Effects of wood smoke particles from wood-burning stoves on the respiratory health of atopic humans
Particle and Fibre Toxicology
Effects of wood smoke particles from wood-burning stoves on the respiratory health of atopic humans
Ingunn Skogstad Riddervold 0
Jakob Hjort Bnlkke 0
Anna-Carin Olin 3
Therese Koops Grnborg 2
Vivi Schlnssen 0
Kristin Skogstrand 6
David Hougaard 6
Andreas Massling 5
Torben Sigsgaard 0 1 4
0 Department of Public Health, Section for Environmental and Occupational Medicine, Aarhus University , Aarhus , Denmark
1 Department of Public Health, Section for Environmental and Occupational Medicine, University of Aarhus , Bartholins Alle 2, Building 1260DK-8000, Aarhus C , Denmark
2 Department of Public Health, Section for Biostatistics, Aarhus University , Aarhus , Denmark
3 Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy , Gothenburg , Sweden
4 Department of Public Health, Section for Environmental and Occupational Medicine, University of Aarhus , Bartholins Alle 2, Building 1260DK-8000, Aarhus C , Denmark
5 Department of Environmental Science, Aarhus University , Roskilde , Denmark
6 Department of Clinical Biochemistry, Statens Serum Institute , Copenhagen , Denmark
Background: There is growing evidence that particulate air pollution derived from wood stoves causes acute inflammation in the respiratory system, increases the incidence of asthma and other allergic diseases, and increases respiratory morbidity and mortality. The objective of this study was to evaluate acute respiratory effects from short-term wood smoke exposure in humans. Twenty non-smoking atopic volunteers with normal lung function and without bronchial responsiveness were monitored during three different experimental exposure sessions, aiming at particle concentrations of about 200 g/m3, 400 g/m3, and clean air as control exposure. A balanced cross-over design was used and participants were randomly allocated to exposure orders. Particles were generated in a wood-burning facility and added to a full-scale climate chamber where the participants were exposed for 3 hours under controlled environmental conditions. Health effects were evaluated in relation to: peak expiratory flow (PEF), forced expiratory volume in the first second (FEV1), and forced vital capacity (FVC). Furthermore, the effects were assessed in relation to changes in nasal patency and from markers of airway inflammation: fractional exhaled nitric oxide (FENO), exhaled breath condensate (EBC) and nasal lavage (NAL) samples were collected before, and at various intervals after exposure. Results: No statistically significant effect of wood smoke exposure was found for lung function, for FENO, for NAL or for the nasal patency. Limited signs of airway inflammation were found in EBC. Conclusion: In conclusion, short term exposure with wood smoke at a concentration normally found in a residential area with a high density of burning wood stoves causes only mild inflammatory response.
Air pollution; Controlled exposure; Wood smoke; Particles; Airway inflammation; Lung function; Humans
Particulate air pollution can induce a major aggravation
of respiratory symptoms and diseases. Because of this,
the awareness of the impact of airborne particles related
to different sources of air pollution, particularly fine and
ultra fine particles, on human health is increasing. It is
established that exposure to secondhand tobacco smoke
particles during childhood increases the risk of asthma
and other allergic diseases . Growing evidence also
indicates that particulate matter (PM) from diesel vehicle
exhaust or concentrated ambient particles (CAPs) has the
potential to cause or exacerbate asthma [2,3]. Researchers
hypothesise that increased mortality can be associated
with the particle levels in urban air [4-7]. Several studies
have reported that especially the fine and ultrafine
particles have an adverse effect on airways; and that children
and asthmatics, among other vulnerable groups, may be at
greater risk for developing adverse health effects of air
Recent reviews have thoroughly discussed the
relationship between wood smoke exposure and health effects
. It is well-established within air pollution research, that
wood-burning stoves and fireplaces as well as agricultural
and wild fires emit significant quantities of known
healthdamaging pollutants to both ambient and indoor air. The
burning of wood gives rise to study pollutants like
chlorinated dioxin, carbon monoxide (CO), methane, volatile
organic compounds (VOC), nitrogen oxides (NOx), polycyclic
aromatic hydrocarbons (PAH), and particulate matter
(PM) [9,12]. These pollutants may trigger cough, throat
and mucosal irritation, can cause acute inflammation in
the respiratory system, and may contribute to an increased
incidence of asthma and allergic diseases observed after
prolonged exposure . Approximately one-third of the
worlds population and most of the rural households in
developing countries still rely on unprocessed biomass
fuels for cooking and heating . Wood, dung and crop
residues are typically burnt indoors on open fires or poorly
functioning stoves, often causing extreme pollution levels
indoors. In developing countries, these indoor pollution
levels can be a serious threat to the health of especially
women and children. Children are often carried on their
mothers backs while cooking and therefore spend many
hours breathing wood smoke particles and other related
pollutants . Some of the effects of wood smoke particle
exposure are decreased pulmonary function and evidence
of airway inflammation [8,15]. Furthermore, an increasing
number of studies indicate a correlation between wood
smoke exposure and lung diseases, such as acute respiratory
infections (ARI) [16,17] and chronic obstructive pulmonary
disease (COPD) [18-21]. Schei and colleagues showed that
the prevalence of symptoms of asthma were higher in
children from households that used open fires compared to
those with improved stoves with chimneys .
The levels of respirable particles from wood burning in
the outdoor environment in developed countries may be
magnitudes lower compared to high exposures indoor in a
rudimentary kitchen with poor ventilation. Still, there is
increased concern about possible health effects as a
consequence of wood smoke pollution due to the increasing use
of wood burning. In recent years, exposure to fine and
ultra fine airborne particles has been identified as an
important factor affecting human health in the developed
world [23-25], but the mechanisms underlying these
effects are still unclear.
Recently, four experimental studies assessing
comparable levels of wood smoke exposure on humans have been
conducted. In an exposure study from Gothenburg,
increasing alveolar NO and FENO270 (i.e. fraction of exhaled
nitric oxide at exhalation flow of 270 ml/s) were found 3
hours after exposure, indicating inflammation in the lower
respiratory system . In a second study from
Gothenburg the fraction of FENO also increased after wood
smoke exposure . This finding could not be confirmed
in the study by Sehlstedt and colleagues, where NO levels
were unaffected by wood smoke exposure, as were lung
function parameters . Ghio and colleagues reported
from their human experiment that wood smoke exposure
among other could be associated with pulmonary
In the present study, we report the results from
respiratory health measurements among atopics exposed
to wood smoke in a controlled experiment. The
hypothesis tested was that short-term exposure to wood
smoke could induce acute signs of nasal and pulmonary
inflammation. Other results from this study (coagulation
and systemic inflammatory biomarkers) will be reported
Figure 1 presents summary statistics on the achieved
exposure levels. Table 1 presents the estimated changes
from baseline to end of exposure (3 h) and to 6 h for
all the included outcomes, respectively. For the majority
of the outcomes the estimated values for the change
over time within each exposure are included in the
confidence intervals (CI) for the other exposures, suggesting
no differences in changes over time between exposures.
None of the outcomes except from conductivity and
nasal volume (vol2-4) seemed to be influenced by the
RH% and the CO levels between the exposures. Therefore,
all p-values, estimates and confidence intervals are based
on the models not including these variables.
The results of the lung function measurements are
presented in Figure 2. No statistically significant differences
over time between the three exposures were found for any
of the outcomes: PEF (p = 0.7453); FEV1 (p = 0.6283); and
FVC (p = 0.8364). FENO levels at baseline and after
exposure for each exposure are illustrated in Figure 3. No
significant effect of exposure over time was found for
FENO50 (p = 0.3578) or FENO270 (p = 0.5081).
Most of the measured nasal lavage cytokines did not
show any variations related to time or exposure and the
majority of the measurements were below the lower
detection limit (LDL). This was true for IL-4, IL-5, IL-10,
GM-CSF, IFN-, MCP-1, MIP-1, RANTES, TGF-1 and
TNF- and these data were therefore not analysed or
presented graphically. The curves for the remaining nasal
lavage cytokines are presented in Figure 4. Even though
Figure 4 suggests cytokine levels responding to wood
smoke exposure, none of the concentrations of the
analysed biomarkers in nasal lavage were statistically
significantly different when using the mixed model effect of
exposure over time. (IL-1: p = 0.3256; IL-6: p = 0.1133;
IL-8: p = 0.0704; IL-12: p = 0.1663; IL-18: p = 0.2139).
Curves for conductivity, pH and 8-isoprostane in EBC
are shown in Figure 5. Conductivity was not significantly
changed for exposure over time (p = 0.9998), but when
RH% and levels of CO were included in the model as
explaining variables the result reached statistical
significance (p = 0.0228). For pH there was a statistical significant
effect of exposure over time (p = 0.0331). The levels of
8isoprostane were found not to be significant for exposure
over time (p = 0.1795). Figure 4 shows a tendency to
increased inflammation for the high wood smoke exposure
6 hours post exposure initiation.
Figure 1 The number of particles with diameters of Dp10-110nm and of Dp110-700nm during high, low, and clean air exposure is shown.
Negative error bars for the lower size regime N10-110 show one standard variation and positive error bars for the larger size regime N110-700 show
also one standard variation of the measurement. Please note that concentration values for the clean exposure sessions are multiplied by a factor
of 10 (in order to make the bars visible on the figure).
The nasal patency assessed by the nostril sum of the
vol2-4 was not significantly affected by the exposure over
time (p = 0.5452). The time pattern changes in nasal
patency can be seen in Figure 6.
The aim of this study was to investigate whether short-term
experimental wood smoke exposure induced inflammation
in the airways of healthy atopic subjects. Despite the
relatively high particle concentrations during the wood
smoke exposure sessions and symptoms of airway
mucosal irritation as reported in detail elsewhere , very
few measurable effects were observed.
No statistically significant effect of wood smoke exposure
was found for any of the lung function measurements,
although there were non-significant indications of mild
airway inflammation in the high exposure sessions regarding
exhaled NO, airway inflammatory markers in NAL and
nasal patency. The only outcomes that were found to be
significantly associated to exposure over time were the
conductivity and pH level measured in EBC.
The specific wood smoke exposure levels used in this
study were chosen to have levels comparable with similar
studies and to reveal information of the possible dose
response associations between wood smoke exposure
and different health outcomes in atopics.
The dose during one day for the participants was in
the order of 318 ug TSP/day during the day of the high
concentration exposure lasting 3 h. Considering half the
ambient air annually mean of 30ug/m3 in DK as the
exposure for the rest of the day, an hourly inhaled amount
of 400 L and a deposition fraction of 0.5. This compares
to 75ug for a normal day in DK and 2,940 g for a day
in a citizen from Kenya who on average will be exposed
for at least 1,000ug/m3 for 14 hours daily . It follows,
that the dosing we have used is approximately 4 times
higher than a normal day in DK. However the dose was
much lower than an average personal exposure in Kenya
where the incidence of respiratory inflammation is high
and related to the use of biomass for burning inside
Changes in lung function as a method for measuring
health effects of exposure to PM air pollution have been
used for decades [8,31,32]. A study, completed during the
wood-burning season, showed that FVC and FEV1
decreased in association with increases in PM air pollution
in children . Our findings of no observed changes in
lung function is concordant with other experimental
studies on wood smoke showing no significant changes in any
lung function parameters investigated [27,34]. This lack of
change in lung function has also been found in other
studies where we observed airway inflammation ,
indicating that lung function measurements may be less sensitive
than other measurements.
FENO has been suggested as a marker of airway
inflammation and NO production has in some studies been
reported to increase with high levels of air pollution
[36,37]. Increased airway inflammation as assessed by
FENO measurements has been associated with
woodburning PM in asthmatics . Likewise, controlled
exposure to wood smoke has been associated to increased levels
in FENO270 20 h after exposure . Pietropauli et al., on
the other hand, did not find any increase in distal NO
production after exposure to ultrafine carbon particles . In
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b n o
Low Clean air
Baseline 30 min 3 hours 6 hours
Low Clean air
Low Clean air
High Low Clean air
Low Clean air
Baseline 30 min 3 hours 6 hours
Figure 2 Mean values of the different lung function measurements; FEV1 (A), FVC (B) and PEF (C) at baseline, after 30 min of build-up exposure,
at the end of exposure (3 hours) and 6 h post exposure initiation for the 3 exposures to clean filtered air, low and high concentrations of wood
smoke. Error bars represent +1 SEM.
Baseline 3 hours
Figure 3 Mean values of Fractional Exhale NO measurements: FENO50 (A) and FENO270 (B) are shown at baseline and at the end of exposure
(3 hours) for the 3 exposures to clean filtered air, low and high concentrations of wood smoke. Error bars represent +1 SEM.
Figure 4 Selected markers found in nasal lavage; Selected markers are illustrated by mean values of IL-1 (A), IL-6 (B), IL-8 (C), IL-12 (D) and IL-18
(E) at baseline, at the end of exposure (3 hours) and 6 h post exposure initiation for the 3 exposures to clean filtered air, low and high concentrations
of wood smoke. Error bars represent +1 SEM.
this experimental study neither FENO50 nor FENO270
increased after exposure to wood smoke. The fact that no
influence on FENO50 was found indicated no significant
inflammatory effect of the exposure on the conducting
airways. No increase in the FENO270, was represented, to a
larger extent than FENO50, NO deriving from the distal
airways. Our findings are in accordance with a previous
experimental study on effects of wood smoke exposure, in
which no influence on FENO was found .
Our observation of very limited signs of inflammation,
although weak, is in line with the two previous experimental
wood smoke exposure studies [27,34]. Even though, no
significant effects were found for any of the biomarkers
measured in nasal lavage fluid, there seems to be a changing
tendency of in the biomarker levels between exposures
which seems to be most pronounced for the acute
measurement just after exposure (see Figure 4). The general low
concentrations of the different cytokines in the nasal lavage
Baseline 3 hours 6 hours Detomisation
Figure 6 Mean values of nasal patency (Vol2-4 cm) are
illustrated; at baseline, at the end of exposure (3 hours) and 6 h post
exposure initiation for the 3 exposures to clean filtered air, low and high
concentrations of wood smoke. Detomisation represents the
measurement after inhalation of nose drops. Error bars represent +1 SEM.
Figure 5 Selected measurements obtained from the Exhaled Breath Condensate; Selected measurements are shown as mean values of
conductivity (A), pH (B) and 8-Isoprostane (C) at baseline, at the end of exposure (3 hours) and 6 h post exposure initiation for the 3 exposures
to clean filtered air, low and high concentrations of wood smoke. Error bars represent +1 SEM.
fluid may be explained by the method used where the fluid
is collected after only 30 seconds. Furthermore, the fact
that we flushed the nose twice before sampling in order to
clear the nose of debris could possibly cause unwanted
removal of the cells releasing the relevant biomarkers.
However, the method is continuously being optimised [35,39].
It is obvious from earlier studies, that there is a downward
shift in concentrations of all cytokines and cells from the
first NAL to the subsequent ones. Hence inflammatory
effects on nasal mucosa are seen as no deviation from
baseline, whereas no inflammation results in a drop in
cytokine level due to repeated lavage. We have further
observed a great intra-individual variation in the
pre-exposure concentration in the NAL . The introduction of
the pre-flushing was therefore set in place in order to bring
down intra-individual variation and to create a true zero
value. Figure 4B shows that there is a distinct different
pattern for IL-6 after clean air and high exposure. This may
be indicative of a slight inflammatory response after wood
smoke exposure leading to an exhaustion of the IL-6
level in the nose as previously proposed by Krger and
Airway inflammation was further assessed by evaluating
conductivity, pH, and levels of 8-isoprostane in exhaled
breath condensate (EBC). The condensate pH is one of the
most extensively studied nonspecific markers in EBC, and
has been reported to be related to eosinophilic and
neutrophilic inflammation of the airways . However, a
challenge with EBC samples is that concentrations of most
markers of inflammation are near detection limits,
resulting in high variability . Only pH levels were found to
be significantly affected by time-related exposure. Since
electrical conductivity quantifies the ion content, it may be
related to pH . More comparable patterns for pH and
conductivity were therefore expected. Isoprostanes appear
as metabolites in tissue and plasma samples, which have
undergone oxidative degradation during prolonged or
improper storage. The fact that levels of 8-isoprostane were
below the lower detection limit in the majority of our
samples cannot be explained by prolonged or improper
storage. It may therefore be considered if 8-isoprostane is a
relevant marker to look for in EBC. As seen in Figure 5,
changes in EBC seem to be most pronounced for the
measurement 6 hours post exposure initiation. The
presence of significant changes in the EBC only is probably a
reflection of the compartment sampled. Compared to
NAL EBC is sampled deeper in the airways, and therefore
represents an area with a higher susceptibility to
environmental challenge. The correction for RH and CO
increased the significance of the EBC findings which is
intriguing since we know that the ambient humidity
influences the toxicity of the particles and the CO-content. The
fact that no severe effects on the respiratory system were
found is supported by other findings in this study. We
observed borderline significant effects in the symptom
index of Weak Inflammatory Responses and no
significant effect of exposures were found for the symptom index
of Lower Respiratory Effects .
Although health outcomes and exposure conditions are
not directly comparable when studying respiratory
outcomes in populations at large and in controlled exposures,
no major effects were detectable during exposures lasting
a few hours in healthy participants at rest or mildly
exercising. Our findings are in concordance with other
investigations studying acute effects of short-term wood smoke
During the study, the problem occurred that despite the
fact that the exposure order was randomised, the baseline
values during some exposures clearly deviate from others.
Nevertheless, it is important to remember, that all
participants participated in all exposure session types, and that
the difference can therefore not be ascribed different
treatments groups. We have no reasonable explanation for this
variation and believe that we have done everything possible
to prevent this variation by using the balanced randomised
cross-over design, by pre-conditioning the participants
before starting the experimental day, and by introducing
pre-flushing of the nose. Considerable variations in both
the between-participants and within-participants variation
were present for most outcomes in this study. In a majority
of the cases, the variation seemed to be larger than the
possible effect, ruling out all possibilities of measurable effects
from wood smoke exposure. It is however interesting that
several graphs showed almost no differences between clean
air exposure and low wood smoke exposure and only a
difference between low and high exposure. Generally, it seems
as though the variation is more pronounced for the high
exposure sessions indicating huge differences in the
responses to exposure to high levels of wood smoke. The
observed variation may partly be caused by the investigated
population. Atopy was expected to represent a
homogenous study group, which we suspected to be more
vulnerable and more directly respondent to wood smoke
exposure. However, our findings suggest that atopics
respond very differently to exposure, and therefore may be
considered as a very heterogeneous population, unsuitable
for investigating health effects from short-term exposure.
This is supported by the fact that baseline levels for several
outcomes varied greatly between exposure sessions and
participants. We designed the study carefully to minimize
random variation. We dont believe that diurnal variation
can explain the differences we find between exposure
sessions since these were conducted at the same week days
and in the same time of the day for all participants.
Moreover, we tried to restrict the problematic activities of the
participants during the preceding days in order to minimize
this variation. Still we found variation in the baseline
between days for the individuals, and this attenuates any
responses even though the individual baseline is the basis
and hence, controlled for in the mixed model analysis.
In this study multiple comparisons were made which
increases the risk of finding false positives. According to
Rothman and Greenland  multiple comparison
adjustment (Bonferoni corrections) is not pertinent to this
type of study. However, we are aware that this requires
more caution in the data interpretation and
conclusiveness of the study. However, we emphasize effects that
might be biologically plausible or can be supported by
similar findings in other studies.
The majority of the included outcomes were not
indicative of inflammation, and where significance was
observed these were marginal. The lack of significant
findings may be explained by some natural human
defense mechanisms that might cope with high wood
smoke exposures for a limited exposure period.
On the other hand, based on the existing scientific
evidence it is recognized that several of the included health
outcomes may be related to wood smoke exposure why
effects like these are biological plausible. In addition,
similar findings of limited signs of airway inflammation
are seen in most of the controlled human experiments.
From our mixed model analyses we can exclude effect of
learning and carry-over between exposure sessions and
exclude potential effects of RH% and CO-pollutants.
Compared to other conducted experimental wood smoke
exposure studies this experiment included the highest
number of participants and used the most optimal
controlled design (randomized, blinded, balanced
crossover). Our own estimate suggest the dose administered
to our participants was significantly lower than dose
encountered by people in the third world on a daily
basis, where an association to clinical inflammation has
been shown. We believe that a true but very mild effect
of the wood smoke exposure occurred in this study,
chance is an alternative explanation, and therefore
interpretation should be made with caution.
In conclusion, the results of this study indicate that
wood smoke, at least from the exposure situation under
investigation, do not exert severe acute toxic effects, as
no changes in lung function or nasal patency were
observed. Only very mild inflammatory responses of
inflammatory parameters were seen mainly in the central
airways. Effects during prolonged wood smoke exposure
or with exercise cannot be precluded with our current
This experimental study used a balanced cross-over
design. The exposure sessions were carried out in groups
of 4 participants randomised to the six possible exposure
orders. All participants attended three different exposure
sessions: clean air (<20 g PM/m3) low particle
concentration (~200 g PM/m3) and high particle concentration
(~400 g PM/m3) for 3 hours. Each exposure session
was separated by at least a 2-week period. The study was
blinded and clean air exposures and wood smoke
sessions were identical except for the air quality .
Twenty non-smoking atopic volunteers (10 males, 10
females, mean age 25.1 years) with normal lung function
and no bronchial hyper responsiveness completed the
study. All participants underwent a standard medical
assessment consisting of medical history and clinical
examination. Atopy was determined by skin-prick testing to 10
common aeroallergens. Atopy was defined as a positive
skin-prick tests (the mean of the longest diameter and the
midpoint orthogonal diameter of the weal >3 mm) to one
or more of the 10 common allergens. Bronchial
hyper-responsiveness was measured using the method of Yan et al.
 with De Vilbiss nebulisers connected to a device that
operates on compressed air and produces a pressure pulse
similar to that created by a hand , delivering
cumulative dose of 2.49 mg metacholine bromide. Subjects whose
FEV1 dropped by 20% of baseline FEV1 were considered
as bronchial hyper responsive (BHR). Further exclusion
criteria were a medical history of diseases, which could
involve a risk for the participant or possibly influence the
outcome measurements. Prior to each exposure session
participants had to be free of infections or airway
symptoms for at least 1 week and were not allowed to have
taken any medication or drugs within the last 48 hours
before exposure. Written consent was obtained from all
participants and the study protocol was approved by The
Aarhus County Human Study Review Board in accordance
with the regulations for the protection of the participants
(Ref. no. 20070097).
Exposure facilities and exposure description
This study was conducted at the Section for
Environmental and Occupational Medicine, Aarhus University.
Exposure sessions took place under controlled conditions in a
79 m3 climate chamber optimised for experiments with
gasses and particles as air pollutants. Environmental
conditions (temperature and humidity) were monitored and
kept constant throughout the experiment.
Wood-smoke was generated in a wood-stove facility
using a Mors wood stove (model 7110). Only
beechwood (standardised logs of approx. 1 kg ( 200 g) in mass
with a relative humidity at 16-20%) was used in the stove
and burned at a high temperature in order to achieve
optimal and stable burning conditions. The wood smoke was
aged in a pre-chamber and mixed with filtered outdoor air
to reach the target concentration. After a transport and
aging time of 510 min, the air reached the climate
chamber causing the exposure. Combustion procedures were
the same for all exposure sessions, but during clean air
exposure the wood smoke inlet to the climate chamber was
The exposure atmosphere was characterised by a mean
temperature of 22.94C for clean filtered air, 22.97C for
low PM exposure and 22.92C for high PM exposure. The
mean relative humidity was 22.04%, 33.97% and 32.92% for
clean air, low and high PM exposure, respectively. For CO,
the mean concentration was 0 ppm in the clean air
exposure and 9.85 ppm and 16.05 ppm in the low and the high
PM wood smoke exposures, respectively.
Size-fractioned particle sampling (TSP, PM2.5 and PM10)
was obtained with stationary measurements. The TSP load
was in the range 183263 g particles/m3 for the low
exposure and 215649 g particles/m3 for the high exposure.
The PM2.5 load was 165303 g particles/m3 for the low
exposure and 205662 g particles/m3 for the high
exposure. The range of the PM10 load were for low and
high exposure 165249 g particles/m3 and 213640 g
particles/m3, respectively. In clean air sessions, stationary
PM samplers showed that the mean particle load was most
often below the detection limit (<20 g/m3). The particle
number size distribution for the observed aerosols was
monitored using a Differential Mobility Particle Sizer
(DMPS) operating in a size range with particle diameters
(Dp) of Dp = 10700 nm . During low and high PM
exposures, the particle number size distribution generally
resulted in a bimodal distribution, hinting that emitted
particles from the wood combustion showed two
chemically different fractions. Thus, the particle number in two
size regimes N10-110 (10 nm < Dp < 110 nm) and N110-700
(110 nm < Dp < 700 nm) was determined. In Figure 1, the
number of particles in size regimes with particle diameters
of Dp10nm110nm and of Dp110nm-700nm is presented for the
low, the high and the clean air individual exposure sessions.
Average number concentrations in N10-110 were about
33919 ( 17757) and in N110-700 about 38103 ( 17287)
during high exposure sessions. In contrast, these values
correspond to 13289 ( 5923) for N10-110 and 16397 (
3051) for N110-700 during low exposure sessions. The
fractionation of particles measured for the two size regimes
was quite stable with regard to the different sessions. The
average number N 10110 of particles within individual
sessions varied only between 41 and 53 % and 32 and 57 %
compared to the total number Ntotal of particles during the
high and the low exposure sessions, respectively. Particle
numbers in the respective size regimes were about a factor
of 100 lower during clean air exposure sessions. Except for
one exposure session, the total number of particles was
generally about twice as high during high compared to low
exposure sessions. Particles in the lower size regime N10-110
are assumed to have larger contribution from alkali salts as
those in the larger size regime N110-700 are assumed to have
higher contribution from carbonaceous aerosol. Further
exposure details have been reported elsewhere .
All exposure sessions were conducted at the
approximately same time of day to minimize the influence of
diurnal variation in the outcome measurements. Participants
entered the climate chamber and had a 30-min
acclimatisation period with clean air prior to exposure. Following
acclimatisation, approximately 30 min were used to build up the
exposure, followed by 3 hours of maintained exposure.
Participants were exposed at rest sitting at a desk. After
exposure (3 h) until the late follow-up measurements
(6 h) participants stayed indoors at rest to minimize
competing exposures to influence these measurements. The
climate chamber was thoroughly cleaned before each
exposure session and participants wore clean-suits to avoid
unintended contamination in the chamber.
Clinical measurements and biomarkers
Several measurements were carried out over time to assess
respiratory and inflammatory effects. The outcomes were
spirometry, fractional exhaled NO (FENO), nasal lavage
(NAL), exhaled breath condensate (EBC) and nasal
patency. Prior to each exposure session baseline
measurements were obtained and follow-up measurements were
carried out at selected time points. Time for initiation of
exposure was set to time 0. Follow-up measurements were
performed after 30 min (30 min ~ after building up the
exposure) after additional 3 hours of maintained exposure
(3 hours ~ the end of exposure) and at 6 hours post
exposure initiation (6 hours). All methods are standard
methods used in our previous exposure studies [35,39,47,48].
For all outcomes the participants served as their own
A MicroDL pocket spirometer (Micro Medical Limited,
UK) was used to measure the flow/time profile of a full
forced exhalation after maximal inhalation to obtain the
peak expiratory flow (PEF). Testing was performed in
accordance with the American Thoracic Society guidelines
. Electronically, a curve using the standards supplied
by the Danish Society of Lung Physicians  was
calculated and integrated to give predicted values of Forced
Expiratory Volume in 1 sec (FEV1) and forced vital capacity
Fractional exhaled nitric oxide (FENO) was measured
using a chemiluminescence analyser (NIOX system;
Aerocrine AB, Sweden). The following flow rates were
used to assess different fractions of exhaled NO; 10 ml/s
(FENO10), 50 ml/s (FENO50), 100 ml/s (FENO100), and
270 ml/s (FENO270). During the plateau phase an instant
flow (10%) and a mean flow (5%) of the flow aimed at
was accepted. All measurements were performed in
duplicate according to the 2005 ATS/ERS
recommendations after at least 1 hour of fasting. Prior to the study
we considered calculating alveolar NO according to
Tsoukias et al. . However, only 70 out of our 120
measurements fulfilled the quality criteria of R2 0.7,
which is the reason not to present this data in this study.
Consequently, only changes in FENO50 and FENO270 are
NAL samples were conducted from the participant
sitting with a flexed neck. Through a nasal cork plug
attached to a syringe, 5 ml of 0.9% sterile saline water
(~37C) was injected into the nostril. The saline water
was held in the nasopharyngeal region for 30 sec and
was then collected in a cup. The lavage was then
repeated in the other nostril. For baseline measurements
nasal lavage flush 1 and 2 were discharged in order to
clean the nose from cellular debris and to receive a zero
baseline, and only flush number 3 was analysed. Each
nostril was flushed only once for the follow-up lavages.
Each nasal lavage sample was transferred to a centrifuge
tube, and the amount of fluid was determined by
differential weighing and separated into a pellet and the
supernatant. The samples were kept on ice during processing
and the supernatant was kept frozen until analysis. The
supernatant samples were analysed for analytes
(Interleukin-1 (IL-1), IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-18,
Tumor necrosis factor (TNF)-, Interferon- (IFN-),
granulocyte-macrophage colony stimulating factor
(GMCSF), transforming growth factor-1 (TGF-1), monocyte
chemoattractant protein-1 (MCP-1), Macrophage
inflammatory protein-1 (MIP-1), Regulated upon Activation,
Normal T cell Expressed and Secreted (RANTES)) with
an in-house assay as described by Skogstrand et al. .
In short, 50L sample (undiluted nasal lavage) and 50L
of a suspension of capture-antibody-conjugated beads
were mixed in plate wells. After 1.5 hours of incubation,
the beads were washed twice and subsequently reacted
for 1.5 hours with a mixture (50L) of corresponding
biotinylated detection antibodies, each diluted 1:1000.
50L of streptavidin-phycoerythrin were added to the
wells and the incubation was continued for an additional
30 min. Finally, the beads were washed twice and
re-suspended in 125L of buffer and analysed on the Luminex
100 platform. All samples were measured in duplicate.
Analytes measured in concentrations below their limit of
detection were expressed as of the lowest detection level
in the statistical analyses.
EBC samples were collected with the participants
sitting comfortably and breathing through a frozen cylinder
for 10 min . The participants were instructed to hold
the cylinder upwards like a chimney pot or down to
horizontal and never to hold it down, since saliva then
might spill into the cylinder and contaminate the sample.
The aluminum cylinders were kept in a 20C freezer for
at least 3 hours before the sampling, with the insulation
in place. After the 10 min period, the cylinder was
emptied into a small plastic cup and the condensate was
further transferred to an Eppendorph tube. The tube was
stored in a 80C freezer until the analyses were
performed. EBC samples were analysed for pH, electrical
conductivity and 8-isoprostane. Due to the very limited
condensate volume pH and conductivity were measured
directly in the samples. Conductivity was measured with
a WTW ino Lab Cond 730 (with an electrode WTW
D82362 Weilheim Type LDM/S). For measuring the
pHvalues of the samples a pH meter (WTW pH 330i with a
Hamilton minitrode) was used. The 8-Isoprostane
analysis kit used was 8-isoprostane EIA Kit (catalog no.
516351, Cayman Chemical Company) with the following
limits of detection: 80% B/B0: 2.7 pg/ml and sensitivity:
50% B/B0: 10 pg/ml.
Acoustic rhinometry was used to assess the nasal cross
sectional area and volume . The left and right nasal
cavity were studied alternatively until three reproducible
measurements were obtained. The minimum cross
sectional cavity area was calculated from the means of the
measurements. By integration of the area-distance curve,
the sum of the volume 2 to 4 (vol2-4) from the nostril
was determined on both sides. Acoustic rhinometry was
as a rule performed before nasal lavage to avoid
influences of the nose flushing on the nasal volume.
For the outcomes PEF, FEV1, FVC, FENO50, FENO270,
IL1, IL-6, pH and vol2-4, a mixed model was fitted using
SAS (SAS 9.2, SAS Institute Inc., USA). In the analyses
class variables for exposure status (clean air, low
exposure, high exposure) were used. Exposure, time, the
interaction between exposure and time, learning effect, and
carry-over effect were included as fixed effects. Patient,
the interaction between patient and exposure, and the
interaction between patient and time were included as
random effects. Furthermore, the analyses were
conducted including CO levels (continuous) and RH levels
(continuous) to see whether these variables affected the
results. Due to flooring-problems and a high number of
zeros in the data sets the complex mixed model could
not directly be fitted for IL-8, IL-12, IL-18, conductivity
and vol2-4. By leaving out one of the model parameters
of least importance (the random effect of the interaction
between patient and exposure) the modified model could
be fitted for these outcomes. A significance level of 0.05
was used in all analyses. The mixed models were used to
detect statistical significant effects of exposure over time
for all the included outcomes and to calculate estimates
for the change (difference) from baseline to end of
exposure (3 h) and 6 h, respectively. The time courses of
the health effects are described by mean comparisons
relevant to the time of measurements, including the
standard errors of the means (SEM) to show the
underlying data variation.
The study was financially supported by The Danish Council for Strategic
Research Program Commission on Sustainable Energy and Environment
(Grant no. 2104-05-0045) and by the Danish Heart Association (08-4-R65-A
1999-B662-22436 F). Vibeke H. Gutzke, Tine Bank and Kirsten stergaard are
acknowledged for skilful technical assistance during data acquisition and
ISR has contributed substantially to the completion of the study, acquisition,
analysis and interpretation of the data, and has been the prime mover in
relation to writing the manuscript. JHB has contributed substantially to the
design, the completion of the study, the statistical analyses and been
involved in drafting the manuscript. A-CO provided the equipment for the
FENO measurements and contributed with the interpretation of the data.
TKG contributed substantially with the statistical models and analyses. VS
contributed to the design, the completion of the study and critically revising
the manuscript for important intellectual content. KS and DH have
contributed with the sample analysis of inflammatory markers. AM has
provided equipment for the exposure measurements, contributed to the
execution of the measurements and the analysis and interpretation of the
exposure data. TS contributed substantially to the concept, the design and
the completion of the study, the statistical analyses and critically revising the
manuscript for important intellectual content. All authors have read and
approved the final manuscript.
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