et al.,
1983; Eriksson et al., 1996; Lindberg, 1979; Svedberg and Galle, 2000), although
similar levels have been observed in plywood mills (Fransman et al., 2003), and
amongst woodwork teachers (Åhman et al., 1996). The low levels of monoterpenes may be explained by the fact that raw material is processed before it
reaches the wood pellet plants, which may also explain the relatively low levels
seen in plywood mills and for woodwork teachers. In study I, higher levels of
monoterpenes were also observed when fresher raw materials were used in the
process.
The area measurements also indicate that monoterpene levels were low. The
highest average levels of monoterpenes were observed near the kilns, in accordance with a previous finding that found most (68-95 %) terpenes are released in
wood pellet production during the drying process (Ståhl et al., 2004). Since the
plants are seen as representative for this industry the results strongly indicate that
monoterpene levels are low during the production of wood pellets, so it should be
unnecessary to monitor monoterpenes provided that there are no major changes
in the production process.
5.1.3 Resin acids
It was found that workers were exposed to 7OXO as well as other resin acids,
notably DHAA, which has not been shown previously in wood processing and
handling plants. Exposure to resin acids was generally lower than the British OEL
of 50 Pg/m3, although occasionally levels up to 74 % of the OEL were measured,
and was classified as acceptable with respect to risks of overexposure for all four
plants. The correlation coefficient (r) between resin acids and total dust was fairly
strong. However, 42 % (personal exposure) and 50 % (area measurements) of
the variation in resin acid levels (1-r2) cannot be explained by the variation in total dust levels. This may be partly because dust from pine and spruce wood contains different amounts of resin acids (Fengel and Wegener, 1983). The plants
reported that approximately half of the raw material they used over the year
originates from spruce and half from pine trees, but they were unable to specify
52
the proportions of spruce and pine that were being processed when the measurements were performed. It is also possible that, contrary to our assumptions, some
of the dust measured was not wood dust and it was also noticed that the correlation between the resin acids and total dust varied between the different monitoring sites, being highest at the pellet presses, followed by the storage and bagging
stations. This may be due to the presence of variable amounts of other types of
dust in the samples in addition to wood dust. The pellet presses are in a closed
area, in which wood dust may comprise larger proportions of the total, while the
bagging and storage sites are in open areas. So, other sources may include clay,
metal, gravel, plastic from the bags used in bagging and diesel exhaust.
5.1.4 Nitrogen dioxide, VOCs and carbon monoxide
Levels of nitrogen dioxide were low, and below the Swedish OEL of 4 000 Pg/m3,
or 2 000 Pg/m3 if considered as diesel exhaust, in all samples (AFS, 2005). Nitrogen dioxide was used as a marker for diesel exhaust, but since nitrogen dioxide
was detected in areas where no trucks were present or vehicles spent short times,
the emissions may have originated from the production process. Therefore, may
not nitrogen dioxide be an appropriate marker for this work environment, and
elemental carbon could be an alternative.
Generally, major constituents of the VOCs were aldehydes, which are known to
be upper airway irritants and have been detected at high levels in areas where
wood pellets are stored (Svedberg et al., 2004). Thus, they are potentially important compounds to monitor when assessing personal exposure. However, despite
being relatively important contributors to the TVOCs the levels of aldehydes in
the wood pellet plants were low, although it should be noted that recoveries of
aldehydes from TENAX tend to be low, which can lead to their concentrations
being underestimated (Hallama et al., 1998). Thus, the two main conclusions
from the VOC screening are that TENAX is not an ideal adsorbent for sampling
in the investigated environments, and VOC monitoring should focus on aldehydes.
Detected levels of carbon monoxide were consistently below the limit of detection
(<1.6 mg/m3) after 35 hours of sampling, and below the Swedish OEL of
40 mg/m3. The results indicate that exposure to this substance is not hazardous at
the plants participating in study II. However, carbon monoxide has been detected
53
in wood pellet storage facilities (Svedberg et al., 2004) and deaths have occurred
due to high levels of the chemical during the shipping of wood pellets in both
Holland (Swaan, 2002) and Sweden. Since the use of wood pellets in private
households has increased rapidly in recent years (PIR, 2006) and people sometimes store wood pellets in their cellars it would be of interest to monitor exposure levels in private households. In addition, VOCs should be monitored, especially aldehydes, which have low smell thresholds, due to complaints to producers
regarding smells originating from the storage of wood pellets at private homes.
5.1.5 Estimated variation components and determinants of exposure
The results show there was greater variability in exposure between work shifts
than between workers, indicating that the work practices of individual workers’
affect the variability to a lesser extent than the differences in exposure between
the days. Exposure to inhalable dust also seems to be more dependent on personal behaviour than exposure to total dust, because the between-worker fold
was 12 for inhalable dust, and the within-worker variation estimates accounted
for less of the total variability than for total dust and resin acids.
Within-worker variability has often been found to be greater than betweenworker variability in various work environments (Burdorf and Van Tongeren,
2003; Symanski et al., 2006; Kromhout et al., 1993; Nieuwenhuijsen et al.,
1995). In studies of exposure to particulates, higher within-worker variability
then between-worker variation estimates have been found in some cases (Scheeper
et al., 1995; Vinzents et al., 2001; Tjoe Nij et al., 2004; Burstyn and Kromhout,
2000; van Tongeren et al., 1997; Symanski et al., 2000; van Tongeren et al.,
2000; Kromhout et al., 1993; Preller et al., 1995) and lower within-worker variability in others (Rappaport et al., 2003; Kromhout and Heederik, 1995; Tjoe Nij
et al., 2004; Mwaiselage et al., 2005; Houba et al., 1997; Nieuwenhuijsen et al.,
1995; Peretz et al., 1997; Scheeper et al., 1995). Large within-worker variation
has also been seen in: mobile groups working outdoors (Burdorf and Van Tongeren, 2003; Peretz et al., 1997); in groups working on intermittent processes
(Burdorf and Van Tongeren, 2003); groups working without local exhaust ventilation (Burdorf and Van Tongeren, 2003); and those working in environments
where there are mobile sources of exposure (Peretz et al., 1997). The tasks involved in the production of wood pellets meet all of these criteria. It has also been
thought that measurements on consecutive days may lead to autocorrelation,
which in study II would lead to lowered between-worker variability. However,
54
relatively weak autocorrelation was observed in the cited studies (Francis et al.,
1989; Kumagai et al., 1993; Symanski and Rappaport, 1994). Our betweenworker variation estimates were zero for exposure to total dust at plant 1, resin
acids at plants 2 and 3, and for inhalable dust and total dust at plant 4. This may
occur when the sample size is small, and/or the within-worker variability is much
larger than the between-worker variability (Brown and Prescott, 1999).
Cleaning, and cleaning with compressed air, were positively correlated to exposure, in accordance with the findings of a real-time exposure monitoring program, indicating that these tasks are associated with high exposure. In accordance
with area measurements indicating low levels (d0.18 mg/m3) of total dust in the
control rooms, time spent in the control room was negatively correlated with exposure. The total group of workers could be defined as a uniformly-exposed
group with respect to resin acids. However, in further analyses workers at only
two of the plants could be defined as uniformly-exposed groups.
5.1.6 Overexposure
Compliance testing assesses whether individual measurements exceed relevant
OELs. In contrast, chronic health effect evaluations are more concerned with
long-term, cumulative exposures (Tornero-Velez et al., 1997; Rappaport et al.,
1995; Rappaport, 1991), and in that context the most suitable indicator of unacceptable exposure levels is the risk of overexposure. This is especially important
when sample sizes are small and there is a high risk of measurements exceeding
the OEL (as in study II), since it has been shown that compliance testing can underestimate health risks in such cases (Tornero-Velez et al., 1997). The estimated
probability of overexposure for inhalable dust varied between 13-97 % for plants
1-3 (unacceptable at all plants) and 12 % at plant 2 and 2 % at plant 3 for total
dust (acceptable at plant 3). However, it should be noted that the estimates of the
probability of overexposure are biased and should only be regarded as rough indications, while the test of the probabilities is more reliable (Lyles et al., 1997).
The exposure to resin acids was found to be acceptable at all plants in comparison to the British OEL for colophony, with estimates of the probability of overexposure close to 0 %. However, since the overexposure is calculated by comparing acquired data to a given OEL, the results would be altered if the OEL is
changed. Ideally, OELs should comfortably exceed levels at which there are significant health risks for workers. If an OEL is set too low, there may be implications for the workers’ health, even if no measurements exceed it. There may be a
55
greater uncertainty in the OEL for colophony than for wood dust since wood
dust has been studied more intensively. However, in this case the OEL would
have to be 10-fold lower for the proportion of estimated overexposures to rise to
around 10 %.
5.1.7 Attenuation
As stated above, higher levels of wood dust and lower levels of monoterpenes
have been seen in this industry than in other wood-processing industries, warranting investigations of the worker’s health risks, since they could differ from those
in the other industries. The individual-based model analyses indicated that the
level of attenuation was high, implying that exposure-response relationships derived from the data would be subject to substantial bias, leading to complications
in attempts to draw conclusions in an epidemiological study. According to the
individual-based model, 12 repeated measurements for inhalable dust, through 88
for total dust to 1 600 for resin acids would have been needed to reduce the attenuation to d10 %. These are high numbers compared to corresponding numbers of repeated measurements (2-42) found in other studies in which exposure to
particles has been examined (Scheeper et al., 1995; Vinzents et al., 2001; Tjoe Nij
et al., 2004; Burstyn and Kromhout, 2000; van Tongeren et al., 1997; Symanski
et al., 2000; van Tongeren et al., 2000; Kromhout et al., 1993; Preller et al.,
1995, Rappaport, 2003 #645; Kromhout and Heederik, 1995; Mwaiselage et al.,
2005; Houba et al., 1997; Nieuwenhuijsen et al., 1995; Peretz et al., 1997). Attenuation can be decreased by maximizing the differences between workers’ exposure levels in relation to the within-worker variation (Liu et al., 1978; Kromhout and Heederik, 1995), or by using a grouping strategy rather than an individually-based strategy (Kromhout et al., 1996; Tjoe Nij et al., 2004; Teschke et
al., 2004; Schlünssen et al., 2004; Mwaiselage et al., 2005; Nieuwenhuijsen et al.,
1995), as seen in study II. For inhalable dust the attenuation was decreased from
40 % for the individual-based model to 7 % for the group-based model with two
repeated measurements per person. However, a group-based strategy will give less
precision in estimates of dose-response relationships (Kromhout et al., 1996) and
it may therefore be better to use an individual-based model, even if large numbers
of repeated measurements are required.
56
5.2 Dermal exposure (Paper IV)
5.2.1 In vivo study,
y, recovery and stability tests
The mean recovery of the resin acids from human skin was low and variability
between individuals high. This may be because the resin acids have reacted with
substances present in the SC and/or the resin acids may have diffused beyond the
SC. The in vivo test also showed significantly lower levels of AA than of 7OXO
or DHAA when application of solution 1 (47 550 ng of resin acids) and significantly higher recovery of DHAA than of 7OXO and AA following exposure to
solution 2 (4 800 ng of resin acids). The skin is not uniform in terms “of amount
of SC”, density of hair follicles and sweat glands, and many other parameters that
will affect permeability even within a relatively small skin area (Breternitz et al.,
2007), which may explain the differences in recovery of individual resin acids.
The spiking experiments with the tape used and the tests in which the analytes
were tape-stripped from a glass plate showed that recoveries of the resin acids
were sufficiently quantitative for the tape-stripping technique to provide reliable
indications of dermal exposure to 7OXO, DHAA, AA and PA. Following application of the resin acids to the glass plate, quantifiable amounts of all studied
compounds were detected on all three consecutive tapes, indicating that the acids
may not have been efficiently removed from the glass surface, which may have
been due to saturation of parts of the first tape. However, this does not apply to
the tapes used to retrieve the 1 600 ng application of AA, the recovery of which
was low and the amounts on the second and third tapes were below the LOQ. In
this case it is possible that if the second and third tapes had been extracted in the
same 3 ml volume of methanol used to extract the first tape the overall recovery
level may have been higher.
5.2.2 Field study
The detection of resin acids in pre-shift samples may be explained by the fact that
a few of the workers might have become contaminated when they entered the
production area before their shift or changed into work clothes that were probably contaminated. Another possibility is that the resin acids remained from previous exposures. Most of the tapes collected both before and after a shift were uncontaminated by resin acids. However, it should be borne in mind that the skin is
probably not uniformly contaminated by resin acids, and since only a relatively
57
small surface area is sampled it is possible that some contamination “hot spots”
were missed.
Resin acids were detected on the second and third tapes as well as the first, and it
is very intriguing that for some individuals the highest amount was observed on
the third tape. This indicates that resin acids can penetrate the SC, and probably
also the viable epidermis, where the substances or their metabolites may behave
as haptens or prohaptens. 7OXO has been reportedly observed to have been haptenated with L-lysine, and this mechanism may be involved in the development of
occupational asthma and contact dermatitis (Smith et al., 1999). However, even
though the skin area to be sampled was carefully marked in an attempt to ensure
that the same area was sampled by the successive tape-strips, the second and/or
third strips may have been slightly misplaced, and small areas of unsampled skin
may have been sampled by them.
It would have been of interest to examine how deeply the resin acids may have
penetrated the SC, which could have been done by using more tapes to strip further layers and using up to 10 tapes should not have posed any significant risks to
the workers (Jacobi et al., 2005). It would also have been of interest to analyse
how much of the SC was sampled with each tape. This was attempted using the
method described by Chao and Nylander-French (2004), but unfortunately the
samples were damaged and no meaningful results were obtained. In addition, attempts to estimate the amounts retrospectively is not possible since the amount of
SC sampled varies with differences in variables such as the cohesion between the
cells (King et al., 1979), individual differences (Holbrook and Odland, 1974),
body region (Holbrook and Odland, 1974; Breternitz et al., 2007), furrows (van
der Molen et al., 1997), hydration of the SC (Weigand and Gaylor, 1973), and
pressure applied during application of the tape (Breternitz et al., 2007).
Increases in exposure to 7OXO and DHAA during shifts were detected during the
first sampling occasion (n = 41) at each of the sampled skin sites. During the second sampling occasion, exposure was determined only for 20 workers, which
reduced the statistical power of the test used. However, increases in exposure
were also observed on the second occasion for 7OXO at two of the skin sites: the
front of the neck and the frontal side of the forearm. Repeated measurements
were carried out on individuals, and to minimize bias due to this practice, separate tests were performed for each individual during the first and second sampling
58
occasions. It should also be noted that work tasks done by the workers may have
differed between the two sampling occasions, and more care may have been taken
to avoid dermal exposure on the second occasion.
5.2.3 Dermal occupational exposure limits
In the Swedish OEL list there is a skin notation referring to chemicals that can
easily be absorbed through the skin (AFS, 2005), but as in other countries, no
dermal occupational exposure limits (DOELs) have been set. Historically, exposure analyses have focused on monitoring exposures via the air and on applying
OELs, probably because air exposures often make the largest contributions to
total body doses. However, since air levels of chemicals have decreased, the proportional contribution of dermal uptake to total exposure doses may have increased (Fenske and van Hemmen, 1994; Schneider et al., 1999), and for some
substances, like pesticides and PAHs, dermal absorption is an exposure route that
can cause poisoning (Semple, 2004). Unfortunately, there are considerable difficulties to overcome before setting DOELs, since there is often little knowledge
concerning the uptake rate of target substances, and the areas of exposed skin
have to be taken into account, in addition to the concentrations of target analytes
and exposure times (McDougal and Boeniger, 2002). Furthermore, as mentioned
above, the mass of analytes is also often measured, rather than their concentration, which is the factor that drives the diffusion process (Cherrie and Robertson,
1995; Semple, 2004), so new methods may also be needed. Merely setting a
DOEL may help to drive the acquisition of further knowledge and the development of new measurement techniques. Even in cases where OELs have been set,
the available information on the effects and uptake rates of the substances concerned has often been scarce. For example, total dust was previously measured in
air exposure assessments, and still is sometimes, but we now know that total dust
is often poorly correlated with uptake of dust in the respiratory system. Similarly,
the observations that resin acids have local effects on the skin, and may penetrate
the SC, which can lead to systemic effects, indicate that attempts to further evaluate their effects and establish DOELs for them are warranted.
5.3 General discussion
For the reasons discussed above it is important to reduce exposure to wood dust,
and since the plants are seen as representative for this industry the results should
be taken into account in other, existing plants and in the establishment of new
59
facilities. I cannot draw any firm conclusions regarding the level of dermal exposure to the resin acids, but since they can cause contact dermatitis it is, of course,
advisable to reduce exposure to them too. Lowering the air exposure to wood
dust would probably also reduce the dermal exposure and good housekeeping is
also important to limit exposure that can occur via contact with contaminated
surfaces.
Measures that could be beneficial are increasing automation, improving local ventilation, identifying tasks associated with the highest levels of exposure and implementing good housekeeping practices. For instance, exposures would probably
be lowered if sawdust was automatically transported from storage areas to production stations instead of being moved on manually loaded trucks. For identifying tasks associated with high exposure risks, instruments such as DataRAM
monitors can be used in conjunction with work record sheets and analyses of determinants of exposure. In these studies, such monitoring and analysis of determinants highlighted the importance of cleaning, which should be a prioritised
task, preferably done using central vacuum cleaners, since sweeping and cleaning
with compressed air can lead to high exposure. In addition, respiratory protective
equipment (RPE) could be used for certain work operations associated with high
exposure, such as cleaning, but not continuously since that could be tiring and
cause discomfort for the workers. Regarding the use of RPE it is also important
to note that the efficiency of RPE in the workplace is often lower than reported in
standards or manufacturers’ literature, and that facial hair may cause leakage. It
is also important to clean, service and maintain the RPE well (Howie, 2005).
Biological monitoring is often used to assess dermal adsorption (Benford et al.,
1999) and/or to assess body loads, arising from both air and dermal exposure and
in some cases oral ingestion. The individual resin acids and their metabolites can
be used as biomarkers, for example DHAA has been used as a biomarker of exposure to colophony in soldering, (Jones et al., 2001; Baldwin et al., 2007), during which a positive linear relationship has seen observed between air levels of
solder fumes and urinary DHAA (Baldwin et al., 2007).
60
6 Conclusions
x
Workers’ personal exposure to wood dust was high compared to the Swedish
OEL and the exposure is classified as unacceptable with respect to the risk of
overexposure, indicating that the exposure is likely to have implications for
the workers’ health and thus should be reduced.
x
Real-time monitoring of wood dust with a DataRAM can identify critical
working tasks in which high wood dust exposures occur.
x
Personal exposure to monoterpenes in the studied workplaces is low compared to the present Swedish OEL, and levels previously observed in joinery
shops, saw- and lumber mills.
x
Overall the levels of resin acids were low and classified as acceptable, but occasionally levels up to 74 % of the British OEL were measured.
x
A larger variation in exposure between days than between workers was seen.
x
Cleaning is associated with slightly increased levels of exposure to wood dust
and resin acids, while work in the control room is associated with decreased
levels.
x
High attenuation was observed and may lead to underestimation of the
strength of a potential exposure-response relationship.
x
Occupational dermal exposure to resin acids can be assessed using a tapestripping method.
x
Quantifiable amounts of resin acids were detected on four different skin areas.
x
An increase in dermal exposure to resin acids during a work shift was observed.
61
7 Acknowledgments
Many people have contributed to the work reported in this thesis and made it all
possible. I would like to thank all of them, but especially:
My supervisor Kåre Eriksson for terrific cooperation and for being a superb supervisor. I think we complemented each other perfectly, I really enjoyed our discussions and I’m looking forward to more cooperation in the future.
My co-supervisor Gunilla Lindström for giving me the opportunity to do my doctorate at Örebro University, and prompting me to meet all the associated requirements on time.
My boss, Håkan Westberg, for luring me to Örebro and the department, for providing me with the opportunity to do my PhD and for all the support.
Helena Arvidsson, my friend and co-worker. Without you there wouldn’t have
been a study II. I thank you for your help, ideas, our productive, enjoyable cooperation in the field and all the nice talks about work and life in general.
Sara Axelsson, my friend and co-worker, for developing new analytical methods
when I needed them, helping me with a million things and always being next door
to me if I needed to talk.
Ing-Liss Bryngelsson, my friend and co-worker, thanks for doing all that you do
so well, teaching me some research ethics and encouraging me when I needed it
most. It would not have been as much fun without you.
Cecilia Lundholm, my friend and co-worker, for helping me understand the
world of statistics and doing superb work.
The wood pellet plants involved and the participating workers, without whose
cooperation there would not have been any studies.
Anders Seldén, who thought about investigating the exposure and workers health
at wood pellet production first and Peter Berg for letting me taking care of the
exposure assessments in study I.
All my other co-authors: Håkan Löfstedt, for starting this with me, Ingrid Liljelind, for teaching me about variation analysis, and Leena Nylander-French, who
showed me the interesting field of dermal exposure.
Eva Andersson, our medical doctor in study II, thanks for your inspiration and
for being a superb travelling companion.
Gunilla Färm, Marianne Andersson, Lena Ohlin, Mona Svensson, Gerd Lidén,
Sofia Loodh, Margareta Jurstrand, Britt-Marie Larsson, Maria Aksbjer, Marga-
63
reta Landin, Marita Nyström, Carl-Göran Ohlson and Katarina Perälä for valuable cooperation and help.
Anita, Bernt, Bims, Birgitta, Carin, Jessica, Krister, Leif, Lena, Lennart, Lisbet,
Lotta, Rigmor and Sibylla, for analysis, help with measurements and practical
matters, and/or just being a friend at the laboratory in the department.
All the employees at the Department of Occupational and Environmental Medicine for welcoming me to Örebro and making it interesting to go to work.
The people I worked with on the board of SYMF, for introducing me to occupational hygiene and to the board of NäPFo, Maria, Magnus and Johan for introducing me to the realm of wood pellet research and making the wood pellets conferences so much nicer.
My friends and co-PhD students: Anneli Julander, Malin Johansson, Ulrika Magnusson and Katja Boersma, it is always nice to have someone to discus the ups
and downs of PhD studies.
“Tjejgänget”, Elisabeth, Elsa, Emma, Karro, Lisa, Malin and Nadja, thanks for
sharing things big and small in life.
Klas and Carro, I’m so glad when we have time to meet, Victoria, 25 years of
friendship is a long time, thanks for everything, Susanne, for taking me shopping
when I need it and Leila, Helena and Johan for being my first friends when I
moved to Örebro.
My “new” family in Örebro: Stina, Eva, Stefan, Jenny, Sonja and Lars for welcoming me and making me part of your family.
My “old” family: Dad, Mikael, Viktor, grandmother Ann-Marie, grandfather
Ingemar, grandmother Margareta, Veronika, and my uncles Inge and Owe and
their families. For all your love and support, thank you!
My mother and Hardy, for teaching me that life is unfair, and for always believing in me and making me believe in myself. Mom, what can I say? You are an
inspiration and the best mother and friend ever.
And last but not least: Björn, thank you for loving me, listening and taking care
of me. I could not have done it without your support. And Linus, my son and
sunshine, a hug from you and the world is a happy place.
64
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in Electronics Recycling: Air and Human Plasma Levels.
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treatment of industrial sludge containing nitroaromatics.
8.
Svensson, Margareta (2006). Mercury Immobilization. A Requirement for Permanent Disposal of Mercury Waste in Sweden.
9.
Grahn Evastina (2006). Lake sediment as environmental archive
– natural and anthropogenic influence on the chronology of trace
elements.
10. Greis, Christina (2007). Rapid analysis of actinide isotopes using
quadrupole ICP-MS for emergency preparedness and environmental
monitoring.
11. Hagström, Katja (2008). Occupational exposure during production
of wood pellets in Sweden.