Dynamic Article LinksC<Journal ofEnvironmentalMonitoringCite this: J. Environ. Monit., 2012, 14, 1824

www.rsc.org/jem PAPER

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online / Journal Homepage / Table of Contents for this issue

Use of real-time sensors to characterise human exposures to combustionrelated pollutants†‡

Juana Maria Delgado-Saborit*

Received 13th December 2011, Accepted 19th March 2012

DOI: 10.1039/c2em10996d

Concentrations of black carbon and nitrogen dioxide have been collected concurrently using

a MicrAeth AE-51 and an Aeroqual GSS NO2 sensor. Forty five sampling events with a duration

spanning between 16 and 22 hours have collected 10 800 5 min data in Birmingham (UK) from July to

October 2011. The high temporal resolution database allowed identification of peak exposures and

which activities contributed the most to these peaks, such as cooking and commuting. Personal

exposure concentrations for non-occupationally exposed subjects ranged between 0.01 and 50 mg m�3

for BC with average values of 1.3 � 2.2 mg m�3 (AM � SD). Nitrogen dioxide exposure concentrations

were in the range <LOD to 800 ppb with average concentrations of 23 � 50 ppb. The correlation

between personal exposures (PEs) and central site (A) concentrations was evaluated, with only NO2

exposures averaged over the sampling event significantly correlating with central site levels. The PE/A

ratio ranged between 1.1 (BC) and 0.2–0.7 (NO2) in the absence of combustion sources to 13 (BC) for

subjects commuting in trains and 2.9 (NO2) for subjects cooking with gas appliances.

1. Introduction

Numerous studies have reported effects of outdoor air pollution

on mortality, hospital admissions for cardiopulmonary disease,

Division of Environmental Health and Risk Management, School ofGeography, Earth and Environmental Sciences, University ofBirmingham, Edgbaston, Birmingham, B15 2TT, UK. E-mail: j.m.delgadosaborit@bham.ac.uk; Fax: +44 (0)121 41 43709; Tel: +44 (0)121 41 45427

† Published as part of a special issue dedicated to Emerging Investigators.

‡ Electronic supplementary information (ESI) available. See DOI:10.1039/c2em10996d

Environmental impact

This study uses a novel set of wearable real-time sensors to chara

pollutants such as black carbon and nitrogen dioxide in a group of

provide insights into which activities and microenvironments contr

Commuting and cooking with gas appliances have been identified

Collection of a larger database to confirm these results is advisable.

that will likely have a greater impact on exposure to pollutants. This

aimed at reducing the exposure to these pollutants, such as building

industrial and domestic kitchens; ventilation in commuter modes,

others. This work also tests a sampling protocol using novel weara

exposure useful to fully characterise patterns of peak and long-term e

air pollution and will promote the refinement and development of m

assessment and modelling) will contribute to reduce the misclassi

exposure and health effects, which eventually will also impact on en

population.

1824 | J. Environ. Monit., 2012, 14, 1824–1837

respiratory symptoms, lung function and changes in cardiac

function.1 Particulate matter (PM) usually that #10 mm and

#2.5 mm in aerodynamic diameter (PM10 and PM2.5) are the

main drivers of the observed health effects for respiratory

outcomes2 and cardiovascular disease3 respectively. Moreover,

combustion-related particles are thought to be more harmful to

health than PM that is not generated by combustion.4 In urban

areas, road traffic is the major source of combustion PM;5 whilst

other sources of combustion particles also include wood and coal

burning, shipping, industrial sources in ambient air;6 and envi-

ronmental tobacco smoke, heating and cooking in indoor

cterise concurrently personal exposures to combustion related

non-occupationally exposed subjects. The results of this work

ibute most to peak exposures of combustion related pollutants.

as the main contributors to peak exposures of NO2 and BC.

Nonetheless, this study points out which could be the activities

information has direct impact on environmental health policies

and construction codes that define the amount of ventilation in

and reduction of emissions from key transport modes, among

ble sensors that can be replicated to collect larger databases of

xposures. This will produce accurate assessments of exposure to

odels for exposure prediction. Both approaches (i.e. exposure

fication error and will help elucidate the relationship between

vironmental health policies to protect the health of the general

This journal is ª The Royal Society of Chemistry 2012

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

environments.7 Ambient air pollution is a complex mixture of

gases and particles with different sizes and chemical composition

reflecting different sources such as combustion-related sources

(e.g. tailpipe emissions), non-combustion traffic sources (e.g.

brake and road wear and tear), natural sources (e.g. crustal

material resuspension) and secondary sources (e.g. photochem-

ical O3 formation).

Recently, several authors have pointed out the benefits of

using additional marker pollutants in conjunction with PM to

understand the effects of air pollution from different sources in

health. Janssen et al. (2011)8 performed a systematic review and

a meta-analysis of the health effects of PM and black carbon

(BC, a marker of combustion PM) measured concurrently. They

estimated that the health effects of a 1 mg m�3 increase in expo-

sure were greater for black carbon than for PM, but the esti-

mated effects of an interquartile range increase were similar. The

authors concluded that black carbon was a valuable additional

air quality indicator to evaluate the adverse health effect of

primary combustion aerosols.8 In another study, Delfino et al.

(2008)9 showed that personal exposures to NO2 and PM2.5 had

independent associations with forced expiratory volume in 1

second (FEV1) suggesting that personal PM2.5 mass represents

different causal components than personal NO2. However, they

also found that both personal NO2 and PM2.5 confounded

associations of FEV1 with ambient NO2, which suggests that in

addition to local sources that affect differently the personal

exposure to PM andNO2, part of the exposure to both pollutants

was attributable to common ambient background sources. This

is consistent with epidemiological studies performed using

central site concentrations as a surrogate of exposure that

suggest that NO2 behaves as an effect indicator of traffic-related

air pollution;9,10 and that the observed health effects related to

NO2 concentrations measured at the central site are related not

to NO2 alone, but also to the presence of other traffic-generated

emissions such as CO, soot, VOCs and PAHs.10 Therefore, to

better understand the relationship between air quality, pollution

sources and health effects, it is advisable to assess the effect of

several pollutants which could act as markers of different sour-

ces, rather than focus efforts on one single pollutant. In this way,

using additional markers of pollution in conjunction with PM

will provide a broader picture of the sources associated with the

health effect of air quality.

Black carbon, also known as elemental carbon (EC) or soot is

produced by incomplete combustion and emitted to the atmo-

sphere as one of the components of airborne particulate matter.11

Black carbon is determined by measuring visible light reflected or

transmitted through a filter; whilst EC is measured using

a thermal–optical method, which involves the sequential vola-

tilization of organic carbon and elemental carbon.11 Black

carbon is of interest as a marker of combustion sources such as

diesel vehicles, wood smoke and cigarettes.12 Due to its absor-

bance properties, BC has been nominated also as a possible

major contributor to climate change via its cooling or heating

effects on the atmosphere.13 BC is an important constituent for

health effects studies due to (a) its submicron size which can be

inhaled deep into the lungs11 and (b) because organic compounds

can coat the BC core, transforming the inert BC into a carrier of

harmful organic compounds.11 Black carbon has been associated

with increased ambulatory blood pressure in a population at

This journal is ª The Royal Society of Chemistry 2012

potential risk of heart attack,14,15 with acute changes in cardiac

outcomes among persons with diabetes, persons who are obese,

and nonsmoking elderly individuals16 and with decreased

cognitive function in older men.17

NO2 is an important ambient air pollutant regulated by

national legislations. It has been identified as a lower respiratory

tract irritant associated with respiratory symptoms.18 The most

important source of NO2 in ambient air arises from oxidation of

emitted NO from combustion mainly from motor engines in

urban areas.19 NO2 has been traditionally considered to be

a marker of traffic related air pollution and the health effects are

attributed to the pollution mixture rather than to NO2 alone.20

However some experimental studies also demonstrate the effects

of NO2 as a single pollutant on pulmonary function and bron-

chial responsiveness.21,22 Epidemiological studies investigating

the associations between exposure to traffic using NO2 as

a marker and respiratory symptoms have produced conflicting

results.23,24 Similarly, studies that have assessed the effect of NO2

from the use of gas appliances upon respiratory health have

produced inconsistent outcomes. There is evidence from some

studies that people living in homes with gas stoves and other

unvented gas appliances experience more respiratory symptoms

than those who do not,25,26 but other studies have found no such

association.27,28

A major methodological problem in elucidating the relation-

ship between exposure to NO2 and respiratory health effects

concerns the misclassification of its exposure,29–31 which can

occur in two ways: (a) erroneously attributing to a single source,

namely traffic or cooking with gas appliances, the contribution of

different sources to NO2 personal exposures; and (b) using

inaccurate methods to assess exposure to NO2 producing

incomplete assessment of exposures. The first type of misclassi-

fication might affect epidemiological studies using NO2 as

a marker of traffic, because personal exposures to NO2 are not

only affected by traffic, but also affected by indoor sources, such

as gas-fire appliances and Environmental Tobacco Smoke (ETS).

This is consistent with recent studies that reported personal

exposures more closely correlated with indoor at home concen-

trations than with outdoor levels.32,33 These indoor sources

generate NO2 levels often many times higher than ambient air

levels34 and therefore hinder the suitability of using NO2 as

a marker of traffic-related pollution. The same type of misclas-

sification error might also affect the results of studies assessing

the influence of cooking with gas appliances, as the variation of

personal exposure to NO2 might not be explained only by vari-

ation in the use of a gas cooker, but also by other activities such

as commuting,35 and outdoor concentrations affecting outdoor

and indoor microenvironments. The second type of misclassifi-

cation error is often seen in epidemiological studies using regu-

latory monitoring stations to characterize exposure, as these

measurements reflect both background regional concentrations

and local sources near the stations, but do not take into account

indoor sources of NO2. The second type of misclassification error

has also been observed in epidemiological studies focusing on the

effect of NO2 from gas-fired appliances on respiratory outcomes,

which have characterized exposures depending upon dichoto-

mous exposure categories (i.e. ‘‘presence’’ or ‘‘absence’’ of the

source); using questionnaires and diaries, that can be inaccurate;

or using personal samplers that report average exposures and can

J. Environ. Monit., 2012, 14, 1824–1837 | 1825

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

underestimate the peak high levels occurring for short periods

(e.g. during cooking).36 Therefore using surrogate measures such

as central site monitors and indoor home concentrations, or

exposure estimates through questionnaires are likely sources of

misclassification of exposure.

To reduce misclassification, several researchers have noted the

need to develop more accurate methods for exposure estima-

tion.37,38 Most personal samplers that are appropriate in large-

scale epidemiological surveys can measure an average air toxic

concentration only over a period of 24 h to several days. Such

samples are time-integrated, which provides important infor-

mation regarding an individual’s average exposure, but present

some drawbacks. First, the temporal variability in exposure

throughout the sampling period is unknown (sampling periods

typically span several hours/days). As a result, acute exposure

events (i.e., concentration peaks) are often attenuated by corre-

sponding periods of low exposure.39 Concentrations of air toxics

well above and poorly correlated with this mean value may be

experienced for short periods during traffic exposures (i.e.

walking on busy streets, in-vehicle exposures) or cooking with

gas appliances. If biological responses may intensify with high

peaks of pollutants that overwhelm certain lung defence mech-

anisms,40,41 then repeated exposure to these peaks could be

important, especially in subjects with compromised health.

Experimental studies with asthmatic subjects estimated that

exposures to 400–500 mg m�3 for 30 minutes produced significant

pulmonary function responses.22 Ng et al. (2001) demonstrated

that acute short term NO2 level (mean value 120 mg m�3) during

10 minutes cooking was significantly associated with an imme-

diate mean fall in the peak expiratory flow rate in adult non-

smokers with mild to severe persistent asthma.41 Hence

measurement of air toxics with high temporal resolution is

advisable to reduce misclassification of exposure within a study

and to add further useful information. The benefit of using real-

time measurement rather than integrated average exposures was

observed by a recent study where the effect of PM2.5 with FEV1

decrement was more strongly associated with 1 h peak personal

exposures than with 24 h average personal exposures.9 This study

emphasizes the suitability of using real-time sensors to better

assess exposures to air pollutants.

In addition, using real time personal sensors also has the

advantage that the spatial variability of exposure is known, so

that particular activities or locations can be directly ascribed to

high exposure events. This information enables us to recognize

and control potentially hazardous exposures. Knowing when and

where exposures occur is crucial for understanding the causality

of exposure-related disease. Hence, the spatiotemporal resolu-

tion of exposures can also inform the design of effective inter-

vention and control techniques. Consequently, there is a need for

alternative, more informative, exposure assessment methodolo-

gies39 such as real time personal sensors.

Moreover, although direct measurement of human exposure

to air pollutants via personal monitoring is the most accurate

exposure assessment method currently available; its wide-scale

application to evaluating exposures at the population level is

prohibitive both in terms of cost and time, and sometimes even

impractical for certain sub-populations.42 To circumvent this,

epidemiological studies considered the ambient concentrations at

a set location as an approximation of the exposure of each

1826 | J. Environ. Monit., 2012, 14, 1824–1837

individual in the population under study.43 However, centrally

located monitors have a tendency to underestimate exposures,44

as central site concentrations reflect both background regional

concentrations and local sources near the stations, but do not

take into account indoor sources or personal activities that might

affect personal exposures.45 To overcome this, modelling

approaches have been developed to predict personal exposures in

the general population, such as land-use regression model-

ling,46,47 Bayesian models,48–50 machine learning techniques

models,51 empirical (i.e. statistical)52,53 and mechanistic models

(i.e. based on time/microenvironment/activity data).52,54–57 Using

personal real-time monitors will provide detailed spatial and

temporal information on personal exposures, which will help to

better understand the determinants of exposure such as activities,

locations visited (i.e. microenvironment) and exposure patterns

for different subpopulations. Such information will be instru-

mental to refine the existing models and to develop new models

able to predict personal exposures more accurately in individuals

and the general population whose exposure has not actually been

measured.

Recent advances in the development of inexpensive, mini-

aturised personal monitors capable of collecting data at second-

to-minute resolution have afforded direct-reading methods to be

used in exposure assessment studies. American research institu-

tions funded by The National Institute of Environmental Health

Sciences (NIEHS)58 have led the way in designing fast response,

reliable and compact size sensors able to assess exposures to

pollutant at the personal level for particulate matter, ultrafine

particles and volatile organic compounds.59 Although these

sensors are not yet commercially available, other sensors with

high time resolution are commercialised for measuring black

carbon,60 gases61 and particulate matter.62

The current study aims to use a set of wearable real-time

sensors to characterise concurrently personal exposures to

combustion related pollutants such as black carbon and nitrogen

dioxide in a group of non-occupationally exposed subjects. The

highly resolved temporal database will be examined to quantify

the magnitude of average and peak exposures to BC and NO2, to

identify activities and microenvironments contributing to peak

exposures, and to assess the degree of agreement between

concentrations measured at the personal level with concentra-

tions measured at the central site.

2. Experimental

2.1 Sampling campaigns

Sixteen subjects were recruited among students and staff of the

university to participate in this study. Subjects were informed of

the aims and methodology of the study, and gave their consent to

join the study. Ethics Committee approval was secured for this

study from the University Research Ethics Committee, Univer-

sity of Birmingham (ERN_10-0657).

Two sampling activities were carried out with the subjects in

order to determine the effect of combustion related activities in

human exposure. Forty five sampling events were collected

during the period 12th of July 2011 to 31st of October 2011. The

first sampling campaign (sampling events ¼ 25) was aimed to

characterise the exposure to BC and NO2 occurring during

This journal is ª The Royal Society of Chemistry 2012

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

normal activities such as commuting and cooking. During this

campaign volunteers were asked to carry on their daily routines

and activities as usual without any constraints. Each sampling

collection started in the afternoon and finished the following

morning, collecting data for 16 to 18 hours at 5 min intervals.

The second sampling campaign (sampling events ¼ 20) was

aimed to characterise the exposure to BC and NO2 in traffic

environments. During this campaign volunteers did not have any

constraints to carry out their normal activity routines during the

afternoon and evening, but during the morning subjects were

asked to walk for 1 hour along trafficked streets and 1 hour along

residential background streets. Each sampling event collected

data for 20–22 hours at 5 min intervals.

2.2 Measurement methodology

2.2.1. Black carbon sensor. Personal exposure to BC

concentrations was measured using a personal aethalometer

MicroAeth Model AE51.60 The MicroAethalometer� performs

a continuous optical absorption measurement. The instrument

pumps air onto a quartz fiber filter strip at 150 mL min�1. It

contains a sensor that measures the attenuation of a beam of

stabilized 880 nm wavelength (IR) LED light source transmitted

through the sample collected on the fibrous filter. The attenuation

is linearly proportional to the amount of BC in the filter deposit.

The increase in optical attenuation from one period to the next is

due to the increment of aerosol black carbon collected from the air

stream onto the filter strip during the period. Dividing this

increment of attenuation by the volume of air sampled during

a specified elapsed timeprovides themeanBCconcentration in the

sampled air stream during a particular period.

The microAeth has a measurement resolution of 0.001 mg m�3

in the range of 0–1000 mg m�3. The sensor is calibrated by the

manufacturers against a chemical analysis of the CO2 produced

by combustion of a sample after extraction and thermal pre-

treatment. This analysis yields a mass of carbon expressed in

micrograms, and provides the basis for the calibration of the

optical absorption measurement of sample ‘blackness’ in terms

of a mass of BC.63 The sensor weights 250 g and the battery runs

for 22–24 hours when logging data at 5 minute intervals.

2.2.2. Nitrogen dioxide sensor. Personal exposure to nitrogen

dioxide was performed using an Aeroqual handheld monitor

Series 505 (ref. 61) with a NO2 NW GSS64 sensor head. The

monitor contains an electrochemical sensor, which consists of

three main components: electrodes, electrolyte, and a membrane.

The NO2 diffuses through the membrane and reacts at the elec-

trolyte–catalyst interface, which creates a current. The instrument

measures the current and translates it into gas concentration.

Since the number of electrons given off is proportional to gas

concentration, sensor output is linear.65

The sensor can operate in the range of 1–200 ppb. The sensor

head is calibrated by the manufacturer. The accuracy is <�1 ppb

in the range 0–100 ppb and is <�10% in the range 100–200 ppb.

The sensor weights 460 g and has a self contained battery that

runs for 7 hours.

2.2.3. Personal exposure protocol. The same BC and NO2

sensors were used by the subjects on successive days, allowing

This journal is ª The Royal Society of Chemistry 2012

only one subject to collect a sample at any given time. The subject

was offered the possibility to carry the NO2 and BC sensors in

either a backpack or a bunbag. The BC sensor inlet was located

at the shoulder of the subject in the backpack, and at the waist in

the bunbag. The inlet of the NO2 sensor was located at the waist

in both configurations. The backpack was generally preferred by

the subjects over the bunbag option.

To extend the battery of the sensors, the subjects were

provided with an AC plug adaptor and were requested to connect

the sensor to the mains whenever they were indoors. Specific

instructions were given to the subjects to keep the sensors at

a maximum distance of 1 metre from the subject while connected

to the power supply in order to ensure that the measurement was

representative of their personal exposure. The subjects generally

reported that the sensors were connected to the power supply

within reaching distance (e.g. on the desk while at the office; next

to the sofa/armchair while at living room; next to the bed while

sleeping).

2.2.4. Ambient concentrations of black carbon and nitrogen

dioxide. Data from Birmingham Tyburn concurrent with the

personal exposure campaign were obtained. The station is

located less than 10 miles away from any of the subjects

participating in the study. The station is part of the Automatic

Urban and Rural Network and it is classified as urban back-

ground. It is located approximately 60 metres north of the closest

road, which has a traffic flow of approximately 31 000 vehicles

per day, and 600 metres to the north of a busy motorway. The

concentrations of nitrogen dioxide were available from the UK

National Air Quality Archive (www.airquality.co.uk), whereas

the ratified data of black carbon for 2011 and 2012 were supplied

by the National Physical Laboratory.

In addition to data for the 2011 sampling campaign in Bir-

mingham Tyburn, data for the year 2010 were obtained from

three Automatic Urban and Rural Network sites (AURN),

namely Birmingham Tyburn, Harwell and London Marylebone.

Harwell is a rural station located in the central southern part of

England, whereas London Marylebone is a kerbside monitoring

station located in a frequently congested street canyon with

elevated traffic flows of over 80 000 vehicles per day on six lanes.

2.3 Subject related information

The atmospheric sampling was backed up with information

regarding the volunteer subject activities. Each subject was

provided with a questionnaire to log details of time, location,

activity and ventilation. Since the sensors measure data on a 5

minute basis, subjects were handed a voice recorder to ease the

process of logging information with detailed temporal resolution.

The subjects were given specific instructions to log accurately the

details related to cooking, such as timing, type of cooking (e.g.

frying, boiling, etc.) and existence of ventilation (e.g. windows

open, exhaust on) during the cooking process.

The researcher checked daily the completeness of the time

activity diary forms. The data stored on the sensor data loggers

were downloaded on reception of the sensors and were checked

against the information provided by the subjects in the forms.

Whenever considered necessary, further information on activities

and timings was gathered from the subjects. The checking and

J. Environ. Monit., 2012, 14, 1824–1837 | 1827

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

validation of the time activity diaries process was completed

within 24–48 h of each sampling event to ensure that subjects

could provide accurate information if needed.

2.4 Quality control–quality assurance

The BC and NO2 sensors are both calibrated by the manufac-

turers. However, in order to validate the manufacturers’ cali-

bration on field conditions, the sensors were run in parallel with

their respective reference methods. The BC microaethalometer

AE-51 sensor was compared with the concentrations provided by

the aethalometer AE-41. The NO2 Aeroqual sensor concentra-

tions were compared with the NO2 concentrations from the

Horiba APNA370 NOx analyser (chemiluminescent method).

Samples were collected with the sensors and reference method

concurrently at 15 min intervals for 7 (NO2) and 4 (BC)

consecutive days in Birmingham Tyburn monitoring station. The

concentrations of nitrogen dioxide with 15 min resolution were

supplied by Birmingham City Council, whilst the 15 min black

carbon data were supplied by the National Physical Laboratory.

Concentrations measured by the sensors were compared with

concentrations measured by the reference methods. Regression

between the sensor and the reference method, bias (i.e. relative

difference of both measurements) and drift (difference of

measurements) over time were calculated.

2.5 Statistical analysis

Data were analysed using SPSS 19 Statistics for Windows (SPSS

Inc, 2010) and Excel 2007 (Microsoft Corporation, 1985–2001).

Personal exposures were tested for normality using the skewness

statistic and distributions were found to be right-skewed, there-

fore the dataset was logged. Statistical differences between two

Fig. 1 Black carbon (mg m�3) concentrations measured at BirminghamTybur

validation campaign of the sensors at field conditions.

Fig. 2 NO2 (mg m�3) concentrations measured at Birmingham Tyburn by

validation campaign of the sensors at field conditions.

1828 | J. Environ. Monit., 2012, 14, 1824–1837

strata were tested in the logged database with a t-test for equality

of means. A 2-way ANOVA in the logged database was used to

test differences among several strata (e.g. different modes of

transport) and a post hoc Tukey test was performed to test which

strata was significantly different from the others. The non-

parametric Spearman coefficient was used to assess correlations

in the logged database.

3. Results and discussion

3.1 Comparison of sensor concentration with reference methods

in field conditions

Samples collected in Birmingham Tyburn monitoring station

concurrently with the sensors and with the reference method are

presented in Fig. 1 for BC and in Fig. 2 for NO2 with 1 h

temporal resolution. In both figures the concentrations measured

with the NO2 and BC sensor follow the same trend as those

measured by the reference method. The BC sensor captures

accurately the temporal variation of BC concentrations, showing

a similar performance to the reference method (Fig. 1). The NO2

concentrations measured by the sensor present more fluctuating

values compared with the reference method, although still

capturing the temporal variation of the pollutant (Fig. 2). This

variability in the performance of the NO2 sensor compared with

the BC sensor is also reflected in the regression lines of the

concentrations measured by the reference method and the sensor

(Fig. 3 for 1 h average and Fig. S1, ESI‡ for 15 min average).

Fig. 3a shows a very good fit between the BC concentrations

measured with the reference method and the sensor (R2 ¼ 0.90),

with a slope similar to the 1 : 1 line. The regression line for NO2

(Fig. 3b) also shows that concentrations measured by both

methods follow a similar trend close to the 1 : 1 line with a

n by the reference method (coarse line) and the sensor (fine line) during the

the reference method (coarse line) and the sensor (fine line) during the

This journal is ª The Royal Society of Chemistry 2012

Fig. 3 Comparison of 1 h averaged black carbon (a) and NO2 (b)

concentrations (mg m�3) measured at Birmingham Tyburn by the refer-

ence method and the sensor during the validation campaign of the sensors

at field conditions.

Table 1 Percentage of time spent in each microenvironment and per-forming routine activities in the complete database (N ¼ 10 800) and the75ile BC database (N ¼ 2656) and 75ile NO2 database (N ¼ 2305)

Completedatabase

75ile BCdatabase

75ile NO2

database

MicroenvironmentHome 69 49 56Other indoors 6 7 5Shops 1 1 1Street 9 24 25Transport 4 8 5Work 12 11 8ActivitiesCommuting 4 10 7Cooking 6 9 15Home activities 65 44 43Other activities 7 4 3Walking 7 21 22Working 11 12 11

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

R2 ¼ 0.63. The fact that the microAeth shows a better fit than the

Aeroqual sensor with their respective reference methods might be

attributed to the fact that the microAeth and the Aethalometer

measure BC following the same physical principle, whilst the

Aeroqual and the Horiba measure NO2 using different analytical

principles, such as electrochemistry (sensor) and chem-

iluminescence (reference method) respectively. The bias for each

method is�10% and�20% for BC andNO2 respectively. No drift

is observed in the validation period (see ESI‡); however a longer

validation period is advisable to include different meteorological

conditions (e.g. temperature and relative humidity), which

potentially could affect the performance of the sensors.

3.2 Study population

Although there was no intended bias on recruitment, most of the

recruited subjects were females (62.5%). All subjects were young

This journal is ª The Royal Society of Chemistry 2012

adults with ages ranging between 20 and 35 years old. The main

occupation of the subjects was postgraduate students or research

staff. The main working environments for all the subjects were

several offices within the University of Birmingham.

Subjects spent on average 87% of their time indoors, with an

average of 68% of this time spent indoors at home, 12% indoors

at work and 7% in other indoor environments (Table 1). The

average time spent outdoors was 9%, whilst subjects on average

spent 4% of their time commuting. From the time spent at home,

subjects were on average performing the following activities:

sleeping (34%), other indoor activities (32%, e.g. relaxing,

entertaining) and cooking (6%). In general, these values are

comparable with data previously reported.45,66,67 However, the

average percentage of time spent at work in this study is lower

than those reported in other studies. This is due to the fact that

data collected in this study in the first sampling campaign tar-

geted daily combustion-related activities, which are mainly per-

formed in the afternoon and evening, such as commuting and

cooking. The sampling time for this campaign was 16 to 18 hours

and inherently excluded the majority of the working time that

generally occurs during the morning and early afternoon.

Similarly, the average percentage of time spent outdoors in this

study is positively skewed to longer than expected, although

within range, compared with previously reported time activity

data (i.e. 2–7% outdoors).68 This can be explained considering

that during the second sampling campaign, subjects were

requested to spend a minimum of 2 hours in outdoor environ-

ments walking along traffic and residential streets.

Therefore, caution should be exercised when comparing the

results of personal exposure in this study with those reported in

other studies for two reasons. Firstly, the concentrations of

personal exposure reported in this study do not generally include

part of the working day whilst previous studies report values of

personal exposure over full periods of 24 h (e.g. 24 h BC69 or 7

day70 averages for BC). Secondly, all the subjects participating in

this study are young adults working in an office environment (i.e.

postgraduate students or research staff), and hence their expo-

sures might not be representative of the exposures for those

whose activity patterns are significantly different, such as chil-

dren, retired, or non-office workers.

J. Environ. Monit., 2012, 14, 1824–1837 | 1829

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

3.3 Black carbon concentrations

Table 2 presents the arithmetic mean (AM) and standard devia-

tion (SD) [1.3 � 2.2 mg m�3, AM � SD] and range of results of

personal exposuremeasured in the 45 events sampled in this study

(<LOD to 50 mg m�3). The BC concentrations observed in this

study over 16–22 hours (Table 2) were similar to those reported by

Dons et al. (2011)70 in Belgium for 24 h, whilst BC concentrations

reported in China69 are much higher than those measured in this

study with average concentrations of 15 � 7 mg m�3.

The mean exposures of subjects at home (Table 2) were 0.95 �1.5 mg m�3. This concentration is similar to that reported by

Sarnat et al. (2006)71 in US homes and by Dons et al. (2011)70 in

Belgian homes, which range 1.1–1.4 mg m�3. These concentra-

tions are however higher than those reported in other studies

performed in USA,12,72 Canada73 in the last decade, which are in

the range of 0.1 to 0.4 mg m�3, although are considerably lower

than those reported by Behrentz et al. (2005) for children homes

in the Los Angeles area.74

Table 2 shows the concentrations of personal exposures of the

subjects while commuting in various means of transport. The

subjects commuting with train were exposed to the higher

concentrations of black carbon, with average values of 5.6 � 6.6

mg m�3, followed by bus (3.5 � 4.4 mg m�3), walking in the street

(3.2 � 2.8 mg m�3), inside cars (2.8 � 2.7 mg m�3), and riding

a bicycle (1.3 � 1.1 mg m�3). An ANOVA test was performed in

the dataset which contained only personal exposure data

collected when in different transport modes. The post hoc Tukey

test confirmed that the exposures in buses are significantly higher

than those experienced while commuting by car, bicycle and

walking on the streets (p < 0.001).

Subjects participating in the second sampling campaign were

requested to walk for 1 hour along trafficked streets and for an

additional hour in residential streets. The concentration of

personal exposure measured while subjects were walking in

trafficked areas (N ¼ 237) was 4.8 � 3.2 mg m�3 and 2.7 � 2.9 mg

m�3 in low traffic streets (N ¼ 259). We observe that walking for

one hour on busy streets has an overall effect on the average

personal exposure measured over 20–22 h as evidenced by the

Table 2 Black carbon (mg m�3) and NO2 (ppb) concentrations

Exposure N

BC (mg m�3

Range

PersonalPersonal exposure all campaigns 10 800 <LOD to 5Personal exposure campaign A 5940 <LOD to 5Personal exposure campaign B 4860 <LOD to 3Personal exposure cooking gas hob 3584 <LOD to 3Personal exposure cooking electric 7216 <LOD to 5IndoorsOffice 1095 <LOD to 2Home 6291 0.35–50TransportCar 124 0.12–18Bus 76 0.18–25Bike 19 <LOD to 6Train 50 0.04–29StreetOutdoors – street 742 <LOD to 2Outdoors – central site 9599 <LOD to 8

1830 | J. Environ. Monit., 2012, 14, 1824–1837

higher BC concentrations (Table 2) measured on the subjects

taking part on the second sampling campaign (1.8 � 2.7 mg m�3)

in comparison with the subjects participating in the first sampling

campaign (1.1 � 1.9 mg m�3). An independent t-test for equality

of means confirmed (p < 0.001) that the personal exposure of

subjects walking for one hour in busy streets (i.e. second

sampling campaign) was statistically higher than the exposure of

those who were not asked to walk for one hour on busy streets

(i.e. first sampling campaign). An independent t-test for equality

of means was also performed in a smaller dataset which con-

tained only personal exposure data collected when walking in

identified trafficked roadsides or background sites. This analysis

also confirmed that the values reported in traffic roadsides by the

sensor were statistically higher than those reported in the back-

ground sites (p < 0.001).

The concentration of personal exposure to BC of subjects

commuting with trains or walking in the street in this study is two

times higher than those reported by Dons et al. (2011),70 whilst

three times lower than concentrations in Chinese trains.69 The

concentrations of personal exposures of cyclists are similar to

those reported by Dons et al. (2011).70 The concentrations of BC

personal exposures experienced by the subjects of this study in

cars and buses are similar to those reported in cars by Dons et al.

(2011)70 and Lee et al. (2010),75 and to BC concentrations in

buses reported by Dubowsky et al. (2007),76 whilst considerably

lower than those reported in Indian cars,77 London cars and

buses35 and school buses in Los Angeles area.74

The concentrations of black carbon measured in Birmingham

Tyburn Automatic Urban and Rural Network monitoring

station concurrently with subject exposures are also presented in

Table 2. For the whole sampling campaign the average concen-

trations of ambient air measured in the monitoring site were 0.91

� 0.85 mg m�3. This concentration is similar to those reported

elsewhere70,71,73 which range between 0.5 and 2 mg m�3.

3.4 Nitrogen dioxide concentrations

The concentrations of NO2 personal exposure measured with the

Aeroqual sensor are summarised in Table 2. The personal

) NO2 (ppb)

AM (SD) Range AM (SD)

0 1.3 (2.2) <LOD to 800 23 (50)0 1.1 (1.9) <LOD to 800 26 (52)2 1.8 (2.7) <LOD to 530 20 (45)2 1.6 (2.1) <LOD to 800 31 (61)0 1.2 (2.3) <LOD to 680 19 (41)

0 2.9 (2.8) <LOD to 460 14 (35)0.95 (1.51) <LOD to 800 17 (44)

2.8 (2.7) <LOD to 208 40 (51)3.5 (4.4) <LOD to 500 71 (88)

.1 1.3 (1.1) 38–564 125 (121)5.6 (6.6) <LOD to 400 58 (98)

7 3.2 (2.8) <LOD to 321 64 (51).9 0.91 (0.85) <LOD to 133 47 (24)

This journal is ª The Royal Society of Chemistry 2012

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

exposure to NO2 ranges between below the LOD (5 ppb) and 800

ppb, with average exposures of 23 � 50 ppb. The average

concentrations reported in this study are similar to those repor-

ted in previous personal exposures78–81 with an average range of

concentrations between 14 and 40 ppb,80,82,83 whilst lower than

other previous European84–87 or American88 studies, with

concentrations ranging between 50 and 80 ppb.

Table 2 shows the concentrations of personal exposure to NO2

when subjects reported to be indoors at home (17 � 44 ppb) or

workplace (i.e. office, 14 � 35 ppb). The concentrations of

personal exposure of subjects at home are similar to concentra-

tions measured in other homes in Europe78,82,89,90 and Amer-

ica72,91 ranging between 6 and 30 ppb, whilst concentrations

reported in China are generally higher92,93 in the range 75–180

ppb. Most of the previous studies in homes were done over

extended periods of time using passive sampling, hence these

studies report integrated concentrations over longer periods of

sampling time and cannot be directly compared. In the current

study we report peak exposures for NO2 as high as 800 ppb for

a subject cooking with gas appliances (see Fig. 5 and later

discussion below).

Cooking has been related with increased concentrations of

nitrogen dioxide concentrations indoors82,83,89,91,92,94–96 and at the

personal level.81,82,90,97 In agreement with previous studies, this

study reports higher concentrations of NO2 personal exposure in

those subjects that cook with gas appliances (31 � 60 ppb) than

those that use electric hobs (19 � 40 ppb). Since the standard

deviations were large, an independent samples t-test was applied

in the logged database to test whether the results from cooking

with gas vs. electricity could be considered different. The NO2

levels measured at personal exposure of subjects cooking with

gas were statistically higher than those using electric hobs,

confirmed with a p-value < 0.001.

In addition to cooking, transport is also recognised to be

a contributor of human exposures to NO2.98,99 Table 2 presents

personal exposure levels measured by the sensors when the

subjects were exposed to traffic in several transport means. The

highest exposures were measured for cyclists with average values

of 125 � 121 ppb, followed by bus (71 � 88 ppb), walking on the

street (64 � 51 ppb), commuting by trains (58 � 98 ppb), whilst

driving the car showed the lowest NO2 levels (40 � 51 ppb). An

ANOVA test was performed in the dataset which contained only

personal exposure data collected when in different transport

modes. The post hoc Tukey test could not confirm that exposures

on bikes are higher than other transport means, but confirmed

that the exposures in buses are higher than car exposures (p <

0.001). These results are affected by the sample size of each

transport mean. Except for subjects walking in the streets, with

a subset containing more than 700 cases, the other transport

means are subsets containing only between 19 and 124 cases.

Therefore, further research into characterising peak and average

pollutant levels in different transport means will help to validate

the levels measured in the current study.

Analysis of the subset of data collected by subjects walking in

specified traffic or background sites during the second sampling

campaign shows that the average concentration of subjects

walking in traffic roadsides (N ¼ 227) was 80 � 53 ppb and 55 �40 ppb while walking in residential areas (N ¼ 225). A t-statistic

for equality of means was performed confirming that higher

This journal is ª The Royal Society of Chemistry 2012

concentrations were measured by the sensors in traffic roadsides

as compared with exposures measured walking through resi-

dential areas (p < 0.001).

The concentrations in ambient air in the AURN monitoring

site of Birmingham Tyburn for the same period when personal

exposure took place (i.e. July–October 2011) were in the range

of below LOD to 133 ppb (Table 2), with average concentra-

tions of 47 � 24 ppb. These concentrations are similar to values

reported in some locations in Europe,100–102 Los Angeles basin

(USA)74,103 and Chile104 and considerably lower than concen-

trations measured in China,92 and Mexico105 (100–150 ppb).

However, most of the concentrations reported in the literature

are generally lower ranging between 5 and 25 ppb (e.g. ref. 32,78

and 79).

3.5 Peak exposures vs. average exposure

The present study reports BC concentrations ranging between

the LOD and 50 mg m�3 and NO2 concentrations as high as 800

ppb. None of the previous studies reported concentrations as

high as the ones measured in this study, with the exception of

occupational exposure tunnel workers who had the highest BC

average exposures (87 � 2.5 mg m�3). However, all the previous

studies reported BC and NO2 with low temporal resolution, i.e.

the values represented 24 h to 7 days average concentrations. The

highly temporal resolved measurements reported in this study

allow the identification of high level peaks occurring for short

periods (e.g. during cooking or commuting), which were over-

looked by previous studies that only reported average exposures.

Fig. 4 and 5 illustrate two cases of BC and NO2 concentrations

respectively measured in two subjects who were using different

types of cooking appliances and commuting means. In the case of

the subject cooking with a gas hob and commuting by bus,

while the average concentrations for BC and NO2 were 1.5 � 1.7

mg m�3 and 19 � 1.7 ppb respectively, the subject experiences

peaks of 17 and 12 mg m�3 when commuting in the bus and peaks

of 6.6 mg m�3 when cooking. The same subject experiences peaks

of 800 ppb NO2 when cooking using the gas appliance and

peaks of 200 ppb when commuting in the bus. In the case of the

second subject who cooked with an electric hob and commuted

to work with train, petrol car and a bicycle, the average personal

exposure concentrations were 1.0 � 2.4 mg m�3 and 15 � 1.9 ppb

respectively. However, that subject experiences peak concentra-

tions around 700 ppb NO2 and 37 mg m�3 of BC when

commuting in the train, 575 ppb NO2 and 6 mg m�3 of BC when

riding a bicycle in the city centre in heavy traffic (i.e. morning

rush hour) and around 200 ppb NO2 and 18 mg m�3 of BC when

driving a petrol car. The results of BC peak exposures reported in

this study are in agreement with those reported by Dons et al.

(2011),70 who also identified peak exposures in the range of 20–25

mg m�3 when in transport in a car and peak exposures of 32 mg

m�3 when riding a bicycle. The peak exposures of NO2 exceed the

1 h indoor nitrogen dioxide guideline of 200 mg m�3 proposed by

WHO and the more restrictive guideline of 100 mg m�3 suggested

by Kraft et al. (2005) for Germany.10 These high peak exposures

have been associated with a detriment in pulmonary function

responses22 in asthmatic subjects. To avoid the peak exposures

when cooking it is highly advisable to use a fume hood or have

a ventilation source in the kitchen during cooking. Since the

J. Environ. Monit., 2012, 14, 1824–1837 | 1831

Fig. 4 Black carbon (mg m�3) concentrations measured in personal exposure (coarse line) and ambient air (fine line) in this study for subject no. 6

(above), who cooks in a gas hob and commutes by bus; and subject no. 7 (bottom), who cooks with an electric hob and commutes by petrol car, bicycle

and train.

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

number of data points collected while commuting in trains,

bicycles and buses is small, further research into peak exposures

from different transport modes will be advisable to confirm the

peak exposures observed by the subjects in this study.

One should bear in mind that the values of black carbon

measured by the micro-aethalometer during cooking are based

on the assumption that the absorption coefficient of black

carbon from cooking sources is the same as that used for cali-

brating the micro-aethalometer. It is well known that mass

absorption coefficients are dependent on particle size, density

and refractive index.106 Size distribution of particles close to

traffic ranges between 10 and 65 nm,107 which is similar to the

size distribution of particles emitted during cooking, which

ranges between 30 and 50 nm.108 According to Horvath

(1993)106 and considering only the effect of particle size on the

absorption coefficient, a similar coefficient for traffic and

cooking sources should be expected. However, the black carbon

particles emitted during cooking might have different densities

and refractive indexes than the black carbon used to calibrate

the micro-aethalometer. Further research should be performed

to ascertain the robustness of the absorption coefficient used in

the micro-aethalometer calibration to analyse black carbon

from different sources.

1832 | J. Environ. Monit., 2012, 14, 1824–1837

Toassess howpeak exposures affect average personal exposure,

the Spearman correlation coefficient between average exposure

and several measures of peakiness, such as the 75ile, 90ile and

maximal concentration of personal exposure was calculated.

Table 3 presents the Spearman correlation coefficient calculated

on the logged database representing the average, percentiles and

maximal concentration for each sampling event (N¼ 45). Table 3

shows that the measure of peakiness that best correlates with

average exposure is the 75 percentile, which was 1.4 mgm�3 and 38

ppb for BC and NO2 respectively. Therefore, only cases in the

complete dataset (N¼ 10 800)whichwere equal or higher than the

75ile were selected, rendering a smaller database of N ¼ 2656 for

BC and N ¼ 2305 for NO2. The distribution of time spent by the

subjects in microenvironments and doing activities associated

with the 75ile subset was analysed (Table 1). In the case of the BC,

the peak concentrations are mainly associated with home (49%),

street microenvironments (24%) and commuting (8%), whilst the

activities associated with the peak concentrations were home

activities (44%), walking (21%) and commuting (10%). In the case

of NO2, the peak exposures are associated with being at home

(56%) and in the street (25%),whilst the activitiesmore influencing

the peak exposures are home activities (43%) including cooking

(15%), walking (22%) and commuting (11%).

This journal is ª The Royal Society of Chemistry 2012

Fig. 5 Nitrogen dioxide (ppb) concentrations measured personal exposure (coarse line) and ambient air (fine line) in this study for subject no. 6 (above),

who cooks in a gas hob and commutes by bus; and subject no. 7 (bottom), who cooks with electric hob and commutes by petrol car, bicycle and train.

Table 3 Spearman correlation coefficient between arithmetic mean andthe 75ile, 90ile and maxima concentration of personal exposure for eachsampling event (NBC ¼ 45, NNO2

¼ 45) in the logged database

Spearmancoefficient

Sig(2-tailed)

Perc75_log BC vs. mean_log BC 0.936a 0.000Perc90_log BC vs. mean_log BC 0.772a 0.000Max_log BC vs. mean_log BC 0.512a 0.001Perc75_log NO2 vs. mean_log NO2 0.651a 0.000Perc90_log NO2 vs. mean_log NO2 0.447a 0.006Max_log NO2 vs. mean_log NO2 0.496a 0.001

a p-Values < 0.05.

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

3.6 Personal exposure vs. ambient concentrations

Fig. 4 and 5 make clear that the concentrations measured at the

central site might be similar to the baseline of personal exposure,

but that the peak exposures experienced by the subjects in their

daily activities are considerably higher thanthe concentrations

reported in the central site. Still, it is crucial to ascertain the

degree of inaccuracy associated with using central site air quality

data as a surrogate for human exposure as most epidemiological

studies rely on central site data to assess the association between

This journal is ª The Royal Society of Chemistry 2012

exposure to pollutants and health effects. However, central site

measurements reflect both background regional concentrations

and local sources near the stations, but do not take into account

indoor sources. A critical assumption in using fixed site data is

that the variation in time of the concentration measured at the

fixed site reflects the variation in time in the personal exposure of

people living in the area.109 Nonetheless, the variation of

personal exposure to pollutants is influenced both by indoor and

outdoor sources as we observed in Fig. 4 and 5.

The relationship between personal exposures to NO2 and BC

measured with the sensors and the concentrations measured at

the nearest AURN central site (Birmingham Tyburn) has been

calculated as shown in Table 4. The regression between levels of

BC measured in the central site and in personal exposure is not

statistically significant in the highly resolved temporal data (i.e.

1 hour data) nor in the integrated dataset (i.e. averaged value

across each sampling event). For the nitrogen dioxide, although

the regression between personal exposure and the central site is

not significant for 1 hour datasets, it is significant when the

concentrations of exposures are averaged across the event time.

In this case, the regression coefficient increases from 0.05 to 0.15,

the latter with p-value < 0.05. This suggests that for the case of

NO2, the integrated exposure that buffers the effect of peaks

can be correlated with ambient air quality. Hence ambient

J. Environ. Monit., 2012, 14, 1824–1837 | 1833

Table 4 Regression between concentrations of pollutants (BC and NO2) measured in personal exposure and in ambient air in this study and in previousstudies. The regression was performed in the logged database. N ¼ 720 (1 h resolution) and N ¼ 45 (16–22 hours event resolution)

Reference Country Years Comment

Regression

RIntercept Slope

BCThis study 1 h BC 0.102 0.061 0.016

Sampling event BC �0.095 0.219 0.002NO2

This study UK 2011 1 h NO2 20.1 0.044 0.024Sampling event NO2 5.24 0.142 0.151a

Schwartz et al. (2005)115 USA 1999 24 hMonn et al. (1998)90 Switzerland 7 day 13.7 0.449 0.57Piechocki-Minguy et al. (2006)80 France 2001 48 h (no indoor sources) �18.38 0.86 0.61

48 h (major indoor sources) 11.39 0.14 0.01Kousa et al. (2001)102 EU 1996–1997 Helsinki 1.58 0.5 0.6Son et al. (2004)116 Korea 2000 48 h 8.76 0.79 0.89Spengler et al. (1994)103 USA 1987–1988 48 h 15.8 0.68 0.61Quackenboss et al. (1986)95 USA Gas 0.55

Electric 0.47Sarnat et al. (2000)117 USA 1998–1999 12 days 0.07Rojas-Bracho et al. (2002)104 Chile 1998–1999 24 h 13.8 0.33 0.51Zipprich et al. (2002)118 USA 1999 48 h 0.48Kodama et al. (2002)94 Japan 1998–1999 48 h 0.47Sorensen et al. (2005)78 Denmark 1999–2000 48 hSarnat et al. (2005)119 USA 1999–2000 12 days 0.19Delfino et al. (2008)9 USA 2003 24 h 0.43b

Sarnat et al. (2006)79 USA 2001 48 h 0.25 0.37b

Kim et al. (2006)120 Canada 24 h 0.4

a p-Values < 0.1. b p-Values < 0.05.

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

concentrations could potentially be a useful estimator for long-

term average exposures. However, such an approach assumes

a complete loss of information on peak exposures, which might

be important to quantify for several health outcomes.110,111

In addition, it should be considered that the 1 hour regression

coefficient obtained in this study reflects a combination of both

longitudinal correlation (i.e. repeated measurements of 1 hour

personal exposure over time for each participant) and cross-

sectional correlation (i.e. all participants’ 1 h measurement

together). A separate longitudinal correlation analysis of 1 hour

personal exposure versus 1 h ambient site for each individual

subject was also performed. This separate longitudinal analysis

showed statistically significant (p < 0.05) regression coefficients

ranging 0.3 to 0.7 for both BC and NO2 in 30% and 36% of the

population respectively. The remaining 60% (BC) and 54%

(NO2) of the subjects did not present any significant correlation

between 1 h personal and central site concentrations in the

separate longitudinal analysis. These values are consistent with

previously reported longitudinal correlations between personal

exposure to NO2 and concentrations measured at the central

site.9 Longitudinal correlations are generally much higher than

cross-sectional correlations as the longitudinal studies have the

advantage of removing the interpersonal variability in exposures

that can obscure the relationship with outdoor air112 likewise

observed in this study.

In order to quantify the magnitude of misclassification from

using central site concentrations instead of personal exposures,

Table 5 presents the ratios between personal exposure (PE) and

central site (A) concentrations. In the case of BC, in the absence of

combustion sources, such as the case of the workplace, the ratio

PE/A is close to unity. However, when combustion sources are

1834 | J. Environ. Monit., 2012, 14, 1824–1837

present, personal exposure concentrations are generally higher

than those measured at Birmingham Tyburn central site. This is

the case for those subjects commuting by train (PE/A ¼ 13), fol-

lowed by walking in the street (PE/A ¼ 6), commuting by car or

bus (PE/A¼ 4.5) and frying (PE/A¼ 2.4–3.9). In the case of NO2,

in the absence of combustion sources, NO2 are generally lower in

personal exposures than inambient air (PE/A¼ 0.2–0.7).However

traffic exposures increase the PE/A ratio to levels between 1.4 and

1.8 when walking in the street or commuting by train or road

transport, and cooking with gas affects the most the PE/A ratio,

with values of personal exposures 3 times higher than outdoor air.

On the other hand, some of the PE/A ratios calculated in the

current study have a large variability as reflected in the standard

deviation values (Table 5). This is the case of e.g. commuting in

trains, in which PE/A ratio for BC carries the largest variability.

The most probable reason is the smaller number of cases in the

train subset. Characterising accurately the PE/A ratios for several

pollutants could be a potential tool to predict personal exposure

concentrations in large epidemiological studies using information

provided by the subjects in time activity diaries as regards the

microenvironments visited and activities performed along with

specific PE/A ratios. Further research focussing on the quantifi-

cation of peak exposures in different transport modes and key

activities such as cooking will help to reduce the uncertainty of the

PE/A ratios and to develop such a modelling tool.

3.7 Strengths and limitations of sensors for personal exposure

The sensors used in this project are miniaturised versions of

technologies already available and widely used in research for the

determination of black carbon and NO2.

This journal is ª The Royal Society of Chemistry 2012

Table 5 Ratios between personal exposures (PEs) measured doingseveral activities and ambient air (A) for BC and NO2

PE measured while

No. ofcasesPE/A BC

BC PE/Aratio

No. ofcasesPE/A NO2

NO2 PE/Aratio

Gas appliance frying 39 3.9 (4.8) 51 2.9 (3.7)Electric appliancefrying

135 2.4 (3.0) 141 0.7 (0.9)

Working office 709 1.1 (1.8) 1055 0.2 (0.7)Being at home 5887 1.4 (2.8) 6004 0.5 (1.4)Commuting in trains 49 13 (25) 60 1.8 (3.1)Walking 609 5.7 (9.3) 732 1.6 (2.0)Commuting bybus or cars

168 4.5 (5.7) 176 1.4 (2.0)

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

The main limitation of the wearable versions of these tech-

nologies is the reliance on battery power. The MicroAeth AE51

has battery power sufficient to run for 24 hours. On the other

hand, the battery supplied with the NO2 sensors only provides

5–6 hours of power to run the sensors autonomously. In order to

extend the battery capacity of the sensor to run for 24 hours two

approaches can be considered. The first approach consists in

providing an external battery connected to the monitor that will

supply extra power at the expense of increasing the weight that

the subject has to wear when performing the measurement. This

approach was successfully undertaken in the MATCH project to

extend the autonomy of the VOC active measurements from 12

to 24 hours.113 The second approach is based on the fact that the

majority of these sensors can be run off the mains (i.e. AC

powered) in addition to their default DC powered design.

Therefore, the subjects participating in the study are provided

with an AC plug adaptor and are requested to connect the sensor

to the mains whenever they are indoors, but to keep the sensors

close to them to ensure that the measurement is representative of

personal exposure. This approach will ensure that the sampler

runs for the whole sampling period as long as the subject

remembers to plug in the sensor. The downside of this approach

is that it heavily relies on the subject remembering or willing to

connect the sensor to the mains. This was the approach used in

this research, and none of the subjects reported any problem in

connecting the sensors. However, this might be a factor to

consider in epidemiological or panel studies where the subjects

characteristics might be different, or the sampling is performed

over longer periods of time which potentially could lead to an

increased number of cases of subjects forgetting to connect the

sensors to the mains.

The main strength of the wearable sensor technology is the

increased resolution of these instruments, which allows for the

identification of short-term or peak exposures.114 These readings

coupled with additional spatially referenced information, e.g.

GPS data or diary information (this case) reveal the location and

activities most relevant to exposure. This is important as

contaminant sources, strengths and exposures vary throughout

the day as individuals move through different environments.

Accurate assessment of instantaneous peak personal exposure

allows researchers to investigate associations between acute

exposures and health effects. Such information will be very useful

to advance the knowledge of the health effects from acute and

chronic exposures to pollutants and to assist with the

This journal is ª The Royal Society of Chemistry 2012

development of intervention techniques to manage disease risks

through exposure reduction.39

4. Conclusions

A set of real-time sensors have been successfully employed to

collect highly resolved temporal data on human exposures to

combustion related pollutants, such as black carbon and

nitrogen dioxide. Data collected every 5 minutes have allowed

identification of peak exposures and which activities contributed

the most to these peaks, such as cooking and commuting. The

recent technological advances in sensor technology used in this

study have allowed reporting transient peak concentrations as

high as 800 ppb for NO2 and 50 mg m�3 for BC in non-occupa-

tionally exposed subjects for the first time ever to the knowledge

of the author. The high temporal resolution of the sensors opens

new possibilities to research various pollutants simultaneously at

the personal level. Further research in this area should be bene-

ficial to (a) fully characterise peak and long-term exposures to

key pollutants and related health effects; (b) to ascertain the

degree of misclassification associated when using central site

monitors as surrogate measures for personal exposures; (c) to

characterise patterns of exposure to air pollutants with detailed

spatial and temporal resolution valuable in the refinement and

development of models to predict exposures in the general

population.

Acknowledgements

The author wishes to thank the 2011 Circles of Influence project

‘‘Hands-on Environmental Science: Enabling teaching and

student research with air quality monitors’’ for supporting the

purchase of the Aeroqual sensors used in this research. Dr Del-

gado-Saborit wishes to thank all the subjects who participated in

this research. Special thanks go to Ms Barbara Macias from the

University of Birmingham (UK) and Ms Tharshini Nadarajah

from ENSIACET (France) for their collaboration during the

sampling campaign and generation of the database. The author

wishes to thank Mr Shawn Woodcock from Birmingham City

Council for providing support and access to Birmingham Tyburn

monitoring station during the inter-comparison campaign and to

Dr David Batterfield from National Physical Laboratory for

providing the 2011 and 2012 Black Carbon concentrations

measured at Birmingham Tyburn monitoring station.

References

1 J. J. de Hartog, J. G. Ayres, A. Karakatsani, A. Analitis, H. tenBrink, K. Hameri, R. Harrison, K. Katsouyanni, A. Kotronarou,I. Kavouras, C. Meddings, J. Pekkanen and G. Hoek, Occup.Environ. Med., 2010, 67, 2–10.

2 R. W. Atkinson, G. W. Fuller, H. R. Anderson, R. M. Harrison andB. Armstrong, Epidemiology, 2010, 21, 501–511.

3 R. D. Brook, S. Rajagopalan, C. A. Pope, J. R. Brook,A. Bhatnagar, A. V. Diez-Roux, F. Holguin, Y. L. Hong,R. V. Luepker, M. A. Mittleman, A. Peters, D. Siscovick,S. C. Smith, L. Whitsel and J. D. Kaufman, Circulation, 2010, 121,2331–2378.

4 Health Effects of Transport Related Air Pollution, ed. M.Krzyzanowski, B. Kuna-Dibbert and J. Schneider, World HealthOrganization, Copenhagen, 2005.

5 HEI_Air_Toxics_Review_Panel, Health Effects Institute, Boston,MA, 2010.

J. Environ. Monit., 2012, 14, 1824–1837 | 1835

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

6 A. M. Dillner, J. J. Schauer, W. F. Christensen and G. R. Cass,Atmos. Environ., 2005, 39, 1525–1537.

7 S. O. Baek, Y. S. Kim and R. Perry, Atmos. Environ., 1997, 31, 529–544.

8 N. A. H. Janssen, G. Hoek, M. Simic-Lawson, P. Fischer, L. vanBree, H. ten Brink, M. Keuken, R. W. Atkinson, H. R. Anderson,B. Brunekreef and F. R. Cassee, Environ. Health Perspect., 2011,119, 116–119.

9 R. J. Delfino, N. Staimer, T. Tjoa, D. Gillen, M. T. Kleinman,C. Sioutas and D. Cooper, Environ. Health Perspect., 2008, 116,550–558.

10 M. Kraft, T. Eikmann, A. Kappos, N. K€unzli, R. Rapp,K. Schneider, H. Seitz, J.-U. Voss and H. E. Wichmann, Int. J.Hyg. Environ. Health, 2005, 208, 305–318.

11 L. B. LaRosa, T. J. Buckley and L. A. Wallace, J. Air WasteManage. Assoc., 2002, 52, 41–49.

12 L. Wallace, Aerosol Sci. Technol., 2005, 39, 1015–1025.13 C. Venkataraman, G. Habib, A. Eiguren-Fernandez, A. H. Miguel

and S. K. Friedlander, Science, 2005, 307, 1454–1456.14 E. H. Wilker, A. Baccarelli, H. Suh, P. Vokonas, R. O. Wright and

J. Schwartz, Environ. Health Perspect., 2010, 118, 943–948.15 R. J. Delfino, T. Tjoa, D. L. Gillen, N. Staimer, A. Polidori,

M. Arhami, L. Jamner, C. Sioutas and J. Longhurst,Epidemiology, 2010, 21, 396–404.

16 E. S. Baja, J. D. Schwartz, G. A. Wellenius, B. A. Coull,A. Zanobetti, P. S. Vokonas and H. H. Suh, Environ. HealthPerspect., 2010, 118, 840–846.

17 M. C. Power, M. G.Weisskopf, S. E. Alexeeff, B. A. Coull, A. Spiro,III and J. Schwartz, Environ. Health Perspect., 2011, 119, 682–687.

18 WHO, WHO Guidelines forIndoor Air Quality: Selected Pollutants,Copenhagen, 2010.

19 R. W. Williams, R. R. Watts, R. K. Stevens, C. L. Stone andJ. Lewtas, J. Exposure Anal. Environ. Epidemiol., 1999, 9, 158–166.

20 G. Weinmayr, E. Romeo, M. De Sario, S. K. Weiland andF. Forastiere, Environ. Health Perspect., 2010, 118, 449–457.

21 H. Magnussen, D. Nowak and R. Jorres, Pneumologie, 1994, 48, 85–88.

22 V. Strand, M. Svartengren, S. Rak, C. Barck and G. Bylin, Eur.Respir. J., 1998, 12, 6–12.

23 R. McConnell, T. Islam, K. Shankardass, M. Jerrett, F. Lurmann,F. Gilliland, J. Gauderman, E. Avol, N. Kunzli, L. Yao, J. Petersand K. Berhane, Environ. Health Perspect., 2010, 118, 1021–1026.

24 B. Oftedal, W. Nystad, B. Brunekreef and P. Nafstad, Environ.Health Perspect., 2009, 117, 839–844.

25 D. Jarvis, S. Chinn, C. Luczynska and P. Burney, Lancet, 1996, 347,426–431.

26 R. E. Volkmer, R. E. Ruffin, N. R. Wigg and N. Davies, J. Paediatr.Child Health, 1995, 31, 116–120.

27 M. D. Keller, R. R. Lanese, R. I. Mitchell and R. W. Cote, Environ.Res., 1979, 19, 495–503.

28 J. M. Samet, W. E. Lambert, B. J. Skipper, A. H. Cushing,W. C. Hunt, S. A. Young, L. C. McLaren, M. Schwab andJ. D. Spengler, Am. Rev. Respir. Dis., 1993, 148, 1258–1265.

29 J. M. Samet and M. J. Utell, Toxicol. Ind. Health, 1990, 6, 247–262.30 M. H. Garrett, M. A. Hooper, B. M. Hooper and M. J. Abramson,

Am. J. Respir. Crit. Care Med., 1998, 158, 891–895.31 C. E. Kuehni, M. P. F. Strippoli, M. Zwahlen andM. Silverman, Int.

J. Epidemiol., 2006, 35, 779–786.32 H. K. Lai, M. Kendall, H. Ferrier, I. Lindup, S. Alm, O. Hanninen,

M. Jantunen, P. Mathys, R. Colvile, M. R. Ashmore, P. Cullinanand M. J. Nieuwenhuijsen, Atmos. Environ., 2004, 38, 6399–6410.

33 M. Stranger, S. S. Potgieter-Vermaak and R. Van Grieken, Sci.Total Environ., 2009, 407, 1182–1192.

34 T. Kawamoto, K. Matsuno, K. Arashidani, M. Yoshikawa,F. Kayama and Y. Kodama, Arch. Environ. Contam. Toxicol.,1993, 25, 534–538.

35 H. S. Adams, M. J. Nieuwenhuijsen, R. N. Colvile, M. J. Older andM. Kendall, Atmos. Environ., 2002, 36, 5335–5342.

36 J. Ciuk, R. E. Volkmer and J. W. Edwards, Arch. Environ. Health,2001, 56, 433–438.

37 M. Jerrett, K. Shankardas, K. Berhane, W. J. Gauderman,N. Kunzli, E. Avol, F. Gilliland, F. Lurmann, J. N. Molitor,J. T. Molitor, D. C. Thomas, J. Peters and R. McConnell,Environ. Health Perspect., 2008, 116, 1433–1438.

1836 | J. Environ. Monit., 2012, 14, 1824–1837

38 J. A. Sarnat and F. Holguin, Curr. Opin. Pulm. Med., 2007, 13, 63–66.

39 C. Adams, P. Riggs and J. Volckens, J. Environ. Monit., 2009, 11,1331–1339.

40 R. J. Delfino, R. S. Zeiger, J. M. Seltzer, D. H. Street andC. E. McLaren, Environ. Health Perspect., 2002, 110, A607–A617.

41 T. P. Ng, C. S. R. Seet, W. C. Tan and S. C. Foo, Thorax, 2001, 56,596–601.

42 W. Liu, J. J. Zhang, L. R. Korn, L. Zhang, C. P. Weisel, B. Turpin,M. Morandi, T. Stock and S. Colome, Atmos. Environ., 2007, 41,5280–5288.

43 J. Schwartz, Environ. Res., 1994, 64, 36–52.44 P. I. Serrano-Trespalacios, L. Ryan and J. D. Spengler, J. Exposure

Anal. Environ. Epidemiol., 2004, 14, S118–S132.45 J. M. Delgado-Saborit, N. J. Aquilina, C. Meddings, S. Baker and

R. M. Harrison, Sci. Total Environ., 2011, 409, 478–488.46 M. Jerrett, A. Arain, P. Kanaroglou, B. Beckerman, D. Potoglou,

T. Sahsuvaroglu, J. Morrison and C. Giovis, J. Exposure Anal.Environ. Epidemiol., 2005, 15, 185–204.

47 J. G. Su, M. Brauer, B. Ainslie, D. Steyn, T. Larson andM. Buzzelli,Sci. Total Environ., 2008, 390, 520–529.

48 M. Blangiardo, A. Hansell and S. Richardson, Atmos. Environ.,2011, 45, 379–386.

49 S. J. McBride, R. W. Williams and J. Creason, Atmos. Environ.,2007, 41, 6143–6155.

50 G. Shaddick, D. Lee, J. V. Zidek and R. Salway, Ann. Appl. Stat.,2008, 2, 1249–1270.

51 N. J. Aquilina, J. M. Delgado-Saborit, A. P. Gauci, S. Baker,C. Meddings and R. M. Harrison, Environ. Sci. Technol., 2010, 44,9370–9376.

52 J. M. Delgado-Saborit, N. J. Aquilina, C. Meddings, S. Baker andR. M. Harrison, Environ. Health Perspect., 2009, 117, 1571–1579.

53 D. L. Heavner,W. T.Morgan andM.W. Ogden, Environ. Int., 1995,21, 3–21.

54 O. Hanninen, H. Kruize, E. Lebret and M. Jantunen, J. ExposureAnal. Environ. Epidemiol., 2003, 13, 74–85.

55 J. L. Adgate, T. R. Church, A. D. Ryan, G. Ramachandran,A. L. Fredrickson, T. H. Stock, M. T. Morandi and K. Sexton,Environ. Health Perspect., 2004, 112, 1386–1392.

56 R. E. Dodson, E. A. Houseman, J. I. Levy, J. D. Spengler, J. P. Shineand D. H. Bennett, Environ. Sci. Technol., 2007, 41, 8498–8505.

57 P. Perez Ballesta, R. A. Field, R. Fernandez-Patier, D. Galan-Madruga, R. Connolly, A. B. Caracena and E. De Saeger, Atmos.Environ., 2008, 42, 5350–5364.

58 NIEHS, 2005.59 F. Tsow, E. Forzani, A. Rai, R. Wang, R. Tsui, S. Mastroianni,

C. Knobbe, A. J. Gandolfi and N. J. Tao, IEEE Sens. J., 2009, 9,1734–1740.

60 MageeScientific, Magee Scientific, Berkeley, CA, 2009.61 Aeroqual, 2011.62 L. A. Wallace, A. J. Wheeler, J. Kearney, K. Van Ryswyk, H. You,

R. H. Kulka, P. E. Rasmussen, J. R. Brook and X. Xu, J. ExposureSci. Environ. Epidemiol., 2011, 21, 49–64.

63 The Aeathalometer�, A. D. A. Hansen, Magee Scientific Company,Berkeley, 2005.

64 Aeroqual, Aeroqual Sensor Head Specifications, http://www.aeroqual.com/wp-content/uploads/2010/12/Aeroqual-Sensor-Head-Specifications-Oct-2010.pdf.

65 J. Chou, Hazardous Gas Monitors: A Practical Guide to Selection,Operation, and Applications, McGraw-Hill Professional, NewYork, 2000.

66 B. Brunekreef, N. A. H. Janssen, M. Oldenwening, K. Meliesfste,G. Hoek, T. Lanki, K. L. Timonen, M. Vallius, J. Pekkanen andR. Van Grieken, Health Effects Institute, Boston, MA, 2005.

67 P. Koutrakis, H. H. Suh, J. A. Sarnat, K.W. Brown, B. A. Coull andJ. Schwartz, Res. Rep. – Health Eff. Inst., 2005, 1, 14.

68 R. M. Harrison, J. M. Delgado-Saborit, S. J. Baker, N. Aquilina,C. Meddings, S. Harrad, I. Matthews, S. Vardoulakis andR. Anderson, Health Effects Institute, Boston, 2009.

69 X. Du, Q. Kong, W. Ge, S. Zhang and L. Fu, J. Environ. Sci., 2010,22, 1757–1764.

70 E. Dons, L. I. Panis, M. Van Poppel, J. Theunis, H. Willems,R. Torfs and G. Wets, Atmos. Environ., 2011, 45, 3594–3602.

71 S. E. Sarnat, B. A. Coull, P. A. Ruiz, P. Koutrakis and H. H. Suh, J.Air Waste Manage. Assoc., 2006, 56, 186–196.

This journal is ª The Royal Society of Chemistry 2012

Publ

ishe

d on

19

Apr

il 20

12. D

ownl

oade

d by

Uni

vers

iteit

Utr

echt

on

26/1

0/20

14 1

6:49

:32.

View Article Online

72 L. K. Baxter, J. E. Clougherty, C. J. Paciorek, R. J. Wright andJ. I. Levy, Atmos. Environ., 2007, 41, 6561–6571.

73 A. J. Wheeler, L. A. Wallace, J. Kearney, K. Van Ryswyk, H. You,R. Kulka, J. R. Brook and X. Xu, Aerosol Sci. Technol., 2011, 45,1078–1089.

74 E. Behrentz, L. D. Sabin, A. M. Winer, D. R. Fitz, D. V. Pankratz,S. D. Colome and S. A. Fruin, J. Air Waste Manage. Assoc., 2005,55, 1418–1430.

75 K. Lee, H. Sohn andK. Putti, J. AirWasteManage. Assoc., 2010, 60,130–136.

76 S. D. Dubowsky, D. R. Gold, B. A. Coull, J. Schwartz, P. H. Stoneand H. Suh, Epidemiology, 2007, 18, 95–103.

77 J. S. Apte, T. W. Kirchstetter, A. H. Reich, S. J. Deshpande,G. Kaushik, A. Chel, J. D. Marshall and W. W. Nazaroff, Atmos.Environ., 2011, 45, 4470–4480.

78 M. Sorensen, S. Loft, H. V. Andersen, O. R. Nielsen,L. T. Skovgaard, L. E. Knudsen, V. N. B. Ivan and O. Hertel, J.Exposure Anal. Environ. Epidemiol., 2005, 15, 413–422.

79 S. E. Sarnat, B. A. Coull, J. Schwartz, D. R. Gold and H. H. Suh,Environ. Health Perspect., 2006, 114, 649–654.

80 A. Piechocki-Minguy, H. Plaisance, C. Schadkowski, I. Sagnier,J. Y. Saison, J. C. Galloo and R. Guillermo, Sci. Total Environ.,2006, 366, 55–64.

81 M. Kattan, P. J. Gergen, P. Eggleston, C. M. Visness andH. E. Mitchell, J. Allergy Clin. Immunol., 2007, 120, 618–624.

82 M. Stranger, S. S. Potgieter-Vermaak and R. Van Grieken, Environ.Int., 2007, 33, 789–797.

83 O. Garcia-Algar, M. Zapater, C. Figueroa, O. Vall, X. Basagana,J. Sunyer, A. Freixa, X. Guardino and S. Pichini, J. Air WasteManage. Assoc., 2003, 53, 1312–1317.

84 L. Mosqueron, I. Momas and Y. Le Moullec, Occup. Environ. Med.,2002, 59, 550–555.

85 O. O. Hanninen, S. Alm, K. Katsouyanni, N. Kunzli, M. Maroni,M. J. Nieuwenhuijsen, K. Saarela, R. J. Sram, D. Zmirou andM. J. Jantunen, J. Exposure Anal. Environ. Epidemiol., 2004, 14,440–456.

86 G. Gallelli, P. Orlando, F. Perdelli and D. Panatto, Sci. TotalEnviron., 2002, 287, 31–36.

87 C. H. Linaker, D. Coggon, S. T. Holgate, J. Clough, L. Josephs,A. J. Chauhan and H. M. Inskip, Thorax, 2000, 55, 930–933.

88 K. Lee, S. M. Bartell and D. Paek, J. Exposure Anal. Environ.Epidemiol., 2004, 14, 137–143.

89 O. Garcia-Algar, S. Pichini, X. Basagana, C. Puig, O. Vall,M. Torrent, J. Harris, J. Sunyer and P. Cullinan, Indoor Air, 2004,14, 298–304.

90 C. Monn, O. Brandli, C. Schindler, U. Ackermann-Liebrich andP. Leuenberger, Sci. Total Environ., 1998, 215, 243–251.

91 N. L. Gilbert, D. Gauvin, M. Guay, M.-E. Heroux, G. Dupuis,M. Legris, C. C. Chan, R. N. Dietz and B. Levesque, Environ.Res., 2006, 102, 1–8.

92 C. Y. H. Chao and A. Law, Build. Environ., 2000, 35, 545–553.93 C. Y. H. Chao, Build. Environ., 2001, 36, 999–1007.94 Y. Kodama, K. Arashidani, N. Tokui, T. Kawamoto, K. Matsuno,

N. Kunugita and N. Minakawa, Environ. Res., 2002, 89, 236–244.95 J. J. Quackenboss, J. D. Spengler, M. S. Kanarek, R. Letz and

C. P. Duffy, Environ. Sci. Technol., 1986, 20, 775–783.96 K. Belanger, J. F. Gent, E. W. Triche, M. B. Bracken and

B. P. Leaderer, Am. J. Respir. Crit. Care Med., 2006, 173, 297–303.

This journal is ª The Royal Society of Chemistry 2012

97 T. Rotko, A. Kousa, S. Alm and M. Jantunen, J. Exposure Anal.Environ. Epidemiol., 2001, 11, 216–223.

98 S. Van Roosbroeck, G. Hoek, K. Meliefste, N. A. H. Janssen andB. Brunekreef, Environ. Sci. Technol., 2008, 42, 1337–1344.

99 J. H. Vanwijnen, A. P. Verhoeff, H. W. A. Jans andM. Vanbruggen,Int. Arch. Occup. Environ. Health, 1995, 67, 187–193.

100 N. Valero, I. Aguilera, S. Llop, A. Esplugues, A. de Nazelle,F. Ballester and J. Sunyer, Environ. Int., 2009, 35, 1196–1201.

101 M. E. Hazenkamp-Von Arx, T. Gotschi, U. Ackermann-Liebrich,R. Bono, P. Burney, J. Cyrys, D. Jarvis, L. Lillienberg,C. Luczynska, J. A. Maldonado, A. Jaen, R. de Marco, Y. H. Mi,L. Modig, L. Bayer-Oglesby, F. Payo, A. Soon, J. Sunyer,S. Villani, J. Weyler and N. Kunzli, Atmos. Environ., 2004, 38,1943–1953.

102 A. Kousa, C. Monn, T. Rotko, S. Alm, L. Oglesby andM. J. Jantunen, Atmos. Environ., 2001, 35, 3405–3412.

103 J. Spengler, M. Schwab, P. B. Ryan, S. Colome, A. L. Wilson,I. Billick and E. Becker, J. Air Waste Manage. Assoc., 1994, 44,39–47.

104 L. Rojas-Bracho, H. H. Suh, P. Oyola and P. Koutrakis, Sci. TotalEnviron., 2002, 287, 249–264.

105 M. Ramirez-Aguilar, P. Cicero-Fernandez, A. M. Winer, I. Romieu,F. Meneses-Gonzalez and M. Hernandez-Avila, J. Air WasteManage. Assoc., 2002, 52, 50–57.

106 H. Horvath, Atmos. Environ., Part A, 1993, 27, 293–317.107 Y. F. Zhu, W. C. Hinds, S. Kim and C. Sioutas, J. Air Waste

Manage. Assoc., 2002, 52, 1032–1042.108 G. Buonanno, L. Morawska and L. Stabile, Atmos. Environ., 2009,

43, 3235–3242.109 G. Hoek, G. Kos, R. M. Harrison, J. de Hartog, K. Meliefste, H. ten

Brink, K. Katsouyanni, A. Karakatsani, M. Lianou,A. Kotronarou, I. Kavouras, J. Pekkanen, M. Vallius,M. Kulmala, A. Puustinen, S. Thomas, C. Meddings, J. Ayres,J. van Wijnen and K. Hameri, Atmos. Environ., 2008, 42, 156–169.

110 W. Lin,W. Huang, T. Zhu,M. Hu, B. Brunekreef, Y. Zhang, X. Liu,H. Cheng, U. Gehring, C. Li and X. Tang, Environ. Health Perspect.,2011, 119.

111 F. He, M. L. Shaffer, S. Rodriguez-Colon, J. D. Yanosky, E. Bixler,W. E. Cascio and D. Liao, Environ. Health Perspect., 2011, 119.

112 L. Wallace, Aerosol Sci. Technol., 2000, 32, 15–25.113 J. M. Delgado-Saborit, N. Aquilina, C. Meddings, S. Baker and

R. M. Harrison, Environ. Sci. Technol., 2009, 43, 4582–4588.114 B. Chakrabarti, P. M. Fine, R. Delfino and C. Sioutas, Atmos.

Environ., 2004, 38, 3329–3340.115 J. Schwartz, A. Litonjua, H. Suh, M. Verrier, A. Zanobetti,

M. Syring, B. Nearing, R. Verrier, P. Stone, G. MacCallum,F. E. Speizer and D. R. Gold, Thorax, 2005, 60, 455–461.

116 B. Son,W. H. Yang, P. Breysse, T.W. Chung and Y. S. Lee, Environ.Res., 2004, 94, 291–296.

117 J. A. Sarnat, P. Koutrakis and H. H. Suh, J. Air Waste Manage.Assoc., 2000, 50, 1184–1198.

118 J. L. Zipprich, S. A. Harris, J. C. Fox and J. F. Borzelleca, J.Exposure Anal. Environ. Epidemiol., 2002, 12, 273–285.

119 J. A. Sarnat, K. W. Brown, J. Schwartz, B. A. Coull andP. Koutrakis, Epidemiology, 2005, 16, 385–395.

120 D. Kim, A. Sass-Kortsak, J. T. Purdham, R. E. Dales andJ. R. Brook, J. Exposure Sci. Environ. Epidemiol., 2006, 16, 172–183.

J. Environ. Monit., 2012, 14, 1824–1837 | 1837