indoor and outdoor personal exposure to benzene in athens, greece

www.elsevier.com/locate/scitotenv

Science of the Total Environ

Indoor and outdoor personal exposure to benzene

in Athens, Greece

Christos Chatzis, Evangelos C. AlexopoulosT, Athena Linos

Department of Hygiene and Epidemiology, University of Athens Medical School, Athens, Greece

Received 15 December 2004; accepted 24 January 2005

Available online 15 April 2005

Abstract

Objective: To evaluate the exposure of urban inhabitants to atmospheric benzene in Athens, Greece.

Methods: Fifty non-smoker volunteers from selected occupational groups and their homes were monitored by passive air

samplers for six 5-day periods during a year. An activity diary was completed during each sampling period and relevant data

were collected by a questionnaire at the beginning of the study. Additional data on urban levels on benzene were also available.

Results: Average benzene home and personal levels in six monitoring campaigns varied between 6.0–13.4 and 13.1–24.6 lg/m3, respectively. Urban levels varied between 15.4 and 27.9 lg/m3 with an annual mean of 20.4 lg/m3. Wind speed seems to

determine largely home levels and personal exposure. Proximity to busy road holds also an important influence on indoor

benzene levels. Adjusted for seasonal or climate variation, other significant prognostic factors of personal exposure were home

levels, total time spent outdoors and transportation mean. Time spent outdoors explains the strong relationship between

occupation and personal levels of exposure. Wind had similar effect in clearing indoor and urban pollution in Athens; lessen

personal exposure and home levels about 2–2.5 lg/m3 per 1 m/s increase in speed.

Conclusions: Factors related to climate (use of non-absorbent materials for wall and floor covering and frequent ventilation)

might be one explanation for homes’ high clearing rate. Our exposure pattern, which suggests that outdoors work give the

greater contribution to benzene exposure of Athens citizens, is uncommon in northern towns of Europe. Policy makers have to

take in account these differences in establishing guidelines for ambient benzene exposure.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Benzene; Exposure assessment; Linear mixed model analysis; Health policy

0048-9697/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.scitotenv.2005.01.034

T Corresponding author. Current address. P.O. Box 23 V. Mela St.,

GR 15562 Cholargos, Athens, Greece. Fax: +30 210 5573518.

E-mail addresses: [email protected],

[email protected] (E.C. Alexopoulos).

1. Introduction

Benzene has raised the focus of interest because of

its known genotoxic carcinogenicity even at typical

ambient concentrations (World Health Organization,

1993; Crump, 1994; Karacic et al., 1995). The World

Health Organization stated a risk level between 4.4

ment 349 (2005) 72–80

Table 1

Personal and job characteristics among study population (n=50)

Mean SD n %

Age 36.3 10.9

Sex

Men 33 66

Women 17 34

Total time spending outdoors (h)T 27.3 13.8

Transportation mean

By foot 7 14

By vehicle 35 70

Both 8 16

Use of vehicle in work 28 56

Smoker roommate 15 30

T Duration of each measurement period=108 h.

Table 2

Participants’ house characteristics

n %

Type & floor of house

N4th 16 32

2nd–3rd 13 26

ground–1st floor 14 28

detached house 7 14

City area

Center 42 84

Suburbs 8 16

Recent painting 6 12

Heating mode

Fireplace 5 10

Oil oven 2 4

Natural gas oven 4 8

Central heating 39 78

Proximity to benzene station (b50 m) 3 6

Proximity to busy road (b50 m) 16 32

C. Chatzis et al. / Science of the Total Environment 349 (2005) 72–80 73

and 7.5 myeloid leukaemia cases every million people

exposed continuously to 1 lg/m3 (World Health

Organisation Regional Office for Europe, 2000).

According to a European Union directive entered into

force on December 2000, benzene concentrations in

ambient air should not exceed 5 lg/m3 as a running

annual average, with a long term target of less than 1

lg/m3 (OJ, L 313, 2000).

Previous studies of personal exposure have high-

lighted prominent sources of benzene (Wallace,

1989). Outdoor exposures to benzene are primarily

connected with vehicle emissions, including both

exhaust and evaporative losses of benzene. Studies

have shown that in areas of high traffic density

concentrations in vehicles are typically many-fold

higher than are measured at urban background

locations (Gee and Perry, 1994; European Environ-

ment Agency, 2001). Sources of indoor hydrocarbons

are numerous, including building materials, solvents,

adhesives, and environmental tobacco smoke.

Tobacco smoke is the most prominent source of

indoor pollution, with benzene concentrations increas-

ing by up to 50% compared with homes of non-

smokers (Wallace and Pellizzari, 1987). However, due

to low dilution rates and the amount of time spent

indoors, even minor sources can contribute consid-

erably to exposure. Such sources include several

consumer products which when combined together

can account for up to 20% of the exposure of the total

population to benzene (Wallace et al., 1987).

In Greece only very recently existing ambient air

networks have started monitoring concentrations of

aromatic hydrocarbons in the atmosphere and very

few results are officially available. Research studies

in the field are sparse to give a consistent estimation

of environmental levels (Moschonas et al., 2001;

Bakeas and Siskos, 2002; Petrakis et al., 2003). In

most studies, time integrated measurements at fixed

point monitoring stations at urban background

locations are used for monitoring trends in atmos-

pheric concentrations but also to evaluate the general

pollution climate. Research on personal exposures to

pollutants has shown that the measurements at fixed

point monitoring stations at urban background

locations may not well represent the exposures of

individual members of the general population

(Cocheo et al., 2000). A few studies showed a good

correlation between the estimation of personal

exposure directly by attaching personal samplers to

people, or by fixed point monitors in individual

microenvironments combined with activity data

defining the times spent in each of the microenviron-

ments. In any case little comparative work has been

conducted (Leung and Harrison, 1998; Maitre et al.,

2002; Bono et al., 2003).

Athens, the capital of Greece, is the most densely

populated town in country, located in a latitude of

37.99 and longitude 23.75, spread in an area of 284.6

km2 with an approximate population of 4 million.

Emission of benzene has calculated as 2159.3 tonnes

per year which ranked as one of the highest between

European towns (European Environment Agency,

2001).

Table 3

Personal, home and environmental benzene measurement (Ag/m3 ) in six periods

Periods Personal Home Urban

Median 10th percentile 90th percentile Median 10th percentile 90th percentile Median

Benzene 1st Sep 23.2 14.4 37.5 13.4 5.6 23.2 26.0

(Ag/m3) 2nd Dec 20.2 12.8 29.7 11.2 5.4 22.2 20.0

3rd Feb 17.0 9.7 34.3 10.2 6.5 15.6 18.5

4th Apr 14.3 7.7 28.4 9.0 4.0 15.0 16.1

5th Jun 12.4 5.3 20.3 5.4 2.7 10.5 13.3

6th Sep 16.7 8.7 30.3 7.8 4.3 12.5 18.9

C. Chatzis et al. / Science of the Total Environment 349 (2005) 72–8074

In this study we have used samplers to monitor

volunteers, their homes and urban sites for one year.

Volunteers were non smokers and were selected

from five occupational groups (traffic policemen,

bus drivers, postmen, teachers and students). The

first three spent a lot of time in the streets, so it

was expected to be more heavily exposed compared

to teachers and students who spend more time

indoors.

2. Methods

2.1. Sampling

The whole sampling campaign repeated every 2

months and lasted 1 year (September 1997–Septem-

ber 1998). Each monitoring campaign lasted from

Monday morning to Friday evening and was carried

Table 4

Personal, home and environmental benzene measurements (Ag/m3) and cl

Profession Total Periods

l sd 1st Sep 2nd De

l sd l

Benzene

(Ag/m3)

Teachers 13.4 5.7 20.4 6.6 15.7

Bus drivers 24.7 13.0 32.1 9.0 22.7

Postmen 19.5 9.1 25.2 2.6 19.4

Traffic policemen 22.2 9.4 24.9 11.6 21.5

Students 14.0 7.1 21.0 5.1 22.5

All persons 18.9 10.2 24.6 8.2 20.4

Homes 10.2 6.5 13.4 6.5 13.3

Environmental 20.7 9.3 27.9 11.3 21.9

Temperature (8C) 23.2 13.0

Wind speed (m/s) 0.5 0

Humidity (%) 57 70

Sunshine (h) 9.9 4.2

out placing radial path diffusive samplers—radiello

(Fondazione S Maugeri, Pavia, Italy)—in hundred

sites of Athens. At the same time, fifty volunteers

and their homes were monitored for the same

duration. The campaign included 1200 measurements

and losses did not exceed 6% in any sampling

period. Experimental database composed of 569

environmental data, 280 and 286 personal and home

pollution data, respectively.

For urban monitoring one hundred sampling sites

have been chosen, distributed along the knots of a

multi-scale grid drawn over the town map. The mesh

size was approximately 400 m. The sampling sites

have been divided among 15% and 75% of hot spots

and background zones, respectively (i.e. almost 8

km2 including city center and its peripheral zone).

The other 10% was chosen in a periurban area

(northeast) with scarce traffic and many copses. For

six times each sampling site has been uninterruptedly

imate parameters in six periods (l: mean, sd: standard deviation)

c 3rd Feb 4th Apr 5th Jun 6th Sep

sd l sd l sd l sd l sd

3.3 14.3 4.5 10.3 2.6 7.9 2.5 12.8 3.6

4.4 24.8 10.5 25.5 24.4 18.5 10.6 25.3 7.8

7.0 20.4 9.4 22.0 13.3 13.9 7.2 16.4 8.6

7.0 24.3 12.8 21.9 8.9 16.9 5.5 24.1 9.1

6.1 13.1 3.7 11.1 5.2 6.7 3.1 9.6 3.0

6.0 19.3 9.7 18.1 14.5 13.1 7.9 17.8 9.1

10.4 11.3 4.7 9.5 5.4 6.0 3.0 8.0 3.2

8.8 20.3 7.2 17.3 6.4 15.4 7.2 21.3 8.7

12.5 10.4 23.6 21.8

2.4 2.2 4.0 3.7

81 62 54 69

2.7 7.6 10.2 7.2


monitored from Monday morning (6–8 a.m.) to

Friday afternoon (6–8 p.m.). Samplers were on site

placed inside its shelter hung to a lamppost about 3

m high. Personnel employed at the beginning of the

campaign for placing the samplers and at its end for

gathering them has been educated and supervised by

the members of the research project (CC and ECA).

The volunteers were selected based on expected level

of exposure. So people who, due to the duties of

their job, spend a lot of time in the street like bus

drivers, policemen and postmen composed the first

group. The group of the lower expected exposure

comprised teachers and students. The selection of

volunteers was based in two criteria: non-smoking

and job located in the city centre. Ten persons of

each of the following groups, teachers, students, bus-

drivers, postmen and policemen were selected. After

the selection of 50 volunteers, personal exposure was

determined directly over the series of six 108 h (4 1/

2 days) sampling periods. The personal sampler used

was attached to the volunteer’s lapel and during

night was set in bedside table. At the end of each

day in each sampling period, volunteers were

instructed to fill in a diary stating their activities

during the sampling period.

period

4th Apr

3rd Feb

2nd Dec

1st Sep

benzene µ

g/m

3 :

108 h

TW

A

30,0

25,0

20,0

15,0

10,0

5,0

0,0

9,010,2

11,2

13,414,3

17,0

20,2

23,2

Fig. 1. Benzene levels (median valu

Home microenvironments monitored were located

in greater Athens basin. Although all volunteers

worked in city centre, about two thirds of all

volunteers’ houses were located outside city centre,

in less urban regions and a few houses were located in

suburbs of Athens. These areas had variant densely

population and traffic density and therefore, were

typically less congested and ambient air concentra-

tions were expected to be lower.

Sampling was performed by a radial symmetry

passive sampler radiello. The sampler works by the

spontaneous transfer of gaseous molecules through a

diffusive barrier. It is composed of a microporous

cylindrical diffusive body and of an absorbing

cartridge, cylindrical also, placed inside the diffu-

sive body and coaxial with it. Once assembled

radiello is exposed and just the day and time of the

exposure beginning and end are needed to know

(Cocheo et al., 1996). Subjects and their homes

were also equipped with radiello to measure the

time weighted average (TWA) concentrations of

benzene in the breathing zone over an 108 h period.

Samplers were desorbed with carbon disulfide,

shaken for 30 min and analysed by gas chromatog-

raphy coupled with mass spectrometry. The method

6th Sep

5th Jun

town

personal exposure

homes

7,8

5,4

16,7

12,4

es) in six monitoring periods.

Table 5

Univariate associations between personal benzene levels (Ag/m3)

and characteristics of study population (regression coefficient (beta),

CI: confidence interval, RC: reference category)

Beta (95% CI) p

Sex

Woman RC

Man 5.14 (1.90, 8.38) 0.003

Age (year) 0.205 (0.053, 0.357) 0.009

Occupation

Students RC

Teachers �0.88 (�5.10, 3.34) 0.674

Postmen 4.37 (0.17, 8.58) 0.042

Policemen 7.99 (3.56, 12.42) 0.001

Bus drivers 9.74 (5.54, 13.94) b10�3

Time spent outdoors (h) 0.324 (0.234, 0.414) b10�3

Transportation mean

By foot RC

Vehicle 7.13 (2.67, 11.58) 0.003

Combination 3.78 (�1.94, 9.50) 0.189

Using vehicle during work

No RC

Yes 5.11 (1.95, 8.28) 0.002

Proximity to benzene station

No RC

Yes 7.11 (0.33, 13.89) 0.040

Home concentration (Ag/m3) 0.537 (0.355, 0.718) b10�3


allows a detection limit as low as 0.2 lg/m3.

Analytical method has been described in details

elsewhere (Cocheo et al., 1996). The overall

reliability of the sampling device has been judged

as excellent by the European Reference Laboratory

for Air Pollution (ERLAP) of the Joint Research

Centre of Ispra and the sampler has been widely

used (Cocheo et al., 2000; Crebelli et al., 2001;

Kouniali et al., 2003).
Table 6
Univariate associations between home benzene levels (Ag/m3) and

characteristics of homes (regression coefficient (beta), CI: con-

fidence interval, RC: reference category)

Beta (95% CI) p

Proximity to benzene station

No RC

Yes 3.92 (0.42, 7.42) 0.029

Smoker roommate

No RC

Yes 1.60 (�0.34, 3.54) 0.100

Home location

Suburbs RC

Centre 2.69 (0.30, 5.09) 0.029

Proximity to busy road

No RC

Yes 2.59 (0.74, 4.43) 0.007

3. Statistical analysis

Univariate analyses were performed to examine the

covariates age, sex, roommates’ smoking habit, job

title, time spent outdoors, transportation mean, and

house characteristics (floor, location, recent, painting,

heating mode, proximity to petrol station, and

proximity to busy road). We applied linear mixed-

effects regression models to estimate the significant

prognostic factors of benzene exposure levels by

using maximum likelihood and restricted/residual

maximum likelihood methods. Mixed effects models

had the advantage of adjusting for invariant variables

by fixed-effects models and accounting for individual

differences by random-effects models. In our mixed-

effects models, we treated subjects’ personal and

home characteristics as fixed effects and each subject

as a random effect. Measurement period was used to

identify repeated observations. The type III sum of

squares was used to calculate the effects in the

models. The multivariate linear mixed model included

all variables that contributed significantly to the final

model (Wald statistics, criterion of pb0.05). For each

factor the regression coefficient and 95% confidence

interval (95% CI) were calculated. All statistical

analyses were performed with SPSS 11.0 software.

4. Results

In Tables 1 and 2 personal and home characteristics

of study population are presented. Time spent out-

doors differed markedly between occupational groups.

Teachers and students spent less than 15 h in each

sampling period (108 h) as an annual mean while bus

drivers and traffic policemen exceeded 40 h.

The mean and median concentrations of benzene

measured during the six sampling periods are pre-

sented in Tables 3 and 4 and Fig. 1. As shown in these

tables, average personal levels ranged 13.08–24.63

Ag/m3 and the highest values were observed during

the first two periods, i.e. Autumn and Winter, periods

that wind speed did not exceed 0.5 m/s (Table 4).

Home levels varied between 6.03 and 13.35 Ag/m3,

Students

Traffic policemen

Postmen

Bus drivers

Teachers

All volunteers

Urban level

benze

ne

µg

/m3

30,0

25,0

20,0

15,0

10,0

5,0

0,0

town

personal exposure

homes

8,78,78,3

9,6

8,48,7

12,0

19,118,2

22,1

12,2

17,3

18,5

Fig. 2. Benzene annual average (median) levels.


resulting in an annual mean of 10.19 Ag/m3 followed

the same trend. Average urban levels varied between

15.4 and 27.9 Ag/m3 resulting in annual mean of 20.4

Ag/m3 higher than 18.85 Ag/m3 of personal exposure

level. Benzene in city center and its peripheral zone

(90% of environmental sampling sites) presented an

Table 7

Multivariate associations between personal benzene levels (Ag/m3) and c

confidence interval, RC: reference category)

MODEL I beta (95% CI)

Transportation mean

Foot RC

Vehicle 4.28 (0.46, 8.11)

Both 2.92 (�1.81, 7.66)

Time spent outdoors (h) 0.278 (0.185, 0.370)

Benzene home levels (Ag/m3) 0.221 (0.040, 0.403)

Period

September 1st 5.19 (2.17, 8.21)

December 2nd 0.45 (�2.80, 3.69)

February 3rd 0.01 (�3.16, 3.18)

April 4th 0.10 (�4.27,�4.48)

June 5th �4.96 (�7.81,�2.12)

September 6th RC

Wind speed (m/s)

Humidity (%)

Sunshine (h)

annual average concentration of 21.6 Ag/m3 (range

16.2–29.2 Ag/m3 ) that far exceeds the limit of 5 Ag/m3, proposed by the European Union guideline.

The lower concentrations during the summer

period are probably due to the fact that car traffic

was considerably reduced. For all six measurements

haracteristics of study population (regression coefficient (beta), CI:

p MODEL II beta (95% CI) p

RC

0.029 4.30 (0.47, 8.13) 0.029

0.218 2.95 (�1.78, 7.69) 0.213

b10�3 0.276 (0.184, 0.368) b10�3

0.018 0.224 (0.043, 0.405) 0.016

0.001

0.785

0.996

0.962

0.001

�2.40 (�3.11,�1.70) b10�3

0.89 (0.52, 1.26) b10�3

3.13 (1.85, 4.41) b10�3

Table 8

Multivariate associations between home benzene levels (Ag/m3) and characteristics of homes (regression coefficient (beta), CI: confidence

interval, RC: reference category)

MODEL I beta (95% CI) p MODEL II beta (95% CI) p

Proximity to busy road RC 0.003 RC 0.003

2.84 (1.06, 4.62) 2.81 (1.01, 4.62)

Wind speed (m/s) �2.14 (�2.47,-1.81) b10�3

Period:

September 1st 5.44 (3.76, 7.13) b10�3

December 2nd 5.12 (2.15, 8.09) 0.001

February 3rd 3.08 (1.77, 4.39) b10�3

April 4th 1.64 (0.17, 3.11) 0.029

June 5th �2.01 (�2.84,�1.18) b10�3

September 6th RC


the wind speed, humidity, duration of sunshine and

temperature were monitored. The average climate

parameters are presented in Table 4.

In Tables 5 and 6 univariate analyses of important

prognostic factors besides seasonal variation are

presented. Bus-drivers were the most heavily exposed

group followed by traffic policemen while volunteers’

houses showed similar levels irrespective of occupa-

tional group (Fig. 2).

Adjusted for seasonal variation only, proximity to

busy road remained as an important factor of benzene

home level while for personal exposure, large con-

tribution hold, in addition to home levels, the time

spent outdoors and transportation mean. Groups who

spent most of work-time outdoors found to be exposed

to higher benzene levels and this explains the strong

relationship between occupation and personal levels of

exposure. Furthermore this specific factor is respon-

sible for leading to non-significance, the relationship

between occupation and personal exposures in multi-

variate analysis. In Tables 7 and 8 two interchangeable

models are presented with seasonal or climate varia-

tion. Wind speed seems to determine largely home

levels and personal exposure Wind had similar effect

in clearing indoor and urban pollution in Athens;

lessen personal exposure and home levels about 2–2.5

Ag/m3 per 1 m/s increase in speed.

5. Discussion

The measured benzene concentrations in the centre

of the city were generally high. They are higher than

values measured at urban sites in other European cities,

similar with concentrations obtained from measure-

ments in the centre of Athens during 1994 and 1996

and, lower than other urban measurements, especially

from big cities of developing countries (Moschonas et

al., 2001; Bakeas and Siskos, 2002; Petrakis et al.,

2003; Cocheo et al., 2000; Leung and Harrison, 1998;

Riveros-Rosas et al., 1997; Bahrami, 2001).

The average pollution level in houses located

within environmental sampling areas was almost half

(45–75%) of urban pollution level came from the same

area (city center and its peripheral zone). The reason

why indoor pollution is much lower than the outdoor

one, even if it reflects its seasonal trend, might be due

to home factors related to climate (use of non

absorbent materials for wall and floor covering)

(Cocheo et al., 2000). Among home factors, only

proximity to busy road was important factor for indoor

benzene levels. Proximity to gasoline station and home

location exhibited a borderline significance but finally

were excluded in multivariate analysis. Probably home

characteristics like frequent ventilation and use of non-

absorbent materials for wall and floor covering in

Greece might also account for the lack of significant

correlations. Opposed to Athens, in poorly ventilated

buildings indoor emission source strength is consid-

ered a more significant influence on benzene concen-

trations than infiltration of outdoor air (Kim et al.,

2002). The fact that personal exposure is lower than

environmental concentrations of benzene is mainly

due to low indoor pollution. Nevertheless, correlations

between personal and home levels were significant but

generally weak, especially in summer compared to

other seasons for the reason that factors outside houses

hold a predominant role in exposure pattern.


Indoor air pollution and personal exposure were

closely related to measurement period, i.e. seasonal

variation. The nature of the source and meteorology or

the degree of atmospheric dispersion influences

seasonal patterns. The variation between seasons

may be explained in large by differences in climate

parameters. Wind speed seems to determine largely

home levels and personal exposure with comparable

clearing rates. On the other hand, very different rates

of clearing benzene pollution outdoors and indoors

through ventilation, especially in northern towns have

been reported (Cocheo et al., 2000). In our case the

decrease between first and last sampling periods

(Autumn) may also be due to interventions that took

place in transport processes and modernization of bus

fleet. The national inspection programme for the

control of emissions from motor vehicles was initiated

and constantly continued since 1994, consisting of

regular inspections of all private vehicles once per

year and taxis and light trucks twice per year.

Furthermore a project for the full replacement of the

old buses with new ones equipped with anti-pollution

devices has been continued during the study period.

More than 100 buses have been replaced during study

period and in addition to the replacement of buses, the

increase of dedicated bus lanes improved running

conditions. It was anticipated that these measures have

influenced to some extent atmospheric pollution

during the study period especially in city centre.

Adjusted for seasonal or climate variation, signifi-

cant prognostic factors of personal exposure were

home levels and total time spent outdoors. Groups

who spent most of work-time outdoors found to be

exposed to higher benzene levels and this explains the

strong relationship between occupation and personal

levels of exposure. Transportation means was also a

significant prognostic factor of personal benzene

levels. Our results suggest that outdoor works give

the greater contribution to benzene exposure of

Athens citizens.

Our study shows that population exposure

depends mainly on time spent outdoors, indoor

pollution and transportation mean. In northern towns

where an opposite trend in indoor pollution pre-

vailed, outdoors exposure holds a beneficial effect on

personal levels. Our exposure pattern suggests that

strategy of fixed point monitoring is insufficient to

estimate exposure in specific groups of people who

may be subject to higher exposures. Policy makers

have to take into account exposure patterns of

specific groups in establishing guidelines for ambient

benzene exposure.

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