Traffic air pollution effects on lung function: a study in outdoor workers with
personal monitors in São Paulo, Brazil
Authors
Ubiratan Paula Santos¹
Maria Lucia Siqueira Bueno Garcia2
Alfésio Luís Ferreira Braga3,4
Paulo Afonso de André5
Claudia Sayuri Furukawa Oshiro2
Diogo Kogiso2
Beatriz Mangueira Saraiva-Romanholo2
Carmen D. Saldiva de André5,6
Julio M. Singer6
Paulo Hilário Nascimento Saldiva5
Affiliation
¹ MD, PhD, Pulmonary Division of Heart Institute (InCor) do Hospital das clínicas da
Faculdade de Medicina da Universidade de São Paulo, Brazil
2
Laboratory of Experimental Therapeutics, Faculdade de Medicina da Universidade de
São Paulo, Brazil
3
Environmental Epidemiology Study Group, Laboratory of Experimental Air Pollution,
Faculdade de Medicina da Universidade de São Paulo, Brazil
4
Environmental Exposure and Risk Assessment Group, Collective Health Post-
graduation Program, Catholic University of Santos, Brazil
5
Laboratory of Experimental Air Pollution, Department of Pathology, Faculdade de
Medicina da Universidade de São Paulo, Brazil
6
Institute of Mathmatics and Statistics, Universidade de São Paulo, Brazil.
Running title : Traffic Air Pollution and Lung Function
Funding: Tecnology Science Ministry National Council of Tecnological and Scientific
Development,(MCTI-CNPq, Edital 18/2006)
Corresponding Author: Ubiratan de Paula Santos
Divisão de Pneumologia do Instituto do Coração (InCor)-HCFMUSP
Av Dr Eneas de Carvalho Aguiar 44, 5 andar, São Paulo, Brazil
Cep: 05403-000
e-mail: pneubiratan@incor.usp.br
All authors declare there are no competing interests
2
Abstract
Background: chronic effects of outdoor air pollution on lung function in adults are still
controversial, although some studies suggest a significant association. Objective:
evaluate the chronic effect of exposure to different levels of traffic-generated PM2.5 on
the lung function of outdoor workers in São Paulo, Brazil. Methods: in order to explore
the effects of a wider range of exposures, three categories of workers were considered:
taxi drivers and traffic controllers, expected to have a greater exposure, and Forest
Institute rangers with an expected lower level of exposure. One hundred and one
participants fulfilled the inclusion criteria. In an initial evaluation, anthropometric and
clinical variables, asthma symptoms prevalence and the intake of fruits and vegetables
per day were assessed. Participants were then scheduled to attend four visits (once a
week for one month) in which they received a 24-hour personal PM2.5 sampler and have
lung function tests, exhaled nitric oxide and exhaled CO measured in the following day.
The volunteers were classified into three exposure groups according the tertiles of all
individual measurements of PM2.5. The association between the spirometry variables
and exposure was assessed by means of multiple robust regression models adjusted for
age, waist circumference, time in job, daily work hours, diabetes or hypertension,
environmental tobacco smoke, former smoking habits and number of fruit and vegetable
servings per day. Results: workers with the highest level of exposure (> 39.6 g/m3)
presented reductions on predicted FVC (-12.2%), FEV1 (9.1%), and increase in FEF2575/FVC
(14.9%) without changes in FVC/FEV1 when compared to the lowest exposed
group (< 25 g/m3). Conclusions: chronic exposure to vehicular traffic air pollution is
associated with restrictive ventilatory effects.
3
Introduction
Ambient air pollution has been linked to adverse health effects, especially on the
cardiovascular and respiratory systems, with important impact on morbidity and
mortality. A recent report (Lim et al. 2012) estimated that exposure to particulate matter
(PM2.5) led to 3.2 million deaths in 2010. More recently the World Health Organization
(World Health Organization 2014) ascribed 3.7 million deaths (6.7% of the total global
deaths) to environmental pollution in 2012, with 40% attributed to cardiovascular
diseases, 11% to chronic obstructive pulmonary diseases (COPD), 6% to lung cancer,
and 3% to lower respiratory tract infections in children.
In most cities, vehicular emissions represent the main source of atmospheric pollutants.
Studies in different countries have revealed that both short and long-term exposure to
traffic pollution are associated with increased morbidity and mortality from
cardiopulmonary diseases (Brunekreef et al. 2009; Romieu et al. 2012).
Chronic respiratory effects of air pollution have been associated to impairment of lung
development in children and adolescents (Gauderman et al. 2004; Gauderman et al.
2007) and to increased incidence of asthma (McConnell et al. 2006). In adults, several
studies (Andersen et al. 2011, Götschi et al. 2008, Kan et al. 2007) have evidenced lung
function decline associated with chronic exposure, and suggested that the reduction in
air pollution may improve lung function (Boogaard et al. 2013) or attenuate its decline
with age (Downs et al. 2007). However, chronic effects of outdoor air pollution on lung
function in adults is still controversial, although some studies suggest an effect even at
low levels of pollutants (Adam et al. 2015; Schikowski et al. 2014). Acute effects of
ambient levels of traffic pollution were observed in healthy adults (Rice et al. 2013;
4
Zuurbier et al. 2011), but the small number of studies and limitations in their designs
maintains this subject still controversial (Health Effects Institute 2010).
Outdoor workers in urban areas are more exposed to the mix of pollutants generated
from automotive fleet. Indeed, in the urban scenario, there is a gradient of exposure
related to the site where these professionals develop their activities (closer or farther
from high traffic roads). Also, different categories of workers demand different physical
activities and the healthy worker effect cannot be assumed to be present in all of them
(Choudhary and Tarlo 2014).
Our objective is to evaluate the chronic effect of exposure to different levels of trafficgenerated PM2.5 on the lung function of outdoor workers in the city of São Paulo.
Methods
This is an observational study approved by Ethics on Research Committee (CAPPesq)
from Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo,
number 0565/07. All participants signed an informed consent.
Participants
In order to explore the effects of a wider range of exposures, three categories of workers
were selected. Two of them – taxi drivers and traffic controllers – were expected to have
a greater exposure, whereas the third, workers of the Forest Institute rangers, were
presumably submitted to lower levels of exposure, since they spent part of their daily
life in a park in the outskirts of São Paulo. The inclusion criteria were: a) working on
streets and/or avenues (traffic controllers or taxi drivers) or parks (Forest Institute in
São Paulo) for, at least, two years; b) male; c) age between 18 to 65 years and d) nonsmoker or former smoker for over than one year (with air-exhaled carbon monoxide less
than 6 ppm, measured in each of the clinical evaluations).
5
Taxi drivers were informed about the study and invited to a meeting through
advertisements inserted in their union paper. Eighty two workers attended the first
presentation, 64 attended a meeting for clinical evaluation and 57 fulfilled the inclusion
criteria and were admitted to the study.
Three hundred traffic controllers attended 6 meetings in the Traffic Engineering
Company, 149 volunteered, 75 fulfilled the inclusion criteria and 26 remained to the end
of the study. These participants work in the expanded central region of São Paulo.
Forest Institute rangers (working in a park in the outskirts of the city) were recruited
after a project presentation meeting at their institution, responsible for monitoring and
preserving biodiversity in the State of São Paulo. Fifty workers attended the meeting, 23
fulfilled the inclusion criteria and showed up to the clinical evaluation, and 22 remained
in the study.
Study Design
In the period from 2008 to 2012, each participant was followed at specified intervals
within a month, and exposure and effect indicators were measured repeatedly with the
objective of better evaluate the level of exposure and lung function during this short
period.
Participants were submitted to an initial evaluation that included recording of
demographic characteristics, confirmation of the non-smoker condition, occupational,
cardiorespiratory risks and clinical examination. After the initial evaluation, participants
were scheduled, in pairs, to attend four visits (once a week for one month) in the Heart
Institute of Faculdade de Medicina da Universidade de São Paulo. In each visit, they
were submitted to a clinical evaluation to assess the occurrence of complications
between the assessments, to have exhaled CO and exhaled NO measurements as well as
to perform pulmonary function tests.
6
At 7:00 am on the eve of each clinical evaluation, each individual received a portable
lightweight battery operated sampler installed in a shoulder bag to be carried during all
day and kept nearby during sleeping period or baths, in order to record real 24 hours
environmental exposure during. In the following day, before the clinical evaluation and
the pulmonary function test, the monitor was withdrawn.
All participants answered a structured food frequency questionnaire to evaluate the
frequency of usual food consumption (Fisberg et al. 2008). The intake of fruit and
vegetables, sources of antioxidant elements, was recorded as the number of servings per
day.
Anthropometric measures
Weight in light clothes was measured to the nearest 100 g (Tanita scale) and height to
the nearest 0.1 cm was measured barefoot with head in the horizontal Frankfort plane,
using a Seca 208 stadiometer. Waist circumference midway between the lateral lower
ribs and the iliac crest, and hip circumference at the widest part over the greater
trochanters were measured according to standard protocols (World Health Organization
1989).
Respiratory Symptoms and Lung Function
All participants answered a questionnaire on asthma symptoms prevalence in the
preceding 12 months (European Community Respiratory Health Survey 1996; Ribeiro
et al. 2007). Spirometry was performed in the morning, with a KoKo spirometer
(Pulmonary Data Services Instrumentation Inc., Louisville, USA), according to
ATS/ERS recommendations (Miller et al. 2005a; Miller et al. 2005b), during morning
periods. Three manoeuvres were carried out and the best one was chosen and analysed
according to the ATS/ERS criteria (Pellegrino et al. 2005). The predicted values were
7
computed according the reference values recommended for the Brazilian population
(Duarte et al. 2007; Pereira et al. 2007).
Exhaled Nitric Oxide (NOex)
Exhaled nitric oxide (NOex) was collected before each clinical evaluation, according to
ATS standardization. Briefly, exhaled air was trapped in sealed balloons collected by a
Siever Kit (Sievers Instruments Inc., Boulder, CO, USA) with a 0.3 m biologic filter
HEPA (TROX Technik Ltda, SP, Brasil) and measured by chemiluminescence (Sievers
apparatus NOA 280) . Before measurement, the NOex analyzer was tested daily by a
filtering apparatus of ambient air (standardized mixture to transform NO into NO2 in
45ppb, the physiologic pattern, White Martins gases industriais AS, São Paulo SP,
Brasil). The range of NOex among three measurements did not exceed 10% (American
Thoracic Society 1999; American Thoracic Society Workshop 2006).
PM2.5 concentration
The sampler, designed by the Harvard School of Public Health, operates at a flow rate
of 4 lpm through an impaction plate to obtain a PM2.5 cutoff. A silicone catheter
connects the air inlet positioned at the volunteer shoulder to the sampler inflow. A
vacuum pump powered by a rechargeable Li-Ion battery, model WR-5000 Aircheck
from SKC, that incorporates a flow control and a chronometer, provides the necessary
continuous air flow during the 24-hour sampling period. A polycarbonate membrane, a
Whatman filter with a diameter of 37 mm and 0.8 pore size (part number 110809),
installed inside the sampler after the impaction plate retains the particulate matter
sampled. This membrane was weighed before and after the sampling process in an
accurate 1 microgram Mettler Toledo scale, model UMX2, following a laboratorial
protocol developed to control temperature and humidity (Miranda et al. 2012),
estimating the total mass of particulate matter collected. The volume of air sampled is
8
obtained considering the 4 lpm pump flow and the sampling period recorded by the
pump chronometer. The average daily concentration of PM2.5 was obtained dividing
total mass by total air volume sampled.
Statistical Analysis
The tertiles of all individual measurements of PM2.5 were computed and the volunteers
were classified according to them in three exposure groups: low, when all measures
were lower than the first tertile, high, when all measures were higher than the second
tertile and intermediate, otherwise. After defining the exposure groups, the analysis was
based on the averages of the within-individual spirometry variables and baseline
covariates.
The means of the baseline and spirometry variables for the different groups of exposure
were compared by a one-way analysis of variance. When residual diagnostic tools
suggested coarse deviations from the model assumptions, the analysis was
complemented by a Kruskal-Wallis test. To assess the association between passive
smoking, respiratory symptoms, hypertension and/or diabetes and exposure to air
pollution the likelihood ratio tests were considered. The association between spirometry
variables and exposure, adjusted for age, waist circumference, time in job, daily work
hours, diabetes or hypertension, environmental tobacco smoke, previous smoking habits
and number of fruit and vegetable servings per day was assessed by means of multiple
robust regression models based on MM-estimators (Yohai 1987) . The models were
fitted via the lmrob function in the R software package (R Development Core Team
2013).
9
Results
From the 83 traffic-exposed participants (taxi drivers and traffic controllers) four were
excluded due to inconsistencies in the pulmonary function measurements. All 22 Forest
Institute workers were included in the analyses. Participants who attended only one
evaluation session were excluded resulting in a sample size of 101.
345 measures of PM2.5 and 359 pulmonary function tests were completed during the
study period. Figure 1 presents the distribution of individual PM2.5 measurements.
Particulate matter measurements smaller than 25 g/m3 were observed in the group of
traffic controllers and taxi drivers, while some forest rangers were exposed to
concentrations above this value. The observed values of the PM2.5 tertiles were 25
g/m3 and 39.6 g/m3. The mean concentration of PM2.5 was 17.8 g/m3 (standard
deviation= 3.5 g/m3) in the low exposure group, 36.6 g/m3 (standard deviation= 10.8
g/m3) in the medium exposure group and 67.4 g/m3 (standard deviation= 14.1 g/m3)
in the high exposure group.
Table 1 presents demographic and clinical characterization according to exposure
groups. The three groups of exposure are quite similar in terms of age, waist
circumference and body mass index (BMI). On average, participants in the low
exposure group have worked in the same function for a longer period and have less
work shifts than those in the other groups.
Table 2 presents descriptive statistics for symptoms, exhaled nitric oxide (NOex) and
lung function data. Participants in the high exposure group tend to present smaller FEV1
values and higher FEF25-75 when compared to those in the other groups. The prevalence
of respiratory symptoms was higher in the medium exposure group, although
prevalence of wheezing was similar in the three groups. In general, we observed a
decrease of FVC% and FEV1% from the low exposure to the high exposure group,
10
although without statistical significance. On the other hand, a significant increase of
FEF25-75% without a decrease of FEV1/FVC was observed.
In Table 3 we highlight the adverse effects on spirometry parameters observed in the
PM2.5 high and medium exposure groups when compared to the low exposure group. In
order to verify the robustness of the observed findings, we performed a sensitivity
analysis, progressively adding more explanatory variables to check how the estimates
were affected across different model specifications. Because only 86 participants filled
out the food consumption questionnaire and there is no statistical significant differences
among the mean intake of fruit and vegetables in the three exposure groups, this
variable was not included. The estimates for FVC, FEV1, and FEF25-75% remained
reasonably stable for the different sets of explanatory variables considered. In the high
exposure group smaller FVC and FEV1 values were observed. Also, in the same group,
FEF25-75% values were higher with no statistically significant differences detected for
FEV1/FVC.
Discussion
This study suggests that individuals chronically exposed to ambient levels of traffic
pollution present significant alterations in pulmonary function. Moreover, the results
indicate that exposure to ambient traffic pollution vary markedly across individuals,
and, being more evident in individuals with high exposure. The observed findings were
robust and remained reasonably stable for the different sets of explanatory variables
considered. Reductions in FVC and FEV1 were not accompanied by a reduction in
FEV1/FVC, but an increase in FEF25-75% was observed suggesting a restrictive lung
effect. A similar effect, with reduced FVC and FEV1, but no reduction of FEV1/FVC
has been observed after exposure to ozone probably due to change in airways hysteresis
compared with the parenchymal hysteresis (Kjaergaard et al. 2004).
11
A strength of this study is the more precise characterization of the differences in
personal exposure to ambient pollution obtained via personal monitoring (24 hours
PM2.5). Exposure to PM2.5 measured by personal equipment seems to be more
appropriate to assess daily experience of exposure including work shift, time in
commuting and at home. In fact, our results reinforce the concept that urban dwellers
may experience a considerable variation of exposure, depending on their habits and
work characteristics, since the difference between the means of the PM 2.5 in the high
and low exposure groups is almost 50 µg/m3.
More than 75% of the participants (classified in the medium level) were exposed to a
mean concentration of 36.6 μg/m3, exceeding the recommended daily concentration of
25 μg/m3 suggested by WHO (World Health Organization 2006). Considering such a
scenario the detection of significant differences among the groups is indeed impressive.
Most probably our results underestimate the effects of air pollution on lung function,
since there is not a group without chronic exposure, since all participants live in São
Paulo, a city known to have high levels of air pollution.
Most of the studies that have focused on chronic (Götschi et al. 2008; Sava and Carlsten
2012; Schikowski et al. 2005) and acute (Sava and Carlsten 2012; Strak et al. 2012)
effects of air pollution on pulmonary function suggest a reduction of this outcome and
most of them reported evidences of obstructive pulmonary disorder (Andersen et al.
2011; Schikowski et al. 2005), although this topic is still controversial (Schikowski et
al. 2014). A recently published study (ESCAPE), designed to evaluate the chronic effect
of air pollution on pulmonary function, suggested an association between exposure to
NO2 and PM10 but not to PM2.5, along with reduced FEV1 and FVC, mainly in obese
individuals (Adam et al. 2015).
12
A longitudinal study carried out in Germany with women living near major roads
detected a significant association between exposure to PM10 and reduction of FVC,
FVE1 and of FEV1/FVC (Schikowski et al. 2005). A study carried out with 1391 nonsmokers followed up by 16 years (Abbey et al. 1998) detected a decrease of 7.2% in
FEV1 among those with higher levels of exposure, as well as a significant decrease in
FEV1/FVC. A similar obstructive effect on children exposed to traffic-generated
pollutants was reported in a study realized in Munich (Wjst et al. 1993).
While the effects of exposure to pollutants generated by burned biomass on lung
function are better established (Eisner et al. 2010; Regalado et al. 2006), this does not
seem to occur with the effects of exposure to urban pollutants. A detailed analysis
(Health Effects Institute 2010) involving studies from different continents concludes
that the evidence is still insufficient, even though it points out that chronic exposure
suggests effects on adolescents and young people, similarly to that suggested by others
authors (Kelly and Fussell 2011). A study conducted in the Netherlands, comparing
transportation modes (bus, car, and bicycle), suggests that exposure to PM10 is
associated with the reduction in PEF and increased resistance of airways. An association
between exposure to soot and increase in FEF25-75% has also been reported (Zuurbier et
al. 2011).
In a statement on risk factors for Chronic Obstructive Pulmonary Diseases (Eisner et al.
2010) The American Thoracic Society considers that there are enough evidences to state
that air pollution reduces lung function.
The results of this study are in agreement with results from other studies (Kalappanavar
et al. 2012; Kan et al. 2007) in the sense that air pollution is associated to a reductions
in FVC and FEV1 without a reduction in FEV1/FVC, suggesting lung volume reduction
13
with restrictive ventilatory disorder, although a possible inflammatory effect of ozone
cannot be ruled out (Goodman et al. 2014; Schelegle et al. 2007).
Few studies also showed increased levels of FEF25-75%. Gupta et al. (2011) considered
two groups of 100 non-smoking policemen, one of them consisting of traffic policemen
and the other consisting of policemen who were never worked exposed to vehicular
pollution, and suggested decreases in lung function as well as higher prevalence of
respiratory symptoms in the exposed group. However, they did not use predicted values
to compare the groups and did not show FEF25-75 and FEV1/FVC values, complicating a
comparison with our results. The lower prevalence of asthma-suggestive respiratory
symptoms in the high exposure group in our study is in agreement with the results of
their lung function test that showed a restrictive functional pattern.
We also detected a significant increase in FEF25-75% without a corresponding reduction
in FEV1/FVC suggesting a restrictive disorder with probable increase in the elastic
recoil. This can be attributed to a characteristic of fine particles which tend to deposit in
the small airways causing wall inflammation, thickening, and remodelling, as suggested
by studies using lungs from autopsies in São Paulo, Brazil (Saieg et al. 2011) and in
Fresno County, California, USA (Pinkerton et al. 2000).
A study carried out in India with 300 children living in an industrial area and 300
children living in a green zone estimated a prevalence of restrictive ventilatory disorders
of 20,3% and 6,0%, respectively, supporting the hypothesis that exposure to air
pollution may be associated with lung function impairment (Kalappanavar et al. 2012).
These findings are also consistent with a study involving 230 children in Mexico city
and 19 in the city of Tlaxcala; the authors detected 60% of interstitial markings in
children of Mexico City in contrast with 0% in city of Tlaxcala, where the PM and
ozone levels are lower(Calderon-Garciduenas et al. 2006). An interesting recently
14
published study, comparing pulmonary function of Indian and UK-Indian children
suggests that children living in rural areas in India have lower values of FVC and FEV1,
without reduction of FEV1/FVC, suggesting an association between nutritional and
environmental effects and restrictive disorders, since exposure to biomass in rural areas
of the country is usual (Sonnappa et al. 2015).
In one of the few studies reporting values of FEF25-75% , a restrictive pattern of lung
function disorder very similar to that observed in our study was observed in traffic
controllers in India (Patil et al. 2013). However, in addition to the methodological
problems of the study, this effect is still controversial given that a previous study
conducted in India three years before, showed reductions in FVC and FEV1 among
nonsmoking participants. When the analysis took only smokers of exposed and
unexposed to traffic groups into account smaller values of FEF25-75% and maximum
voluntary ventilation among the traffic exposed participants was observed, suggesting a
mixed ventilatory disorder (Pal et al. 2010). It is already known that part of the people
exposed to tobacco smoke develop interstitial lung disease (Morse and Rosas 2014;
Vassallo and Ryu 2012), also limiting the interpretation of such results.
Waking up with respiratory symptoms was more frequently present in participants
classified in the medium and high exposed groups. Wheezing, asthma, nasal allergy
were rare and similar among participants of the three groups. Low perception of
respiratory symptoms has been far studied in literature, mainly in asthma patients. Lack
of illness severity perception is associated to worse outcome and prognosis.
Furthermore, the need for education and follow up by symptoms and signs monitoring
by peak flow meter and lung function which present more sensibility and specificity
(Kaptein et al. 2008; Koinis-Mitchell et al. 2009). We conjecture that asthma patients
who may present severe clinical signs during exacerbations may have low perception of
15
their symptoms and it is plausible to assume that exposed individuals may also be less
perceptive of respiratory symptoms due to small fluctuations of ambient air pollution.
This hypothesis supports the need of more specific tools to check adverse effects on
respiratory symptoms secondary to air pollutants.
Although NOex is considered as a good and not invasive method to check for
respiratory inflammation (Choi et al. 2006; Ricciardolo et al. 2004), in our study its
levels were similar among groups independently of the pollutant exposure levels. This
may be caused by the sensibility in detecting low levels of respiratory inflammation.
Accordingly, another study conducted by our group suggested that air pollution was
related to respiratory inflammation in street traffic controllers in São Paulo suggesting
that NOex is a marker of airway inflammation and of decrease the pH of the exhaled
condensate breath (Lima et al. 2013). Differently from this study, the authors compared
indoor and outdoor workers measuring the NO in the condensate breath which may
have improved the sensibility of their results.
In conclusion, this study suggests that adult individuals chronically exposed to higher
concentration of PM2.5 vehicular air pollution showed significant reductions in FVC,
FEV1 and PEF, without a concomitant reduction of FEV1/FVC, as well as an increase of
FEF25-75%, suggesting that the exposure to fine particles is associated, predominantly,
with ventilatory restrictive impairment.
16
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23
TABLE 1. Baseline characteristics of participants according to exposure groups (N=
101)
Variables
Age (years)
BMI (kg/m2)
Waist circumference (cm)
Time of work (years)
Work shift (hours)
Exhaled Carbon Monoxide (ppm)
ETS(2) (N (%))
at home (N(%))
at work (N(%))
Hypertension and/or diabetes (N
(%))
Haemoglobin (g/dL)
Haematocrit (%)
White blood cells (N/mm3)
Total Colesterol (mg/dL)
HDL
LDL
Albumin (g/dL)
Fasting blood glucose (mg/dL)
Exposure groups
Medium
Low (N=15)
(N=76)
46.6 (±8.4)
48.4 (±9.0)
26.4 (±3.7)
28.9 (±4.4)
94.9 (±8.8)
102.5 (±12.7)
16.5 (±7.8)
10.3 (±8.2)
9.3 (±2.4)
11.3 (±3.5)
2.5 (±2.9)
2.6 (±3.1)
7 (46.7)
39 (51,3)
1 (6.7)
5 (6.6)
6 (40.0)
41 (54.0)
High (N=10)
49.3 (±12.4)
28.4 (±3.7)
101.3 (±10.4)
11.9 (±11.4)
12.6 (±3.5)
1.5 (±2.0)
4 (40.0)
2 (20.0)
5 (50.0)
p
0.730 (1)
0.117 (1)
0.098 (1)
0.041 (1)
0.040 (1)
0.525 (1)
0.775 (3)
0.430 (4)
0.610 (4)
5 (33.3%)
24 (31.6%)
3 (30.0%)
0.984 (4)
15.3 (±0.9)
44.4 (±2.9)
6217 (±1685)
200.1 (±39.6)
47.4 (±8.3)
130.3 (±37.6)
4.7 (±0.2)
96.0 (±9.7)
15.1 (±0.9)
44.7 (±2.4)
6765 (±1634)
212.1 (±34.2)
46.2 (±12.7)
136.5 (±31.6)
4.7 (±0.2)
101.4 (±23.4)
14.8 (±0.6)
43.9 (±1.3)
7052 (±2539)
201.8 (±34.9)
42.9 (±8.3)
132.8 (±32.2)
4.8 (±0.3)
106.3 (±21.7)
0.463 (1)
0.587 (1)
0.469 (1)
0.384 (1)
0.662 (1)
0.774 (1)
0.623 (1)
0.510 (1)
Intake of fruits and vegetables
(portions)(5)
5.7 (±3.6)
6.3 (±3.1)
5.7 (±3.3)
ANOVA ; (2) Enviromental Tobaco Smoke; (3) Chi-Squared test; (4) LikelihoodRatio test; (5) Low: N=8, Medium: N=68, High: N=8.
Results expressed as mean (± standard deviation) or number (percentage).
24
0.774 (1)
TABLE 2. Prevalence of respiratory symptoms and median (interquartile range) of
exhaled nitric oxide (NOex) and spirometric parameters of participants according to
exposure groups (N=101)
Exposure groups
Medium
Low (N=15)
(N=76)
Respiratory Symptoms
Q1. Wheeze
Q1.1.Wheeze with breathless
Q1.2. Wheeze without a cold
Q2. Waking with tightness in the
chest
Q3. Waking with breathless
Q4. Waking with cough
Q5. Attack of asthma
Q6. Treatment for asthma
Q7. Nasal allergy
Q5 or Q6 - Asthma diagnose
High
(N=10)
p
2 (13.3%)
1 (6.7%)
0 (0.0%)
12 (15.8%)
7 (9.2%)
10 (13.2%)
1 (10.0%) 0.867 (1)
1 (10.0%) 0.940 (1)
1 (10. 0%) 0.146 (1)
0 (0.0%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
2 (13.3%)
0 (0.0%)
12 (15.8%)
11 (14.5%)
17 (22.4%)
3 (4.0%)
2 (2.6%)
21 (27.6%)
5 (6.6%)
4 (40.0%)
0 (0.0%)
0 (0.0%)
1 (10.0%)
0 (0.0%)
3 (30.0%)
1 (10.0%)
NOex
19.5 (15.7)
20.7 (10.5)
22.1 (10.1) 0.845 (3)
Lung function parameters
FVC (%) (2)
FEV1 (%) (2)
FEV1/FVC (%)
94.0 (13.0)
91.5 (11.3)
77.5 (10.9)
0.014 (1)
0.035 (1)
0.005 (1)
0.388 (1)
0.562 (1)
0.445 (1)
0.343 (1)
83.5 (17.2) 0.076 (3)
82.2 (19.2) 0.143 (3)
80.3 (6.8) 0.997 (3)
120.2
(2)
FEF25-75/FVC (%)
102.5 (17.1) 104.8 (18.3)
(22.5)
0.041 (3)
PEF (%) (2)
79.9 (30.0)
74.9 (18.5) 72.6 (26.7) 0.386 (3)
(1) Likelihood- Ratio test; (2) Percentage of predict value; (3) Kruskal-Wallis test.
94.9 (16.4)
91.2 (13.8)
79.6 (7.3)
25
Table 3. Effects of PM2.5 on lung function in different stages of the regression model fitting: high exposure – low exposure and medium exposure – low
exposure
Model 1
Model 2
Model 3
Model 4
Exposure groups
compared
Medium -Low
High - Low
effect
-0.13
-12.96
std error
2.71
3.88
FEV1 (%)(1)
Medium -Low
High - Low
0.94
-10.12
3.35
5.03
0.780
0.047
1.77
-9.36
3.47
4.52
0.611 1.76
0.041 -8.41
3.50
5.04
0.617
0.099
1.16
-9.10
3.33
5.10
0.729
0.078
FEV/FVC (%)
Medium -Low
High - Low
1.51
1.42
2.39
2.51
0.529
0.573
1.76
1.46
3.02
3.09
0.561
0.638
1.86
1.97
3.29
3.28
0.572
0.550
1.96
1.97
3.31
3.35
0.557
0.558
Medium -Low
High - Low
1.74
18.43
3.09
5.78
0.574
0.002
-0.83
15.57
3.66
5.93
0.821 -0.80
0.010 14.99
3.79
5.93
0.833 -0.91
0.013 14.86
3.86
6.08
0.814
0.017
FVC (%)(1)
FEF25-75/FVC
(%)(1)
PEF (%)(1)
p
effect
0.960 1.01
0.001 -11.01
std error
p
effect std error
p
effect std error
p
2.97
0.730 0.81
3.18
0.800 -0.24
3.01
0.938
3.98
0.007 -10.76
4.09
0.010 -12.19
3.98
0.003
Medium -Low
-4.45
5.53
0.423 -1.48
5.77
0.799 -1.76
5.82
0.763 -1.84
5.66
0.746
High - Low
-6.87
7.34
0.351 -3.58
7.43
0.631 -4.34
7.99
0.588 -4.41
7.97
0.581
(1) Percentage of predict value
Predictors included in the models
Model 1: Exposure groups
Model 2: Exposure groups + age + waist circumference + time of work + work shift
Model 3: Exposure groups + age + waist circumference + time of work + work shift + ETS at home + former smoker
Model 4: Exposure groups + age + waist circumference + time of work + work shift + ETS at home + former smoker + DM or HAS
26
Figure 1. Distribution of individual personal PM2.5 measurements during the study period in
the group of traffic controllers and taxi drivers, and in the group of forest workers
27