COMMUTER EXPOSURE TO AEROSOL POLLUTION ON PUBLIC TRANSPORT IN SINGAPORE 1

COMMUTER EXPOSURE TO AEROSOL POLLUTION
ON PUBLIC TRANSPORT IN SINGAPORE




TAN SOK HUANG
(B.Soc.Sci. (Hons), NUS)




A THESIS SUBMITTED
FOR THE DEGREE OF MASTER OF
SOCIAL SCIENCES




DEPARTMENT OF GEOGRAPHY
NATIONAL UNIVERSITY OF SINGAPORE
2014

ii


DECLARATION


I hereby declare that this thesis is my original work and it has been written by me in

its entirety. I have duly acknowledged all the sources of information which have been
used in the thesis.

This thesis has also not been submitted for any degree in any university previously.


_________________________
Tan Sok Huang
30 December 2014

iii

Acknowledgements

To my advisors, family and friends,
This thesis would not have been possible without your kind support, guidance and
patience.
Thank you so much for helping me and putting up with me during the past few years.




iv

Table of Contents
Acknowledgements iii
Table of Contents iv
Abstract vi
List of Tables viii
List of Figures xii

List of Abbreviations xv
Chapter 1. Introduction 1
1.1 Human exposure to air pollution 1
1.2 Singapore’s air quality 4
1.3 Objectives 8
1.4 Thesis outline 9
Chapter 2. Literature Review 10
2.1 Estimating exposure 10
2.1.1 Measuring exposure to particle pollution 11
2.2 Particle pollution in the transport microenvironment 16
2.2.1 Transport emissions 17
2.2.2 Spatial and temporal distribution of particles 19
2.3 Personal exposure in the transport microenvironment 23
2.3.1 Summary of past results 26
Chapter 3. Methods 33
3.1 Measurement Area and Study Period 33
3.2 Instrumentation 37
3.3 Sampling design 43
3.3.1 Sampling route 43
3.3.2 Background measurement site 49
3.3.3 Instrument set-up and sampling procedures 50
3.4 Data quality control 53
3.4.1 Pre-sampling procedures 53
3.4.2 Data post-processing 53
Chapter 4. Results 57
4.1 Commuter exposure on door-to-door trips 59
4.1.1 Particulate matter mass concentrations (PM) 61
4.1.2 Particle number concentration (PN) 64
4.1.3 Active surface area (ASA), particle-bound polycyclic aromatic
hydrocarbons (pPAH), pPAH to ASA (PC/DC) ratio, and diameter of average

surface (D
ave,S
) 66
v

4.1.4 Black carbon (BC) 72
4.1.5 Carbon monoxide 75
4.2 Spatial variation within transport modes 76
4.2.1 Bus 78
4.2.2 MRT 87
4.2.3 Taxi 92
4.2.4 Walk 101
4.3 Dosage 105
4.3.1 Ventilation rates 105
4.3.2 Dosage results 107
Chapter 5. Discussion 111
5.1 Comparison across overall trips and background site 111
5.2 Spatial variation of pollutant concentrations 115
5.2.1 Bus-stops and Taxi-stands 118
5.2.2 In-vehicle concentrations 119
5.3 Dosage 122
Chapter 6. Conclusion 126
6.1 Summary of key findings 126
6.2 Final notes and suggestions for future research 129
References 132
Appendix A DustTrak Calibration 141
Appendix B Description of a trip on each transport mode 147
Appendix C Anderson-Darling Test for Normality 149
Appendix D Supplementary results 154

vi

Abstract
Brief periods of exposure to high concentrations of air pollution may have significant
health impacts. In cities, a large proportion of exposure to airborne pollutants, in
particular, particulate matter, is likely experienced during daily commuting trips due
to the proximity to a number of pollution sources (vehicular traffic, industry,
construction sites, etc). A better understanding of the variability in pollutant
concentrations across available transport modes is important for commuters and
authorities. Unfortunately, personal exposure to particle pollution in the transport
microenvironment of Singapore to date has not been well documented.
The present study analyses exposure concentrations of particulate matter
(PM
10,
PM
2.5
), particle number (PN), black carbon (BC), carbon monoxide (CO),
particle-bound polycyclic aromatic hydrocarbons (pPAH), and active surface area
(SA) measured along a selected route in the commercial shopping district of
Singapore. Portable instruments capable of real-time monitoring were used during
door-to-door trips on three different modes of public transport (bus, taxi, MRT) and
walking. Simultaneous measurements of PM (various sizes)
,
PN, and CO were taken
at a local park to capture the background concentrations. In addition to exposure
concentrations, inhaled dose (dosage) was also estimated.
Except for CO, exposure concentrations of all pollutant metrics were highest
during walking, and lowest on MRT trips. Mean PM
2.5

concentrations observed
during bus, MRT, taxi and walking modes were 1.17, 1.09, 1.11 and 1.50 times
higher than at the background site. PN exhibited a similar trend except for the MRT
mode which showed lower average values than at the background site (ratio of 0.6).
In-vehicle concentrations for buses and taxis were also lower than those found in the
literature, which may be attributed to differences in local driving behaviour, fleet
composition and age, and ambient pollution. Such differences also highlight the
vii

importance of monitoring pollutant exposure under local conditions. After taking into
account the effect of inhalation and travel duration in the calculation of dosage,
differences between transport modes increased by a factor of two. Mean dosages of
PM
2.5
and PN on the Walk mode were 2.5 – 5 times higher than that experienced on
the other three transport modes.
viii

List of Tables
Table 1-1: Air quality standards for US-EPA, WHO and NEA and annual air quality
in Singapore in 2012 for the criteria pollutants. Singapore’s air quality in
2013 is provided in parentheses for comparison purposes. 6
Table 2-1: Summary of transport modes and metrics measured for selected studies 25
Table 2-2: Summary of PM
2.5
, UFP and CO results for the studies listed in Table 2-1.
27
Table 2-3: Comparison of PM
2.5
exposure concentrations with inhaled dose from two

of the studies listed in Table 2-1 31
Table 3-1: Mean ambient T and RH for the entire Singapore island and 24 h-averaged
PSI based on PM
10
and PM
2.5
concentrations reported at 16:00 h for the
Central region of Singapore during the entire fieldwork campaign. (Data
from NEA website) 34
Table 3-2: Sensors employed in the present study and their measurement
characteristics. Sources: EcoChem Analytics (2005), Langan Products
(2006), Onset Computer Corporation (2011), Polar Electro Oy (2013),
TSI Incorporated (2007, 2009, 2010) . 39
Table 3-3: Characteristics of the indoor and outdoor spaces associated with each
transport mode. Values of T, RH and time spent in each section are
means of 23 transects. 47
Table 3-4: Total number (N) of trips sampled for each mode of transport and the
number of trips used in the final analysis after discarding measurements
affected by transboundary pollution, precipitation or technical problems.
54
Table 3-5: Fraction of total data used for analysis after quality control including
removal of suspicious data based on field notes and zero values. 55
Table 4-1: Mean (SD) of pollutant metrics from all trips for 4 transport modes and
measured at the background site. (N = 23 for Bus, Taxi, and MRT, N =
22 for Walk) 59
Table 4-2: Transport mode to BG ratios from all trips for pollutant metrics measured
in both environments. 60
Table 4-3: Results from the Kruskal-Wallis test validating that concentrations
measured on each mode of transport were significantly different from
each other and the background site. H = test statistic, df = degrees of

freedom. 60
Table 4-4: PM
1
/PM
2.5
and PM
2.5
/PM
10
ratios for each transport mode and at the
background site averaged across the entire dataset. 63
Table 4-5: Results of multiple-comparisons tests for effect of transport mode on PM
1

concentrations. 64
Table 4-6: Same as Table 4-5 but for PM
2.5
. 64
Table 4-7: Same as Table 4-5 but for PM
10
. 64
Table 4-8: Results of multiple-comparisons tests for effect of mode on PN
concentrations. 66
ix

Table 4-9: Results from multiple-comparisons tests for effect of mode on ASA
concentrations. 67
Table 4-10: Results from multiple-comparisons tests for effect of mode on pPAHs
concentrations. 68
Table 4-11: Mean (SD) PC/DC ratio and D

ave,S
for four transport modes. (N = 23 for
Bus, Taxi, and MRT, N = 22 for Walk) 71
Table 4-12: Results from multiple-comparisons tests for effect of mode on BC
concentrations. 73
Table 4-13: Results of Spearman rank correlation between BC and other metrics on
the four transport modes. 74
Table 4-14: Results from multiple-comparisons test for effect of mode on CO
concentrations 76
Table 4-15: Mean time spent in each section for all measurements presented in
minutes and percentage of overall trip. 77
Table 4-16: Mean (SD) of pollutant metrics for different sections of the Bus mode
journeys. (N = 23) 82
Table 4-17: Mean PM
1
/PM
2.5,
PM
2.5
/PM
10
, PC/DC ratios and D
ave,S
in different
sections of Bus mode journeys. (N = 23) 83
Table 4-18: Results of Spearman rank correlation between BC and other metrics in
the different sections of Bus mode trips. 83
Table 4-19: Results of the Kruskal-Wallis test for effects of the different sections on
pollutant concentrations during Bus mode trips. H = test statistic, df =
degrees of freedom. 83

Table 4-20: Mean (SD) of measured pollutant metrics in different sections of MRT
mode journeys. 90
Table 4-21: Mean PM
1
/PM
2.5
, PM
2.5
/PM
10
, PC/DC ratios and D
ave,S
in different
sections of MRT mode journeys 91
Table 4-22: Results of Spearman rank correlation between BC and other metrics in
the different sections of MRT mode trips. 91
Table 4-23: Results of Kruskal-Wallis test for effect of the different sections on
pollutant concentrations during MRT mode journeys. H = test statistic,
df = degrees of freedom. 91
Table 4-24: Mean (SD) of pollutant metrics for different sections of Taxi mode
journeys. 96
Table 4-25: Mean PM
1
/PM
2.5
, PM
2.5
/PM
10
, PC/DC ratios and D

ave,S
for different
sections of Taxi mode journeys. 96
Table 4-26: Results of Spearman rank correlation between BC and other metrics in
the different sections of Taxi mode trips. 96
Table 4-27: Results of Kruskal-Wallis test for effect of the different sections on
pollutant concentrations during Taxi mode journeys. H = test statistic, df
= degrees of freedom. 97
Table 4-28: List of taxi models sampled. The vehicles’ age was obtained from the
drivers. 100
x

Table 4-29: Maximum, minimum and mean HR and V
E
for different sections of the
four transport modes for all measurements (N = 23 for Bus, MRT, and
Taxi, N = 22 for Walk). 106
Table 4-30: Inhaled dose by mode and section for PM
2.5
and PN based on all data. 109
Table 4-31: Ratios of PM
2.5
, PN, BC, and pPAH concentrations and inhaled dose
between Bus, MRT, and Taxi modes and Walk mode. 110
Table D-1: Descriptive statistics of measurements on the four modes of transportation
and at BG. Maximum and minimum are the highest and lowest data-
points recorded throughout the sampling. GM is the geometric mean of
all measured data-points. Mean (SD) is the arithmetic mean (standard
deviation) of the GM for each trip. 154
Table D-2: Descriptive statistics of measurements in different sections within Bus

mode trips. See Table D-1 caption for details. 155
Table D-3: Same as Table D-2 but for MRT mode. See Table D-1 caption for details.
157
Table D-4: Same as Table D-2 but for Taxi mode trips. See Table D-1 caption for
details. 158
Table D-5: Results from multiple-comparisons tests for effect of different sections on
PM
1
concentrations on Bus mode trips 160
Table D-6: Same as D-5 but for PM
2.5
. 160
Table D-7: Same as D-5 but for PM
10
. 160
Table D-8: Same as D-5 but for PN. 160
Table D-9: Same as D-5 but for ASA. 160
Table D-10: Same as D-5 but for pPAHs. 161
Table D-11: Same as D-5 but for BC. 161
Table D-12: Same as D-5 but for CO. 161
Table D-13: Results of multiple-comparisons tests for effect of different sections on
PM
1
concentrations on MRT mode trips. 161
Table D-14: Same as D-13 but for PM
2.5
. 162
Table D-15: Same as D-13 but for PM
10
. 162

Table D-16: Same as D-13 but for PN. 162
Table D-17: Same as D-13 but for ASA. 162
Table D-18: Same as D-13 but for pPAHs. 162
Table D-19: Same as D-13 but for BC. 163
Table D-20: Same as D-13 but for CO. 163
Table D-21: Results from multiple-comparisons tests for effect of different sections
on PM
1
concentrations on Taxi mode trips. 163
Table D-22: Same as D-21 but for PM
2.5
. 163
Table D-23: Same as D-21 but for PM
10
. 163
Table D-24: Same as D-21 but for PN. 164
Table D-25: Same as D-21 but for ASA. 164
xi

Table D-26: Same as D-21 but for pPAHs. 164
Table D-27: Same as D-21 but for BC. 164
Table D-28: Same as D-21 but for CO. 164
Table D-29: Calculation of inhaled dose by mode and section for Bus mode
measurements of PM
1
, PM
10
, BC and pPAHs ……………………….166
Table D-30: Same as D-29 but for MRT mode ………… ………………………. 167
Table D-31: Same as D-29 but for Taxi mode .………… …………………….…. 168

Table D-32: Same as D-29 but for Walk mode ………… ………………………. 168

xii

List of Figures
Figure 1-1: Map of air quality monitoring stations (yellow triangles) across Singapore
island. (NEA, 2013) 5
Figure 1-2: Singapore’s vehicle population from 2002 to 2013 (Data from Land
Transport Authority, 2014). 7
Figure 2-1: Conceptual framework of the elements of exposure science as related to
humans and ecosystems. (Lioy and Smith, 2013). 11
Figure 2-2: Vortex flow and dispersion within a street canyon. In the situation
depicted, the wind above roof level is perpendicular to the street. This
generates a vortex within the street canyon, and the wind direction at
street level is opposite to the wind direction above roof level.
Pronounced differences in air pollution concentrations on the two sides
of the canyon is the result of these flows. (Hertel and Goodsite, 2009) . 20
Figure 2-3: Time-series of PM
1
concentration for taxi trips with (a) driver’s side
windows closed and (b) driver’s side window open. Source: Yu et al.
(2012) 29
Figure 3-1: Map of Singapore. The Orchard Road study area is located in the Central
Region. (Source: OneMap.sg) 35
Figure 3-2: Orchard Road field site. (a) Pedestrian sidewalk separated from road by
short bushes and tall trees. (b) Width of the sidewalk extends from mall
entrances to the road. 36
Figure 3-3: Sensors used in present study. (a) DustTrak (measures PM
1
, PM

2.5
, PM
10
),
(b) Condensation Particle Counter (measures PN), (c) Microaethlometer
(measures BC), (d) CO Measurer, (e) Diffusion Charger (top) and
Photoelectric Aerosol Sensor (bottom) (measure ASA and pPAH
respectively), (f) Heart rate monitor receiver and electrode strap, and (g)
HOBO Pro v2 (T and RH). GPS used is not pictured. See Table 3-2 for
more sensor details. 38
Figure 3-4: Route selected to evaluate exposure concentrations on four common
transport modes within the commercial Orchard Rd district, Singapore.
Bus and Walk mode trips were taken along the main route (dashed line),
and Taxi mode trips included travel along secondary roads (dotted line).
MRT mode trips were entire underground and not pictured. Also shown
is the location of the background measurement site. (Source: Google
Maps. 44
Figure 3-5: Indoor spaces covered when using specific transport modes in this study:
(a) mall, (b) MRT platform, (c) MRT station , and (d) underpass. 46
Figure 3-6: Outdoor spaces covered when using specific transport modes in this study:
(a) bus-stop, (b) Taxi-stand , (c) sidewalk near the start point of the Walk
mode and (d) sidewalk near the end point of the Walk mode. 46
Figure 3-7: Field at Fort Canning Park used as the background site for comparison
purposes of pollution data collected along the selected route and ambient
pollution levels. Instruments were placed on a table on the cement
platform in the center of the photograph. 50
Figure 3-8: Instrument set-up (a) at background site and (b) during measurements on
different transport modes. Sensors measuring ASA, pPAHs and BC were
xiii

placed in a backpack with sampling lines arranged to sample the typical
breathing zone of adults. 51
Figure 4-1: Post-processed data measured on 20 May 2013. Time-series shown
include one day of measurements on all transport modes. High
variability and presence of spikes are evident in all measured parameters.
58
Figure 4-2: Boxplots of PM
1
(top), PM
2.5
(middle) and PM
10
concentrations (bottom)
measured during the four transport modes and at the background site
(BG) averaged across the entire dataset. Boxes and thick horizontal line
represent the 25
th
to 75
th
percentile (inter-quartile range [IQR]) and
median, respectively, triangles are mean values, vertical lines extend to
the highest or lowest value within 1.5 times the IQR, and diamonds (if
present) are outliers beyond that 62
Figure 4-3: Boxplots of PN measured on the four modes of transport and background
site (BG). For explanation of boxplot symbols see Figure 4-2. 65
Figure 4-4: Boxplots of ASA measured on the four modes of transport. For
explanation of boxplot symbols see Figure 4-2. 67
Figure 4-5: Boxplots of pPAHs measured on the four modes of transport. For
explanation of boxplot symbols see Figure 4-2. 68
Figure 4-6: Correlation between pPAHs and ASA for the entire dataset for each

transport mode. 70
Figure 4-7: PC/DC ratio versus D
ave,S
for each transport mode. 72
Figure 4-8: Boxplots of BC measured on the four modes of transport. For explanation
of boxplot symbols see Figure 4-2. 73
Figure 4-9: Boxplots of CO measured on the four modes of transport and at the
background site. For explanation of boxplot symbols see Figure 4-2. 76
Figure 4-10: Time-series of PM
2.5
and PN concentrations during the Bus mode trip on
10 June 2013. Vertical dashed lines delineate the different sections of the
trip. Periodic increases in PN were observed within the Bus section when
the bus opened its doors to drop off and pick up passengers. 78
Figure 4-11: Spatial variation of PM
2.5
(top) and PN (bottom) concentrations during
the Bus mode trip on 10 June 2013. 79
Figure 4-12: Boxplots of 8 pollutant metrics in different sections of the Bus mode
trips. For explanation of boxplot symbols see Figure 4-2. Mean
background site concentrations, where available, are indicated as dashed
line on the respective panel. 81
Figure 4-13: pPAHs and ASA data collected at Bus-stop sections plotted against each
other for each day of sampling. Linear regression lines and the r
2
of the
relationship are also plotted. 85
Figure 4-14: pPAHs and ASA data collected at Sidewalk sections during Bus mode
trips plotted against each other for each day of sampling. Linear
regression lines and the r

2
of the relationship are also plotted. 86
Figure 4-15: Time-series of PM
2.5
(top) and PN (bottom) concentrations during the
MRT mode trip on 20 May 2013. Vertical dashed lines delineate the
different sections of the trip. 87
Figure 4-16: Boxplots of the 8 pollutant metrics measured in the different sections of
MRT mode trips. For explanation of boxplot symbols see Figure 4-2.
xiv

Mean background site concentrations where available are indicated as a
dashed line on the respective graphs. 89
Figure 4-17: Time-series of PM
2.5
(top) and PN (bottom) concentrations during the
Taxi mode trip on 20 May 2013. Vertical dashed lines delineate the
different sections of the trip. 92
Figure 4-18: Spatial variation in PM
2.5
(top) and PN (bottom) concentrations during
the Taxi mode journey on 20 May 2013. 93
Figure 4-19: Boxplots of the 8 pollutant metrics measured in the different sections of
Taxi mode journeys. For explanation of boxplot symbols see Figure 4-2.
Mean background site concentrations where available are indicated as a
dashed line on the respective graphs. 95
Figure 4-20: pPAHs and ASA data measured at Taxi-stands plotted against each other
for each day of sampling. Linear regression lines and the r
2
of the

relationship are also plotted. 98
Figure 4-21: pPAHs and ASA data collected measured at Sidewalk sections during
Taxi mode trips plotted against each other for each day of sampling.
Linear regression lines and the r
2
of the relationship are also plotted. 99
Figure 4-22: Geometric mean of in-vehicle PN measurements according to car model.
N = number of trips. 101
Figure 4-23: Time series of PM
2.5
(top) and PN (middle) concentrations, and
PM
2.5
/PM
10
ratio (bottom) during the Walk mode trip on 20 May 2013.
102
Figure 4-24: Spatial variation in PM
2.5
(top) and PN (bottom) concentrations during
the Walk mode journey on 20 May 2013. Traffic light symbols denote
traffic junctions 103
Figure 4-25: Photograph of construction area on walk mode trips which exhibited
unusually high pollutant concentrations. Electric fans were installed at
different points along the passage presumably to improve ventilation. 104
Figure 5-1: Photograph of an air-conditioned bus interchange which is linked to an
MRT station and a shopping mall. 118
Figure 5-2: Boxplots of the 8 pollutant metrics measured inside the three vehicles
(Bus, Train, and Taxi). For explanation of boxplot symbols see Figure 4-
2. 121

Figure 5-3: Boxplots of time spent in each section for the Bus, MRT and Taxi mode
trips. For explanation of boxplot symbols see Figure 4-2. 124
Figure A-1: Instrument set-up for the gravimetric calibration . 142
Figure A-2: Distribution of RH values during the two sampling periods ……… 143
Figure A-3: Scatterplots of all data from both measurement periods, showing (a)
linear regression and (b) power regression ……………………… 144




xv

List of Abbreviations
Pollutant metrics
ASA
Active surface area (mm
2
m
-3
)
BC
Black carbon (µg m
-3
)
CO
Carbon monoxide (ppm)
NO
2

Nitrogen dioxide (ppb)

O
3

Ozone (ppm)
pPAHs
Particle-bound polycyclic aromatic hydrocarbons (ng m
-3
)
PN
Particle number (# cm
-3
)
PM
Particulate matter (µg m
-3
)
PM
1
, PM
2.5
, PM
10

Particles with aerodynamic diameter ≤ 1, 2.5, and 10 µm
respectively (µg m
-3
)
SO
2

Sulphur dioxide (µgm
-3
or ppb)
UFP
Ultrafine particles (particles with aerodynamic diameter ≤ 100
nm)
Organisations
IARC
International Agency for Research on Cancer
LTA
Land Transport Authority (Singapore)
NEA
National Environmental Agency (Singapore)
US-EPA
United States Environmental Protection Agency
WHO
World Health Organisation
Other terms
MRT
Mass Rapid Transit
PSI
Pollutant Standards Index




1

Chapter 1. Introduction
Poor air quality is a public health threat that many modern cities face. Recent events,

mainly in major Chinese cities, have attracted global attention and increased public
awareness of the hazards of air pollution, particularly on human health (Fenger, 2009;
Wong, 2013). In 2011, the World Health Organization (WHO) estimated that two
million deaths resulted from the inhalation of polluted air (World Health Organization,
2011). The International Agency for Research on Cancer (IARC), the specialised
cancer agency of the WHO, has also formally classified outdoor air pollution and
particulate matter as harmful carcinogens (Loomis et al., 2013). Combined with the
rapid urbanisation occurring across the globe, air pollution is likely to become one of
the major challenges facing public health in the 21
st
century. In addition to the
negative impacts to human health, poor air quality has other harmful effects, both
direct and indirect, on physical infrastructure, environmental health, climate change,
and economic activity (Mansfield et al., 1991; Vallero, 2008).
1.1 Human exposure to air pollution
Exposure is usually defined as the instantaneous contact between a person and a
pollutant (Ott, 1982). For airborne pollutants, this is the point of contact whereby
humans can inhale the particles or gases. The concept of exposure helps scientists and
policy-makers identify the important factors linking pollution and human health, and
enable the design of necessary research and effective policies to ensure targeted
solutions to the problem of air quality.
Exposure is a particularly useful concept since air pollution in urban areas is
highly heterogeneous both spatially and temporally, due to the myriad of emission
sources within the city, primarily fossil fuel combustion from industry and vehicular
exhaust (Monn, 2001). Urban air quality is also affected by dispersion and
2

transformation processes in the atmosphere from the micro-scale (e.g. pollutant
accumulation in urban street canyons, and formation of new particles through
photochemical reactions of freshly emitted gases and particles) to the regional and

global scales (e.g. transboundary pollution) (Salmond and McKendry, 2009). Despite
the high spatial and temporal variability in pollutant concentrations, it is clear that on-
road motor vehicles are the most important sources of air pollutants that urban
populations are likely to come in contact with in more developed cities. Although
industry is arguably a larger emitter in absolute terms, improved regulation and urban
planning means factories are usually situated away from main populated areas,
minimising human exposure to industrial emissions.
The link between traffic related air pollutants and human health impact has
been supported by numerous epidemiological studies which show relatively
consistent associations between traffic related air pollution and increased risk of heart
attack and respiratory illness in susceptible persons and overall decreased life-
expectancy (e.g. Hoek et al., 2002; Peters et al., 2004; Pope and Dockery, 2006). It
has been suggested that for the general population, traffic related air pollution could
be a more important cause of heart attacks than drug abuse, considering the
prevalence of exposure in the transport microenvironment (Nawrot et al., 2011).
Thus, despite the short time spent outdoors in the transport microenvironment,
the close proximity to motor vehicles can contribute disproportionately to total
exposure. Furthermore, although a relatively short amount of time is spent in the
transport environment, most of the travelling is done during rush-hour periods (i.e.
periods of intense traffic emissions) which are associated with high pollution
concentrations that contribute significantly to the total daily exposure for commuters
(Zuurbier et al., 2010).
3

Personal exposure measurements using small and portable sensors placed
close to or on an individual as they go about their day provide accurate data on the
actual air pollution levels to which people are exposed (Monn, 2001). These sensors
record an exposure concentration, which is the concentration of pollutant (e.g.
particulate matter [PM]) that people come into contact with. Another method is to
combine fixed site measurements in a variety of ‘representative’ microenvironments

with time-activity diaries (Seaton et al., 1999). Personal monitoring is most ideal to
directly capture the pollutants that individuals are exposed to. However, this
technique requires intensive volunteer participation and effort (Van Atten et al., 2005).
A compromise is to take ‘representative’ samples of the population, which has led to
fairly accurate estimate of mean and variability of population exposures.
The number of exposure studies based on the personal monitoring approach
in the transport microenvironment has increased in recent years. Researchers
frequently use portable instruments during simulated daily commutes. These studies
have been carried out in various cities, including London, Hong Kong, Shanghai and
Barcelona, across a variety of transport modes. One notable conclusion from such
studies is that ambient or background monitoring of air quality does not accurately
reflect the variability of pollutant concentrations that people are exposed to at street
level (Gulliver and Briggs, 2004; Kaur et al., 2005a). A more detailed review of these
studies is provided in Chapter 2.
Clearly, exposure to traffic emissions is an important component of pollution
exposure. However, there are few studies carried out in tropical cities, where the hot
and humid conditions may have even graver consequences. At present, only a handful
of studies in Singapore have investigated exposure to aerosols, measuring aerosol
concentrations in indoor environments such as residential blocks (Kalaiarasan et al.,
2009a, 2009b) or hawker centres (See et al., 2006; See and Balasubramanian, 2008),
but none has looked at street level exposure. The present study aims to fill this lack of
4

information for the transport microenvironment by measuring the personal exposure
to aerosols of commuters on different modes of public transport including walking.
1.2 Singapore’s air quality
This section describes the local air quality management to put the present study
within the context of the actual air quality conditions in Singapore. The city-state of
Singapore is typically depicted as a highly urbanised yet green and sustainable city.
Since the founding of the Republic in 1965, the government has placed significant

emphasis on environmental conservation and management issues, including air
quality. The same year the Clean Air Act was enacted in the United States of America
(USA), the government requested an assessment of the air pollution situation in
Singapore (Cleary, 1970). A few months after the assessment, a campaign against
smoky motor vehicles was launched based on recommendations in the report. This
increased public awareness about air pollution issues in general and led to the
formation of the Anti-Pollution Unit. This unit has since evolved into the present-day
National Environment Agency (NEA) which is in charge of monitoring and
regulating the air quality of Singapore.
As of April 2014, the NEA measures and disseminates concentrations of six
criteria pollutants: PM
10
(PM of aerodynamic diameter ≤ 10 µm), carbon monoxide
(CO), nitrogen dioxide (NO
2
), ozone (O
3
), and sulphur dioxide (SO
2
), and the
recently included PM
2.5
(PM of aerodynamic diameter ≤ 2.5 µm). At present, there
are twelve ambient monitoring stations and two road-side stations across the island
(Figure 1-1). The data from these stations are published on the NEA website and
updated hourly. Except for PM
2.5
and NO
2
, which are reported as a non-averaged

hourly concentration, the concentrations of SO
2
and PM
10
are reported as 24-hour
moving averages and the concentrations of O
3
and CO are reported as 8-hour moving
averages. Besides publishing concentration data for criteria pollutants, NEA uses a
5

Pollutant Standards Index (PSI) as a health advisory. The PSI, an index developed
by the United States Environmental Protection Agency (US-EPA) in 1980s, is
reported as a number on a scale of 0 to 500. The PSI reflects the overall
quality of air based on a set of parameters and pollutant concentrations.
However, in 1999 the US-EPA replaced the PSI with the Air Quality Index
which incorporates new standards of PM
2.5
and O
3
.

Figure 1-1: Map of air quality monitoring stations (yellow triangles) across Singapore
island. (NEA, 2013)


In addition to general air quality monitoring, NEA also plays a strong
regulatory role by controlling emissions at the source (National Environmental
Agency, 2013a). This is enforced through inspections on industrial premises and
monitoring stack emissions directly. Vehicular emissions are also tightly monitored,

with a compulsory annual smoke measurement test for all vehicles.
The NEA recently revised the local Ambient Air Quality Targets (AAQT) for
the criteria pollutants which are now based on WHO guidelines and interim targets.
6

Table 1-1 shows how these new targets compare with the US-EPA air quality
standards and the WHO guidelines. The last column in Table 1-1 shows the state of
Singapore’s air quality as reported in the NEA Environmental Protection Division’s
2012 Report (National Environmental Agency, 2013a). Air quality figures for 2013
are also included in parentheses for comparison purposes. (Ministry of the
Environment and Water Resources, 2015). The values for 2013 may not be indicative
of the general air quality situation in Singapore due to an exceptional transboundary
haze episode in June 2013 that disproportionately raised PM
10
, PM
2.5
, and CO
concentrations for that year. The available data suggest that with the exception of
PM
2.5
, PM
10
, and SO
2
, the ambient concentrations of criteria pollutants fall well
below the air quality standards set by the authorities. This is partly due to the
geography of the city-state, which is ideal for the dispersion and deposition of
pollutants (Velasco and Roth, 2012), as well as the strong enforcement and regulatory
role played by the NEA.
Table 1-1: Air quality standards for US-EPA, WHO and NEA and annual air quality in

Singapore in 2012 for the criteria pollutants. Singapore’s air quality in 2013 is provided
in parentheses for comparison purposes.
Pollutant
(units)
Averagin
g time
Air Quality Standards
Singapore’s air quality
in 2012 and 2013
a

US-EPA
WHO
a

NEA
a,b

PM
10

(µg m
-3)

24-hour
150

50
50
Max 57 (Max 215)

Annual
-
20
20
29 (31)
PM
2.5

(µg m
-3
)
24-hour
35
25
37.5
c

Max 42 (Max 176)
Annual
12
10
15
19 (20)
CO (ppm)
1-hour
35
-
24.3
Max 2.0 (Max 7.5)
8-hour

9
-
8.11
Max 1.5 (Max 5.5)
O
3
(ppb)
8-hour
75
47.3
47.3
Max 57.7 (Max 65.8)
NO
2
(ppb)
1-hour
100
98.7
98.7
Max 76.0 (Max 65.2)
Annual
53
19.7
19.7
12.3 (12.3)
SO
2
(ppb)
1-hour
75

-
-
-
24-hour
-
7.09
17.7
c

Max 26.6 (Max 34.7)
Annual
-
-
5.32
4.9 (4.6)
a
Standards for gaseous pollutants originally in mass concentrations, converted to volumetric
concentrations using ideal gas law
b
Ambient Air Quality Targets for 2020. Separate long-term targets for PM
2.5
and SO
2
are not
listed.
c
Air Quality Targets are based on WHO Interim Targets.
7

The relatively high annual concentrations of PM
2.5
(above WHO and NEA air
quality standards) suggest that Singapore faces an issue with fine particle pollution.
Motor vehicles have been recognised as a major source of PM
2.5
, contributing an
estimated 50% of PM
2.5
emissions (National Environmental Agency, 2013a). To help
reduce emissions from transport, the NEA has introduced stricter emission standards
on diesel and gasoline vehicles which took effect in January and April 2014,
respectively. The vehicle population is also tightly managed, and the growth in
number has been declining in recent years, stabilizing the vehicle population at just
under 1 million (Figure 1-2).

Figure 1-2: Singapore’s vehicle population from 2002 to 2013 (Data from Land
Transport Authority, 2014).


As mentioned above, despite the increased recognition of the impacts of
particle pollution from traffic on human health, personal exposure to PM has not been
well documented in Singapore. Past research has shown that ambient monitoring is
inadequate to capture the spatial and temporal variability of pollutant concentrations
8

at ground-level (Kaur et al., 2005b; Gulliver and Briggs, 2007). However, data from
the NEA road-side monitoring stations are not published nor incorporated into the
PSI calculations. Local air quality research have also focussed on ambient
concentrations of particulate and gaseous pollutants, particularly in relation to the

annual transboundary haze produced by wildfires in neighbouring countries (e.g.
Balasubramanian, 2003; He et al., 2010).
1.3 Objectives
The objective of the present study was to evaluate the exposure concentration of
particulate matter to which commuters are exposed on different transport modes in
the tropical, modern city of Singapore. The three available local modes of public
transport (bus, MRT and taxi), as well as walking were investigated and compared
during a hypothetical door-to-door journey in a busy commercial district during the
evening rush hour when pollution and commuter volume tend to be highest.
The total exposure for the entire journey was investigated as well as the
spatial variation within the transport microenvironment. To provide useful
information to reduce commuters’ exposure it is necessary to assess the individual
contributions from the various spaces encountered during a trip (e.g. bus stop, train
platform, while queuing for a taxi). Because of the importance of particle pollution in
the transport microenvironment, a number of important physical and chemical
parameters of aerosols measured using portable and battery operated monitors were
studied The parameters included particle mass concentration of PM
1
, PM
2.5
and PM
10
,
particle number concentrations, active surface area concentration, particulate-bound
polycyclic aromatic hydrocarbons, black carbon, and carbon monoxide. The intake of
particles, or dosage, during each transport mode was also assessed through measuring
concentrations, time spent in the various microenvironments and estimations of the
volume of air exchanged by respiration.
9

The overall objective of the present study can be broken into the following
research questions:
1. What are the levels of aerosol pollution that commuters are exposed to when
travelling via different modes of public transport and walking in Singapore?
2. How do the aerosol concentrations during door-to-door trips on each mode of
transport compare against each other?
3. What is the spatial variation of pollutant concentrations within the transport
microenvironment?
4. What are the relative aerosol pollution dosages experienced by commuters
using public transport and when walking?
1.4 Thesis outline
Chapter 2 introduces the various metrics of particle pollution that were measured and
analysed, and presents a review of commuter exposure research in the transport
microenvironment. Chapter 3 introduces the fieldwork including sampling methods,
instrumentation, route choice, and data quality assurance. The results from the
observations are presented in Chapter 4, which is followed by a discussion of the
main findings in Chapter 5. Finally, Chapter 6 summarises the findings and provides
recommendations regarding future research directions.


10

Chapter 2. Literature Review
Aspects of particle pollution in urban environments that are investigated in the
present study are reviewed first. This is followed by a brief review of research
regarding the fate of emissions within the transport microenvironment and city streets
and finally research focussing on commuter exposure. Only selected work most
relevant to the present study is considered.
2.1 Estimating exposure
Exposure is defined as the point of contact between pollutants and humans. To make

sense of the pathways by which pollutants and contaminants can affect humans and
natural ecosystems, scientists developed the source-to-receptor conceptual framework
(Figure 2-1). This framework links issues of pollution to human health response,
highlights the different factors that contribute to one’s exposure to various stressors,
and takes into account feedback mechanisms (Lioy and Smith, 2013). As highlighted
by the box in Figure 2-1, the central concept of exposure is focused on studying the
pathways by which stressors (e.g. air pollutants) come into contact with receptors (e.g.
humans). With this source-to-receptor framework, scientists of different disciplines
can begin to design the necessary research for targeted solutions whether at the source
or at the point of contact and authorities can begin to design effective policies.
Ambient pollutant concentrations are frequently used as a surrogate for
personal exposure in epidemiological studies. However, actual exposure is strongly
determined by time-activity and behaviour patterns in a variety of different
microenvironments (Jiao et al., 2012). The average exposure is the most commonly
used term in describing exposure and it is the time-weighted average of pollutant
concentrations measured in the different microenvironments where people live, work,
and play (Monn, 2001).