COMMUTER EXPOSURE TO AEROSOL POLLUTION ON PUBLIC TRANSPORT IN SINGAPORE 1

The present study analyses exposure concentrations of particulate matter PM10, PM2.5, particle number PN, black carbon BC, carbon monoxide CO, particle-bound polycyclic aromatic hydrocar

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

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

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

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

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

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 (PM10, PM2.5), 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 PM2.5 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

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

PM2.5 and PN on the Walk mode were 2.5 – 5 times higher than that experienced on the other three transport modes

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 PM2.5, UFP and CO results for the studies listed in Table 2-1

27 Table 2-3: Comparison of PM2.5 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 PM10 and PM2.5 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: PM1/PM2.5 and PM2.5/PM10 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 PM1

concentrations 64 Table 4-6: Same as Table 4-5 but for PM2.5 64 Table 4-7: Same as Table 4-5 but for PM10 64 Table 4-8: Results of multiple-comparisons tests for effect of mode on PN

concentrations 66

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 PM1/PM2.5, PM2.5/PM10, 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 PM1/PM2.5, PM2.5/PM10, 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 PM1/PM2.5, PM2.5/PM10, 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

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 PM2.5 and PN based on all data 109 Table 4-31: Ratios of PM2.5, 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 PM1 concentrations on Bus mode trips 160

Table D-6: Same as D-5 but for PM2.5 160

Table D-7: Same as D-5 but for PM10 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 PM1 concentrations on MRT mode trips 161

Table D-14: Same as D-13 but for PM2.5 162

Table D-15: Same as D-13 but for PM10 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 PM1 concentrations on Taxi mode trips 163

Table D-22: Same as D-21 but for PM2.5 163

Table D-23: Same as D-21 but for PM10 163

Table D-24: Same as D-21 but for PN 164

Table D-25: Same as D-21 but for ASA 164

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 PM1, PM10, 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

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 PM1 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 PM1, PM2.5, PM10),

(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

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 PM1 (top), PM2.5 (middle) and PM10 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 25th to 75th 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

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 PM2.5 (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

Mean background site concentrations where available are indicated as a dashed line on the respective graphs 89 Figure 4-17: Time-series of PM2.5 (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 PM2.5 (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 PM2.5 (top) and PN (middle) concentrations, and

PM2.5/PM10 ratio (bottom) during the Walk mode trip on 20 May 2013 102 Figure 4-24: Spatial variation in PM2.5 (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

IARC International Agency for Research on Cancer

LTA Land Transport Authority (Singapore)

NEA National Environmental Agency (Singapore)

US-EPA United States Environmental Protection Agency

Other terms

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 21st 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

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)

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

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: PM10 (PM of aerodynamic diameter ≤ 10 µm), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2), and the recently included PM2.5 (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 PM2.5 and NO2, which are reported as a non-averaged hourly concentration, the concentrations of SO2 and PM10 are reported as 24-hour moving averages and the concentrations of O3 and CO are reported as 8-hour moving averages Besides publishing concentration data for criteria pollutants, NEA uses a

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 PM2.5 and O3

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

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 PM10, PM2.5, and CO concentrations for that year The available data suggest that with the exception of

PM2.5, PM10, and SO2, 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

The relatively high annual concentrations of PM2.5 (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 PM2.5, contributing an estimated 50% of PM2.5 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

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 PM1, PM2.5 and PM10, 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

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

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)

Figure 2-1: Conceptual framework of the elements of exposure science as related to

humans and ecosystems (Lioy and Smith, 2013)

Within the source-to-receptor framework, the dose comes between the point

of contact with the pollutant and resultant health impact, forming another part of the core of exposure science Dose is the amount of material that is ingested and become absorbed or deposited in the body (Monn, 2001) For air pollutants, this is the amount

of material that is deposited in the respiratory system via inhalation The potential dose can be calculated by multiplying the average exposure by the volume of air exchanged in the lung and the time spent in the microenvironment (Monn, 2001)

The present study investigated the average exposure for a variety of particle metrics as well as estimated the amount of pollutants that is potentially inhaled The next section introduces these metrics and the method that was used for calculating potential dosage

2.1.1 Measuring exposure to particle pollution

Measured metrics

The particulate matter mass concentration (PM) is the main metric used by regulatory authorities Particles are segregated by size which determines how far the particles can penetrate the respiratory tract Smaller particles can reach the deepest regions of the lungs and potentially enter the bloodstream to be transported to other parts of the

body (Oberdörster et al., 2005) The two main particle size fractions currently regulated by authorities are PM10 and PM2.5 which correspond to the cumulative mass concentrations of particles of aerodynamic diameters up to 10 µm and 2.5 µm, respectively These size fractions are also interchangeably termed coarse and fine particles, respectively More specifically, coarse particles are defined as the particles between PM2.5 and PM10, and their presence is usually indicated by a low PM2.5 to

PM10 ratio In addition to PM10 and PM2.5, the present study also investigated the exposure to PM1 (particles of aerodynamic diameter ≤ 1 µm)

There is growing concern that particles of much smaller size have increased toxicological significance For example, approximately 80% of particles in the PM2.5and PM10 size-fractions are deposited in our nasal passages (Oberdörster et al., 2005) However particles of much smaller diameter (i.e < 100 nm) can penetrate further into our respiratory systems The miniscule size coupled with a high surface area to unit mass or unit volume also increases the particle capability to adsorb carcinogenic compounds which may be absorbed into the blood, increasing the risk of undesirable impacts (Lighty et al., 2000; Oberdörster et al., 2005) The small size means these particles make up a negligible component of the overall particle mass concentration, thus a small PM10 or PM2.5 mass concentration may obscure the true health impact These tiny particles are usually referred to as ultrafine particles (UFPs) defined in the present study as particles with a diameter ≤ 100 nm, and are much better quantified

by the particle number (PN) (# particles cm-3) or active surface area (ASA) (mm2 m-3) concentrations than mass concentrations (Heal et al., 2012)

Particles smaller than 100 nm have been found to account for 82 – 87 % of number concentrations, whereas the slightly larger particles of 0.1 – 2.8 µm form approximately 82% of mass concentrations in European cities (Morawska et al., 2008) Based on these findings, PN is a better metric than PM1 to reflect the presence

of UFPs The unit for PN, particle cm-3, will be shortened to # cm-3 for readability

from here onwards Particles are not idealised spheres and may have both internal and external cavities The ASA is a measure of the particle morphology, and refers to the external surface area of particles – the sites on which transfer of momentum, energy, and mass from gas to particle can take place (Keller et al., 2001) In the context of human health, ASA are locations where chemicals can come into direct contact with and be transferred to the walls of the respiratory system Thus it may be more closely linked to the health impacts of particle pollution

Despite the number of epidemiological studies linking traffic pollution and human health effects, there has been no consistent use of exposure metric A variety

of different metrics for assessing human exposure to vehicular traffic are used, including non-pollutant related measures such as proximity to street (Lipfert and Wyzga, 2008) Mass concentrations, PM10 and more recently PM2.5, are still the most widely used metrics in epidemiological studies, which form the main evidence for the present particle air quality standards However, there is great heterogeneity in the concentration and chemistry of pollutants in near road concentrations There are many different characteristics of the vehicle fleet in cities that could affect the tailpipe emissions (Brugge et al., 2007) Careful selection of traffic exposure metrics is thus required in order to distinguish the specific components of vehicle-related emissions (including exhaust emissions and non-combustion emissions such as brake wear and tear) that cause negative health effects (Lipfert and Wyzga, 2008) Some of these possible metrics include PM10, PM2.5, PN and ASA, which are included in the present study There are also other parameters that can better account for source-based

emissions, which include mass concentrations of black carbon (BC), carbon monoxide (CO), and particle-bound polycyclic aromatic hydrocarbons (pPAH)

Lipfert and Wyzga (2008) note that such exposure estimates based on emission source type would provide better evidence for source-based control strategies since

one source may emit a mixture of pollutants This would be more cost-effective and cover everyone across the exposure spectrum

The latter three metrics are all closely associated with traffic emissions Black carbon (also known as elemental carbon or soot) and CO are typical exhaust components of vehicular combustion and are used as tracers for the amount of traffic emissions that contribute to commuter exposure (Lipfert and Wyzga, 2008) In particular, BC has been found to be more closely associated with health impacts than

PM10 or PM2.5 (Heal et al., 2012) Particle-bound polycyclic aromatic hydrocarbons are particles which are covered in polycyclic aromatic hydrocarbons (PAH), a group

of organic chemicals that form through incomplete fossil fuel combustion and have known carcinogenic properties (Ravindra et al., 2008) Motor vehicles are thus a prime source of pPAHs in urban areas (Velasco et al., 2004)

smaller (larger) D ave,S, due to particle growth by coagulation and condensation with increasing distance and time from the point of emission (Lighty et al., 2000;

Bukowiecki et al., 2002) In the present study, it is calculated from ASA and PN measurements, based on the assumption that the particles are perfect spheres Using

the equation for the surface area of spheres, A = 4πr2, where A is the average surface area per particle equal to ASA/PN, the radius, r, can be derived which is equal to 1/2D ave,S Calculations of D ave,S using this method were found to be consistent with measurements by the scanning mobility particle sizer (SMPS), the standard instrument for measuring size-resolved, particle number distributions, particularly over the lower size ranges of combustion derived aerosols (Bukowiecki et al., 2002)

The other calculated metric, the ratio of pPAH to ASA (henceforth referred to

as the PC/DC ratio due to the monitors used for each), is a measure of the mass of pPAHs per unit of active surface area This ratio is an important parameter regarding the chemistry of particles, relating the amount of PAHs potentially transported into the human respiratory system via airborne particles (Ott and Siegmann, 2006) Ott and Siegmann (2006) made measurements of pPAH and ASA using instruments similar to the present study, and showed that different combustion sources have distinct PC/DC signatures The measured emission sources included: cigarettes, incense, candles, cooking, biomass burning (i.e wood smoke), and in-vehicle exposures travelling on arterial roads and interstate highways in California, USA The values found in the present study will be compared to the findings of Ott and Siegmann (2006)

Estimating dosage

As described above, the potential dose (or dosage) can be calculated from average exposure concentrations (obtained through measurement) × inhalation rate × time spent in the particular microenvironment or activity Inhalation can be described by

the minute ventilation (V E) which is a measured of the volume of air that enters the lungs every minute Research in sports medicine has pointed out that athletes have an

increased risk of exposure to pollutants due to (i) increased V E leading to a higher

quantity of inhaled pollutants, (ii) inhalation through the mouth, effectively bypassing the regular nasal mechanisms that help filter away the larger particles and soluble vapours and (iii) increased air flow velocity during exercise which carries pollutants deeper into the respiratory tract (Carlisle and Sharp, 2001) In this respect, active modes of transport such as cycling and walking are expected to lead to a greater inhaled dose of pollutants

Dose is an important parameter that complements average exposure measurements However, potential dose is not commonly included in exposure studies likely due to the complex expertise and logistics required to directly measure

inhalation rates To circumvent the problem of measuring V E directly, the heart rate (HR) is measured instead Since HR is influenced by oxygen consumption and the

correlation between oxygen consumption and V E is high; HR and V E are understood to

be strongly correlated as well (Vai et al., 1988; Zuurbier et al., 2009) In the present

study, V E (L min-1) will be calculated from HR measurements using an equation developed by Zuurbier et al (2010):

ln 𝑉𝐸= 0.022𝐻𝑅 + 0.89 (1)

where HR is in beats per minute (bpm) A caveat for this method of estimating dosage is that Eq (1) was developed for Dutch men and women, who form a very different demographic compared to the volunteers in the present study At the time of writing, no similar study on Asians could be found, thus the absolute values calculated using Eq (1) may not be completely accurate of the dosage experienced in Singapore

2.2 Particle pollution in the transport microenvironment

The transport microenvironment has been extensively investigated because of the unique mix of conditions and processes that govern the fate of pollutant concentrations The presence of motor vehicles as a major pollutant source

distinguishes the transport microenvironment from other indoor or outdoor locations The factors that affect pollutant concentrations within the transport microenvironment include (but are not restricted to) vehicle characteristics, road conditions, building morphology and atmospheric processes

2.2.1 Transport emissions

Motorised transportation modes with internal combustion engines emit particles predominantly from the combustion of fossil fuel Pollution from engine exhaust is especially important because incomplete combustion leads to emissions of many pollutants including CO, volatile organic compounds (VOCs), nitrogen and sulphur oxides, carbonaceous particles, and pPAH (Kittelson, 1998; Colvile et al., 2001; Brugge et al., 2007)

Vehicles can emit particles directly through engine combustion, from abrasion processes (e.g wear and tear of tyres, brake linings and road surface material), or indirectly when particles are re-suspended due to the mechanical turbulence (Charron and Harrison, 2005; Vallero, 2008; Hertel and Goodsite, 2009)

In the atmosphere, particles can undergo further transformative processes including nucleation, coagulation, evaporation, condensation, and agglomeration, which change their shape, size, and composition, resulting in highly heterogeneous spatial and temporal variations (Lighty et al., 2000) Particles can also form in the atmosphere via physical and chemical reactions of primary emitted pollutants Most UFP are formed in this manner, via the nucleation of primary emitted exhaust gases followed

by condensational growth or agglomeration (Heal et al., 2012) Such particles are known as secondary particles

Strategies to control traffic volume have been shown to be effective in controlling pollution at street level In Los Angeles, USA closing a section of a major freeway led to substantial reductions in PN, PM2.5 and BC (Quiros et al., 2013b)

Stringent traffic controls in Beijing resulted in decreases in ambient concentrations of

up to 50% for BC and PM10 during the 2008 Summer Olympic Games (Wang et al., 2009) This was coupled with the aggressive implementation of the Euro IV vehicle emission standards for new registered vehicles and Euro III for existing buses and taxis which also led to 33%, 47% and 78% drops in emission factors of BC, CO and UFP respectively for light-duty gasoline vehicles The introduction of congestion charges in London also appears to have reduced PM10 concentrations at certain locations, although the authors of that study did not rule out other site-specific factors (Atkinson et al., 2009)

Most particles emitted by vehicles have a diameter between 10 and 100 nm, though this can differ depending on fuel composition, vehicle age, driving patterns, and maintenance history (Lighty et al., 2000) In London, United Kingdom (UK), Colvile et al (2001) found that PM10 emissions from diesel vehicles are higher than gasoline vehicles (67% versus 11%), despite making up only 26% of the vehicle fleet

On average, diesel-fuelled vehicles also emit more nanoparticles, making a larger contribution to total PN compared to gasoline-fuelled vehicles (Kittelson et al., 2004, 2006) These UFP are of bimodal size distribution, with a large proportion below 20

nm and between 30 – 100 nm, respectively, with approximately equal total mass in each mode (Shi et al., 1999; Colvile et al., 2001) There has also been research on the impacts of alternative fuels The increasing use of compressed natural gas (CNG) in Delhi, India from 1995 to 2001 resulted in a drop in the annual mean concentrations

of ambient suspended PM (from 405 to 347 µg m-3) (Goyal and Sidhartha, 2003) Bio-fuel was also found to lead to significant decreases in PM and gaseous pollutants (including CO and carbon dioxide [CO2]), but resulted in higher PN emissions compared to conventional diesel or gasoline (Kumar et al., 2010)

Driving conditions can also affect the particle mass and number emission rates Although diesel vehicles generally emit higher particle numbers than gasoline

vehicles, at high engine load and speeds (~120 km h ), gasoline-fuelled engines have been found to emit PN comparable to that from diesel engines (Kittelson et al., 2001)

In general, higher engine loads and speeds produce higher quantities of particle emissions, especially in terms of PN (Kittelson et al., 2001) Stop-start driving patterns such as during traffic jams have also been found to lead to the storage and release of larger amounts of hydrocarbons due to inefficient combustion (Kittelson et al., 2001) Using a mobile emissions laboratory (MEL), Kittelson et al (2004) found on-road PN ranging between 104 to 106 # cm-3 when driving at free-flowing speeds Previously, the MEL recorded lower PN values (103 to 105 # cm-3) during a traffic jam where the average speed was < 32 km h-1, (Kittelson et al., 2001) These findings were replicated more recently by Buonanno et al (2011) who recorded PN, total particle surface area, and PM2.5 along different roads in Cassino, Italy They found that parts of the road with congested traffic, where vehicles are continually stopping and starting, exhibited PN, total particle surface area, and PM2.5 mean values of 430,000 # cm-3, 670 µm2 cm-3, and 41.4 µg m-3 respectively This contrasted greatly with measurements further down the road with vehicles driving at cruising speed (PN: 150,000 # cm-3, total particle surface area: 260 µm2 cm-3, and PM2.5: 16.5 µg m-3) (Buonanno et al., 2011)

2.2.2 Spatial and temporal distribution of particles

The city street is a heterogeneous microenvironment, with horizontal and vertical concentration gradients across the street canyon (Heal et al., 2000) A comprehensive review of the various factors that control UFP concentrations was completed by Morawska et al (2008) In addition to the variability in emissions from vehicles, there are a multitude of other factors affecting the distribution of pollutant concentrations, including street geometry, road layout, and meteorological conditions (Morawska et al., 2008; Buonanno et al., 2011)

The unique geometry of the urban canyon can generate a vortex flow when the winds above the rooftop level are perpendicular to the street (Figure 2-2) (Colvile

et al., 2004) This wind pattern leads to the recirculation of emissions within the urban canyon, keeping pollutant emissions with low release heights (i.e from vehicle exhaust) trapped within the canyon The vortex pushes pollutants towards the leeward side of the canyon, creating concentrations that can vary by up to a factor of two compared to the other side of the street (Sini et al., 1996; Boddy et al., 2005) This vortex flow is influenced by canyon orientation relative to wind direction, height to width ratio, length, symmetry of the canyon and the differential heating of the various urban surfaces (Salmond and McKendry, 2009; Li et al., 2010)

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)

Wind speed and direction have been found to affect pollutant dispersion and removal processes Shi et al (1999) made measurements in Birmingham, UK, which suggest that higher wind speeds contribute differently to mass concentration than number concentrations Charron and Harrison (2005) found that median PM2.5concentrations decreased from 25 to 18 µg m-3 as wind speed increased from below 1

to > 9 m s-1 within an urban canyon in London, UK However, the concentrations of coarse particles were found to increase with increasing wind speed, suggesting that