lable at ScienceDirect

Atmospheric Environment 142 (2016) 251e263

Contents lists avai

Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Particles exposure while sitting at bus stops of hot and humidSingapore

Erik Velasco a, *, Sok Huang Tan b, 1

a Singapore-MIT Alliance for Research and Technology (SMART), Center for Environmental Sensing and Modeling (CENSAM), Singaporeb Department of Geography, National University of Singapore (NUS), Singapore

h i g h l i g h t s

* Corresponding author.E-mail address: evelasco@smart.mit.edu (E. Velasc

1 Now at Ministry of the Environment and Water R

http://dx.doi.org/10.1016/j.atmosenv.2016.07.0541352-2310/© 2016 Elsevier Ltd. All rights reserved.

g r a p h i c a l a b s t r a c t

� Bus stops are hotspots of personalexposure, especially to ultrafineparticles.

� Properties of such particles wereevaluated in-situ at 5 bus stops ofSingapore.

� Commuters breathe on average 3.5more particles (#) than at ambientlevel.

� Fumes of disproportionate magni-tude full of fresh particles arefrequent.

� Nucleation mode particles from 15 to40 nm represent >90% of the parti-cles budget.

a r t i c l e i n f o

Article history:Received 30 March 2016Received in revised form26 July 2016Accepted 28 July 2016Available online 29 July 2016

Keywords:Particles exposureTransport microenvironmentTraffic particlesBus stopsCommuters exposure

a b s t r a c t

Transport microenvironments represent hotspots of personal exposure to airborne toxics, particularly ofultrafine particles. Thus, a large exposure may be experienced during daily commuting trips. Amongstthese microenvironments, bus stops are critical because of the commuters' close proximity to freshfumes rich in particles emitted by passing, idling and accelerating buses and motor vehicles, in general.Standing at a bus stop may represent a period of disproportionately high exposure and it is, therefore,essential to know the number, chemical composition and physical characteristics of such particles for aproper public health assessment and design of mobility strategies. On this account, a set of portable andbattery operated sensors were used to evaluate a number of properties of the traffic particles to whichthousands of citizens are daily exposed at bus stops of Singapore. In terms of fine particles, the exposureconcentration was on average 1.5e3 times higher than the mean concentration at ambient level reportedby the local authorities. On average 60% of those particles corresponded to black carbon. An importantpresence of particle-bound polycyclic aromatics was observed. The particle number concentration andactive surface area were effective metrics to quantify ultrafine particles, as expected both showed strongcorrelations. The number of particles at bus stops was on average 3.5 times higher than at ambient level.The most alarming issue was probably the size of the particles. Assuming spherical particles, a median of27 nm was estimated based on the active surface area and particle number data. Particles of this sizeform the nucleation mode, which is related to harmful health effects.

© 2016 Elsevier Ltd. All rights reserved.

o).esources, Singapore..

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263252

1. Introduction

Although only a minor fraction of the population commutesprivately, the steady increase of private vehicles hasmade vehiculartraffic responsible not only for people's mobility deterioration butalso becoming a major public health threat. Commuters using pri-vate or public transport are daily exposed to over 1000 differentpollutants that have been associated with vehicular emissions (U.S.Environmental Protection Agency, 2006). The health risk of thesepollutants depends on the proximity to the traffic, fleet character-istics, urban morphology, atmospheric condition and exposuretime (Van Atten et al., 2005). The time spent in close proximity tomotor vehicles is usually short but can contribute disproportion-ately to the total daily exposure to airborne toxics (Zuurbier et al.,2011). Furthermore, most of the traveling is done during rush-hour periods in the morning and evening when the traffic emis-sions are intense and the atmospheric stability conditions do notalways promote an efficient dispersion of such emissions.

Public spaces such as bus stops, taxi stands and traffic junctionsare microenvironments associated with intense and highly local-ized emissions. These are sections of the road where drivers areobligated to decelerate, idle, and then accelerate after picking uppassengers or once the signal turns green. This stop-start drivingbehavior has been shown to lead to higher emissions (Buonannoet al., 2011). For instance, there is evidence that under accelera-tion the high engine load conditions enhance the emission of ul-trafine particles (UFP, � 100 nm in diameter) (Wehner et al., 2009;Kittelson, 1998), with heavy-duty diesel-powered vehicles makinga disproportionately large contribution (Morawska et al., 2008a).

While it is inherently difficult to separate the effects of airborneparticles from other pollutants, recent studies suggest that adversehealth effects are generally associated most strongly with thesmallest particles (see articles cited by Knibbs et al. (2011)). Ultra-fine particles are efficiently deposited by diffusion mechanisms inall regions of the respiratory tract, where their large surface areaper unit mass enhances biological interaction (Chio and Liao, 2008).These particles evade specific defense mechanisms and can trans-locate out of the respiratory tract via different pathways andmechanisms.When in blood circulation, they can reach organs suchas the liver, spleen, bone marrow and heart (Oberd€orster et al.,2005). Furthermore, UFP are carriers of some of the most muta-genic and carcinogenic pollutants, such as the polycyclic aromatichydrocarbons (PAHs) (Ravindra et al., 2008).

In consequence, a few minutes wait for a bus may represent aperiod of disproportionately high exposure (Knibbs et al., 2011).Studies have shown that short-term exposure to fine particles ex-acerbates existing pulmonary and cardiovascular diseases (e.g.,Michaels and Kleinman, 2000; Brook et al., 2004; Peters et al.,2004; Upadhyay et al., 2014), hence the importance of measuringthe exposure concentration (i.e. concentration experienced over aperiod of time spent in a particular microenvironment) and char-acteristics of the particles in transport microenvironments such asbus stops, where commuters stand in close proximity to exhaustplumes.

Studies on commuter exposure commonly focus on the in-vehicle section of the journey, failing to capture the exposureconcentration while sitting at a bus stop. Some of these studieshave indirectly addressed the problem reporting significant in-creases of particles concentration when the bus doors open to loadand unload passengers (e.g., Lim et al., 2015; Asmi et al., 2009; Tsaiet al., 2008). Studies targeting particles pollution specifically at busstops are scarce. Moore et al. (2012) and Hess et al. (2010) evaluatedparticles concentration at bus stops according to their location andshelter design in two US cities. Both studies concluded that com-muters waiting for a bus inside a shelter are prone to a higher

exposure than commuters waiting outside, particularly if theshelter is oriented towards the roadway within an urban canyonand close to an intersection. With the aim of evaluating the asso-ciation between particles pollution and cardiovascular morbidity,Dales et al. (2007) tested the effects on vascular reactivity ofexposure to fine and ultrafine particles in volunteers sitting at busstops for 2 h. Their results provided evidence that traffic particlesimpair vasodilation capacity of the coronary arteries therebyincreasing the risk of cardiac ischemia.

This work investigates the particles pollution experienced bySingapore's commuters while waiting for a bus with the hypothesisthat the characteristics of the particles and exposure concentrationare similar in crowded bus stops next to busy roadways across thecity. Various physical and chemical properties of the particles wereconcurrently measured in-situ using portable and battery operatedsensors at five distinctive bus stops. The mixing ratio of carbonmonoxide (CO) and noise level were measured as additionalstressors associated with vehicular traffic, as well as measurementsof ambient temperature and relative humidity were also included.

The disproportionately high concentrations of fresh particlesreported here to which commuters are exposed on a daily basis atbus stops are expected to raise concern for future public healthassessments and urban mobility plans of Singapore and any othercity where public fossil-fuel powered buses represent an importantmode of transportation.

2. Methodology

Five representative and crowded bus stops were selected toevaluate the particles exposure concentration experienced on adaily base by over 3.6million commuters of a total population of 5.3million (Land Transport Authority, 2014). Current transport regu-lations stipulate a maximum waiting time of 30 min for any busroute, with 80% of the buses arriving within 10 min of each otherduring peak hours (Land Transport Authority, 2013). Therefore, acommuter should ideally linger no more than 20 min at bus stopsconsidering one roundtrip per day (e.g., home-office). All bus stopsin Singapore are sheltered but do not count on cooling systems suchas mechanical fans. Because of its geographical location near theequator, Singapore's climate is characterized by perennial hightemperatures and relative humidity (RH) typical of a tropicalclimate. Temperatures range from ~25 �C in the early morning to~35 �C in the afternoonwith an annual average of ~27.5 �C. Relativehumidity is ~90% in the early morning and remains above 60%during the rest of the day. A description of the selected bus stops isprovided in Table 1.

The measurements were conducted on weekdays during themorning (6:30e10:00 h) and evening (17:30e21:00 h) rush hours.These periods correspond to the times when the toll system(Electronic Road Pricing) in the most conflictive roads of the city isin full operation. In total 15 sets of measurements at bus stops wereconducted between January 2011 and July 2012. Eight measure-ments were in the morning and seven in the evening. Additionally,during 1 h our teamwas able to characterize the exhaust emissionsof passenger cars inside a tunnel restricted to large trucks andbuses, and during 2 h the particles pollution in a bus interchange.These two sets of measurements were designed to investigateindividually the vehicle-exhaust signature of both fleets. Passengercars and taxis dominated (>65%) the fleet composition at allmonitored bus stops (Fig. 1). Light goods vehicles counted for10e15% and buses up to 12%.

2.1. Instrumentation

The measured variables were mass concentration of particles

Table 1Description of the five selected bus stops across Singapore for this study.

No. Bus stop (identification number) Characteristics

BS-1 Vivo City (14141) Largest shopping center in Singapore.4 lane road.18 bus services.West Coast Highway flyover above the road.Taxi stand behind the bus stop.

BS-2 Little India (07031) In front of a hawker center5 lane road, with a bus lane.10 bus services.Facing a row of 2-storey shophouses.

BS-3 Bugis (01112) In front of a row of 2-storey shophouses and sheltered bazaar.7 lane road, 3 m-wide road divider separating vehicles traveling in either direction.10 bus services.Bugis Junction shopping center across the road.

BS-4 One Raffles Quay (03059) In front of high rise office building.4 lane road, with a bus lane.19 bus services.High-rise buildings on both sides of the road.Construction activities on opposite side of the road.

BS-5 National University of Singapore (16091) In front of the university sports fields.8 lane road, 1.5 m-wide road divider separating vehicles in either direction.5 bus services

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 253

with aerodynamic diameters � 10, 2.5 and 1 mm (PM10, PM2.5 andPM1), particle number (PN) concentration (only particles with adiameter < 1 mm), active surface area (ASA), and mass concen-trations of black carbon (BC) and particle-bound polycyclic aro-matic hydrocarbons (pPAHs). The mass concentrations of PM10,PM2.5 and PM1 were measured using a DustTrak Aerosol Monitor(TSI 8534). As suggested by numerous epidemiological studies(e.g., Heal et al., 2012) the ASA and PN concentration weremeasured as a mean to quantify UFP. The former was measured bya Diffusion Charging Sensor (Ecochem Analytics DC-2000CE) andthe later by a Condensation Particle Counter (TSI 3007). The massconcentrations of BC and pPAHs were measured by a Micro-Aethalometer (AE51, AethLabs) and a Photoelectric AerosolSensor (Ecochem Analytics PAS-2000CE), respectively. Both, BCand pPAHs are recognized as good markers for traffic-relatedparticles (Janssen et al., 2011; Ravindra et al., 2008; Schauer,2003). As indicated before, CO, noise, ambient temperature andRH were also measured. Carbon monoxide was measured by anelectrochemical detector (T15n, Langan), noise by an IntegratingSound Level Meter (TES-1353) and ambient temperature and RHby a portable logger (HOBO Pro v2, Onset). Table 2 shows thecharacteristics of each instrument.

All sensors were synchronized and programmed for 1 s read-ings, with the exception of the sensors measuring pPAHs and ASA,which were programmed for 10 s readings. Measurements as suchfrequencies are needed to capture the variability and spikes of theexhaust plumes expelled by the buses when brake and accelerateto pick up passengers, as well as by the adjacent vehicles. Theintense turbulence produced by the heating of the urban surfaceand the vortex-like flow within the urban canyon also contributeto the high variability in the pollutants exposure near roadways.The sensors were placed on a table over the benches of the busstops at ~1 m off the ground to sample the air, as much as prac-ticable at the breathing height of the commuters seated on thebenches.

2.2. Instruments correction and data post-processing

The DustTrak Aerosol Monitor measures particles size segre-gatedmass-fraction concentrations with a laser photometer, whosereadings depend on the ambient humidity and particle properties,such as size distribution, morphology, and refractive index. We

follow the approach of Ramachandran et al. (2003) to correct thehumidity effect using the RH data measured simultaneously. Priorto the study, the monitor response to the properties of the particlesin the tropical atmosphere of Singapore was evaluated through agravimetric calibration. Similar to Apte et al. (2011), a power-lawregression relationship was obtained from comparisons with 24-h PM2.5 concentrations determined by gravimetric analysis of 22co-located filter samples with concentrations ranging from 10 to80 mg m�3 (y ¼ 2.657x0.661, r2 ¼ 0.84).

Similarly, the micro-aethalometer readings of BC are sensitive tomechanical shock or vibrations of the instrument. The BC datawerecorrected using software based on the Optimized Noise-reductionAveraging method (ONA) available on the manufacturer's website(wwww.aethlabs.com). A second correction was needed to accountfor the instrument's sensitivity associated with the filter load.Briefly, because BC concentration is measured by changes in thelight attenuation on a disposable filter through which sample air isdrawn at 100 cm3 min�1, concentrations were adjusted using theempirical relationship of Kirchstetter and Novakov (2007) based onattenuation coefficient reported by the instrument.

The CO readings were also corrected with temperature dataaccording to procedures recommended by the instruments manu-facturer (Langan, 2006). The concentrations of PN and pPAHs, aswell as the readings of ASA and noise did not require additionalcorrections. They only passed through a quality assurance in whichsuspicious data were removed using as reference the notes takenduring the sampling (e.g., if the alcohol cartridge inside theCondensation Particle Counter gets dirty or the alcohol level be-comes low, the internal optical sensor delivers erroneous readings).Readings affected by rain were discarded from further analysis.

Prior to each day of measurement, all instruments were syn-chronized to a computer clock in the laboratory. This ensured thatthe time stamp was consistent across all instruments. Instrumentswith removable parts were dismantled and re-assembled for eachday of sampling. Upon arrival at the measurement site, zero cali-bration procedures for the Condensation Particle Counter andDustTrak Aerosol Monitor were carried out. The data post-processing after the measurements included a second synchroni-zation. The lag times of each instrument were computed throughcross-correlations against the DustTrak Aerosol Monitor to achievebetter synchronization across all instruments. Lag times rangedfrom 2 to 15 s on average.

Table 2Instruments information.

Parameter Instrument Lowerthreshold

Accuracy Logginginterval (s)

Model Manufacturer

Size segregated mass-fractionconcentration for PM1, PM2.5,respirable, PM10 and TSP

Handheld Dust TrakDRX Aerosol Monitor

1 mg m�3 ±0.1% of reading or 0.001 mg m�3 a 1 TSI 8534 TSI

Particle number concentration(particles < 1 mm diameter)

Handheld CondensationParticle Counter (CPC)

1 # cm�3 ±20% above 100,000 # cm�3 1 TSI 3007 TSI

Active surface area Handheld DiffusionCharger (DC)

1 mm2 m�3 ±15% of reading or ± 2 mm 2 m�3 a 10 DC 2000CE EcoChemAnalytics

Black carbon Microaethelometer 0.001 mg m�3 ±0.1 mg m�3 1 AE51 AethLabsTotal pPAHs concentration

(particles < 1 mm)Handheld PhotoelectricAerosol Sensor (PAS)

1 ng m�3 ±15% of reading or ± 3 ng m�3 a 10 PAS 2000CE EcoChemAnalytics

Carbon monoxide CO measurer 0.05 ppm 50 ppb 1 T15n LanganProducts Inc.

Noise Integrating SoundLevel Meter

0.1 dB ±1.5% 1 TES-1353 TES ElectricalElectronic Corp.

Temperature & relativehumidity

HOBO Pro v2 logger 0.02 �C at25 �C,0.03% RH

±0.2 �C (from 0 to 50 �C) ±2.5%(from 10 to 90%) to max ±3.5%

1 U23-001 OnsetComputer Corp.

a Whichever is greater.

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263254

3. Results and discussion

This section starts by discussing the time series of a set ofmeasurements in a specific bus stop, as an example to show thevariability in the particles data caused by the irregular pattern ofbuses and vehicles in general, when approaching to traffic lights.Then the statistics and specific features of each evaluated param-eter are analyzed. Indirect metrics such as the pPAHs concentrationto ASA ratio, the mean particles diameter obtained from the ASAand PN concentration data, and the correlation between these twometrics are used to investigate additional characteristics of theparticles, including the presence of nucleation mode and accumu-lation mode particles.

Fig. 1. Fleet composition at the monitored bus stops and tunnel close to downtown.The figures at the top indicate the mean and standard deviation of the traffic countsper minute conducted during the measurements. No traffic counts were conducted atthe Bedok bus interchange.

3.1. Time series and spikes

All particles' metrics measured at the different bus stops werehighly variable as a consequence of the continuous traffic varia-tions. With the exception of the bus stop at the National Universityof Singapore (BS-5), the stops were located in close proximity totraffic intersections, where drivers decelerate, idle, and accelerateaccording to the traffic lights. The irregular timing of arrival anddeparture of buses, individual state of maintenance/repair of busesand vehicles, instantaneous fleet composition (i.e. fraction of ve-hicles that burn diesel and gasoline), and complexwind flowwithinthe urban canyon also contributed significantly to such variability.All particle parameters and to less extent the CO concentrationstracked closely with each other. The time series recorded at theLittle India bus stop (BS-2) during an evening set of measurements(Fig. 2) show clearly the coincidence between spikes; for instance,the highest PM1 spike recorded at 18:27 h, was also the highestspike for BC, ASA and pPAHs, the fourth for PN concentration andclearly evident for CO. Noise, temperature and RH did not show anycorrelation. Many spikes were associated with buses under accel-eration after picking up passengers according to our field notes, anda higher frequency during the peak of the rush hours (8:00e9:30 hand 18:00e19:30 h) is therefore unsurprising. The fluctuations inthe readings of particles and CO shown in Fig. 2 became less intenseafter 19:30 h. This is evident in the time series of BC, ASA, pPAHSand CO. Only CO showed readings below the mean after this hour,while the particle records showed values close to the means. About10 min before the end of the rush hour the ambient temperatureexperienced a temporal increase of 0.5 �C and the RH a 5% drop.

This small change in the meteorological conditions lasted ~30 minand apparently did not affect the pollution levels at the bus stop.Once the temperature and RH returned to previous conditions nochange was observed in the pollutants' concentrations.

3.2. Statistics and ratios

No trends were observed during the morning and evening rushhours at any bus stops. All depicted similar ranges for eachmeasured parameter as shown in Fig. 3. However, the strong vari-ability caused by the traffic variations yielded significant

Fig. 2. Time series of measurements at Little India bus stop (BS-2) conducted on 11-July-2012 during the evening rush hour. The dashed lines indicate the average of each measuredparameter during the whole measurement period. For noise, the dashed line corresponds to the equivalent continuous sound level (Leq).

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 255

differences (p � 0.05; ANOVA) in their means, even between datacollected in the same bus stop in different days. Apparently, theproposed hypothesis is false, but it is difficult to assess which busstop represents a major health threat in terms of the monitoredmetrics. The most significant parameter to sort the bus stops ac-cording to their potential public health damage might be thenumber of commuters. All were busier during the evening,reporting between 1.4 and 20.5 times more commuters than in themorning. The large difference depends on the bus stop location andcommuters pattern. The major difference was observed at the busstop of Bugis Bazaar (BS-3). In the morning between 7 and 13commuters were counted at any time, while between 175 and 237during the evening. This bus stops is located in front of a largecommercial center and next to a subway station close to down-town. In addition to the box plots of Fig. 3, the mean, median andstandard deviation of each parameter measured during each set ofmeasurements is provided in Table A1 in the supplementarymaterial.

3.2.1. Particles mass concentrationAll particle metrics showed outliers (spikes) up to one order of

magnitude larger than the median (Fig. 3). As already discussed,these outliers are embedded in exhaust plumes of buses underacceleration, and although they are temporal (i.e. few seconds),contribute disproportionately to the personal pollution exposure.

The mean PM1 concentration observed at all bus stops was34 mg m�3, varying from 22 to 55 mg m�3 (Fig. 3a). However, spikes>100 mg m�3 were frequent (~10 per hour). Spikes >200 mg m�3

were observed in 7 of 16 sets of measurements, and spikes>300 mgm�3 only in one set. Interestingly, PM1 at BS-5, the bus stopwith theworst traffic, was in general low compared to the other busstops, contrary to ASA and PN concentration, which suggest a majorexposure to UFP rather than to larger particles.

Figs. 2 and 3 showonly PM1 data because they represent>97% ofPM2.5. Similarly, the fraction of PM2.5 in PM10 varied from 89% to94%, with a few cases up to 99%. Moore et al. (2012) reportedessentially the same percentages for bus stops in Portland, Oregon.Traffic exhaust particles comprise almost exclusively particles<1 mmof primary and secondary origin in two predominant modes.Primary particles are generated directly from the engine and aremostly agglomerates of solid phase carbonaceous material residingin the accumulation mode (50 nme1 mm). As the hot exhaust gasescool and condense, they form new particles in the nucleation mode(<50 nm). These secondary particles are short-lived (from secondsto minutes) and consist mainly of hydrocarbons and sulfuric acid(Morawska et al., 2008a). Accumulation mode particles can remainsuspended for several hours and even days since further growth isinefficient and gravitational settling and deposition slow (Healet al., 2012). Particles >1 mm are usually primary emitted by me-chanical abrasion processes, including wear emissions from brake

Fig. 3. Box plots for each one of the measured variables at the studied bus stops, tunnel and bus interchange. In each box, the mid-line shows the median value, the top and bottomof the boxes show the upper and lower quartiles (the 75th and 25th percentiles), and the top and bottom of the whiskers represent the 90th and 10 h percentiles. The extremevalues farther from the median than 1.25 times the whisker end are drawn with markers. Yellow and blue boxes correspond to data collected during the morning and evening rushhours, respectively. Red boxes summarize the data from all measurements at every location. Given that only one set of measurements were performed at the tunnel and businterchange, their data are shown in red boxes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263256

linings, tires, and road pavement, as well as road dust resuspension(Thorpe and Harrison, 2008).

The mass concentrations reported here are similar or higherthan those reported by the other three existing studies on busstops. Moore et al. (2012) recorded 18e60 and 10e16 mg m�3 asmean PM1 concentrations during the morning and evening rushhours. Dales et al. (2007) and Hess et al. (2010) measured onlyPM2.5. The former found mean concentrations of 10 and 40 mg m�3

for two bus stops of Ottawa, Canada, with a corresponding 5%

relative change in the ability to vasodilate with a 30 mg m�3 in-crease. Hess et al. recorded PM2.5 mean concentrations between 13and 19 mgm�3, with spikes over 200 mgm�3 inside bus stop sheltersof Buffalo, New York. The differences are explained by the back-ground concentrations and traffic flow at each city, as well as by thebus stop characteristics and location.

Using as reference the first eight months (MayeDec 2014) of 1-hr PM2.5 data released to the public by the local authorities, wefound concentrations at ambient level (i.e. average urban

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 257

concentrations used for regulatory and advisory purposes) from 9to 25 mg m�3 and 11e27 mg m�3 during the morning and eveningrush hours, with means of 15 and 17 mg m�3, respectively. A com-parison of such mean concentrations measured at the bus stopsindicates that commuters were exposed to PM2.5 concentrationsbetween 1.4 and 2.9 higher during the morning rush hour, andbetween 1.6 and 3.2 times higher during the evening rush hour.Similarly, during the 2-h observation at the bus interchange,commuters were exposed to concentrations 1.7 higher.

Short-term (a few hours) and even very short (<1 h) exposuresto fine aerosols from traffic exhaust of magnitude comparable tothe mean concentrations reported here cause increase in heart rateand myocardial ischemia (Dales et al., 2007; Lanki et al., 2008;Shields et al., 2013; Hemmingsen et al., 2015a); as well as declinein peak expiratory flow and systematic lung inflammatory re-sponses (McCreanor et al., 2007; Sehlstedt et al., 2010; Xu et al.,2013).

3.2.2. Black carbonAs expected BC was a main component of the traffic particles at

all studied locations. Its contribution to PM1 varied from 25% at BS-4 to 80% at BS-5. The former bus stop was located within thebusiness district center and showed the lowest presence of busesand diesel trucks, while the laterwas next to a busy expressway andshowed the largest number of long-haul trucks. Buses and dieseltrucks accounted 6% and 8.2% of the total fleet at both bus stops,respectively (Fig.1). In average, BC contributed 60% to PM1 at all busstops.

Considering only observations inside the tunnel, BC accounts for~40% of the PM1 on-road emissions during cruise conditionsexcluding buses and heavy-duty vehicles. El Haddad et al. (2009)observed the same ratio in a French tunnel, where heavy dutytrucks were also restricted. The exhaust emission data from on-road measurements in California, US compiled by McDonald et al.(2015) for gasoline vehicles from 2004 to 2010 indicate contribu-tions of 25e43% and 51e71% for gasoline and diesel vehicles,respectively. In Singapore, 1% and 85% of the passenger cars andtaxis are powered by diesel (Land Transport Authority, 2015). Forthe particular case of the tunnel studied here, taxis represent 33% ofthe total fleet (Fig. 1) increasing the fraction of vehicles powered bydiesel to 28%. This fraction of diesel vehicles explains the BCcontribution in Singapore between those reported for US gasolineand diesel vehicles and the close similitude with the contributionreported for France. The French study did not report fleet compo-sition, but the authors assumed it was representative of the Frenchvehicular distribution, where 49% of the light-duty vehicles usediesel.

No difference was found in the BC to PM1 ratio at the businterchange compared to that inside the tunnel. The bus inter-change has a capacity for 60 buses, but at that time, only 40 buseswere present on average, with the majority parked and the engineoff. The reduced number (~5e10) of buses in movement or idlingwas not apparently sufficient to alter the BC fraction of particlesemitted by the traffic in nearby streets composed mainly of pas-senger cars and taxis.

Excluding results from BS-5, which was far from any intersec-tion and had a slightly different fleet composition, the additional20% of BC in PM1measured at bus stops in comparison to the tunneland bus interchange can be explained by the exhaust emissions ofbuses accelerating after picking up passengers, and in general of allvehicles once the traffic signal turns green.

3.2.3. Particle number concentrationAs mentioned previously, PN concentration is frequently used to

quantify UFP because of their negligible mass compared with larger

particles. Vehicular traffic is the dominant emission source of UFPin modern cities (e.g. Pallavi and Harrison, 2013), and microenvi-ronments associated with public transportation are therefore amajor concern in terms of personal exposure. The highest con-centrations have been reported in on-road and roadside measure-ments (Morawska et al., 2008a). In addition to the emissionsvariability produced by the traffic volume and driving pattern, thedistance from the road curb has been shown to be an importantparameter in the exposure to UFP (Kaur et al., 2006). Within a fewseconds and over a few meters away from the road, the PN con-centration can change up to one order of magnitude. At bus stopscommuters concentrate exactly at the curbside within a distanceusually no longer than 3m from the road, and are therefore directlyexposed to fresh fumes rich in UFP emitted by passing, idling andaccelerating vehicles. In general, concentrations over 40 � 103 #cm�3 were observed at all bus stops at any time (Fig. 3b). Theaverage commuter exposure concentration to UFP was 78 � 103 #cm�3, varying between 45 � 103 # cm�3 and 158 � 103 # cm�3. Nodifference was observed between morning and evening measure-ment periods. As expected, bus stops next to roads with heavytraffic showed the highest records. The exposure concentrationencountered at the bus interchange (44 � 103 # cm�3) was lowerthan at any bus stop.

The highest mean PN concentration was observed at BS-5 andthe lowest at BS-4. The traffic flow, speed, and distribution explainthe mean concentration 3.5 times higher at the former bus stop.During the morning period at BS-5, when the highest mean level ofUFP was observed, 223 vehicles min�1 were counted, while only 28vehicles min�1 at BS-4 during the evening period associated withthe lowest mean PN concentration. Buses and heavy-goods vehiclesrepresented together 6.0% and 8.2% of the total traffic volume ateach bus stop, with a difference between the locations of 11 largediesel vehicles min�1. Gasoline and diesel vehicles emit UFP, butdiesel vehicles do it in a larger magnitude (Rose et al., 2006). Thespeed limit on roads next to BS-5 and BS-4 was 90 km h�1 and40 km h�1. As the traffic speed increases, the engine load, exhausttemperature, and exhaust flow increase also, resulting in a higheremission of UFP (Kittelson et al., 2004).

Measurements conducted over a month of 2013 in the middle ofthe 18 ha Fort Canning Park in close proximity to Singapore'sdowntown and exactly above the investigated tunnel in this study,reported a mean PN concentration of 22 � 103 # cm�3 and anassociated standard deviation of 5� 103 # cm�3 during the eveningrush hour (Tan, 2015). This variability is significantly smaller thanthat observed at bus stops (Fig. 3B). Using this concentration asrepresentative of the urban ambient level of UFP in Singapore,commuters at the studied bus stops were exposed to concentra-tions between two and seven times higher, with an average factorof 3.5 among all bus stops.

The mean exposure concentrations to UFP at bus stops ofSingapore are in general higher than those reported from roadsidelocations in other cities. Moore et al. (2012) reported mean con-centrations between 12 and 57� 103 # cm�3, and Dales et al. (2007)between 13 and 80 � 103 # cm�3 for bus stops of Portland, Oregon,and Ottawa, Canada, respectively. Using data from 18 air pollutionstudies in traffic microenvironments Morawska et al. (2008a) foundan average concentration of 48 � 103 # cm�3, while Kumar et al.(2014) using data from 42 different cities found a similar concen-tration of 44 � 103 # cm�3. Although a direct comparison acrosssites is not possible given the numerous factors affecting theemission and dispersion of UFP, such as traffic characteristics, fueltype, urban morphology, climate, etc., as well as sampling locationand instrumentation, the figures presented above provide insightto define bus stops as hot spots of UFP. Both, gasoline and dieselengines emit more UFP at the time of acceleration, as Klems et al.

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263258

(2010) reported for vehicles accelerating after a red traffic lightturns green.

3.2.4. Active surface areaUltrafine particles represent the highest surface area per mass.

This is particularly true in transport microenvironments dominatedby the nucleation and accumulation modes. Current standardsbased on PM2.5 and PM10 mass concentration fail in controllingexposure to such particles (Morawska et al., 2008b). From anepidemiological point of view, ASA is apparently the most relevantmetric to quantify UFP. A strong correlation between ASA and lunginflammatory response has been recognized. The ASA increaseswith decreasing particles size, and surface related parameters, suchas oxidative potency, solubility and bioavailability are widelyregarded as the parameters determining particles toxicity(Oberd€orster et al., 2005).

The ASA measured by the commercial sensor based on diffusioncharging (DC) used in this study is the part of the particles' surfacethat actively exchanges electrical charge with the carrier gas. It isalso known as “effective surface” or “Fuchs surface” and has provedto describe correctly the exchange of momentum, energy and massbetween the particle and surrounding air (Keller et al., 2001).Diffusion charging sensors have been widely used to characterizeUFP from vehicle exhaust (e.g. Bukowiecki et al., 2002; Ott andSiegmann, 2006; Polidori et al., 2008).

Readings of ASA >100 mm2 m�3 were generally observed at alllocations. The highest mean (294 mm2 m�3) and lowest mean(85 mm2 m�3) ASA were recorded at BS-5 and BS-4 in agreementwith the PN concentration. Unsurprisingly ASA and PN concentra-tion followed essentially the same trend at all locations, displayingthe same peaks and valleys, as shown in the time series of Fig. 2b fora particular set of measurements. Considering the different sam-pling frequency for both parameters (10 and 1 s, respectively), theyshowed strong correlations with coefficients of determination (r2)between 0.40 and 0.67. Using as reference the slopes of the linear-least-square fits to the measurements, with the y-intercept of theline forced through zero, we found that the mean ASA per particlewas relatively constant at all bus stops, varying from 1990 to2640 nm2 #�1, with an average of 2238 nm2 #�1. The ratio at thebus interchange was similar, while at the tunnel was slightlysmaller (1740 nm2 #�1), indicating a major contribution of nucle-ation rather than accumulation mode particles. The banning oftrucks and buses inside the tunnel may be the cause. A moreintense nucleation from the fresh emitted gases and/or a slowergrowth by coagulation or vapor adsorption of the particles emittedby passing vehicles in an enclosed atmosphere with limited dilu-tion may also explain the lower ratio.

Compared to ASA data from roads of Mexico City, the readings inSingapore's bus stops were slightly lower. Velasco et al. (2004)reported ASA of 250e350 mm2 m�3 for congested traffic and300e400 mm2 m�3 for smoothly running traffic. This is in agree-ment with the highest ASA reported for BS-5, where traffic speed isrelatively constant and is not affected by any nearby intersection ortraffic light. Spikes over 400 mm2 m�3 were observed in Mexicanroads as well as Singapore's bus stops (Fig. 3d). Such spikes coin-cided with spikes in pPAHS and PN concentration and were asso-ciated with buses under acceleration as already discussed. In thecase of Mexico City, in addition to heavy-duty diesel vehicles, suchspikes were also observed in exhaust fumes of gasoline cars withmalfunctioning engines and probably also deficient catalyticconverters.

3.2.5. Particles-bound polycyclic aromatic hydrocarbonsPolycyclic aromatic hydrocarbons are good tracers of vehicle

exhaust particles (Ravindra et al., 2008). They resist the high

temperature of the tailpipe gases thanks to the stability provided bythe aromatic rings. When the exhaust gases cool, PAHs of four ormore rings condense at the surface of existing particles. Theseparticles binding large PAHs can be detected through their efficientphotoelectric charging (PC) enhanced by their hydrophobic nature.Water molecules at the surface would quench any photoelectricalactivity (Bluhm and Siegmann, 2009). The commercial PC particlesensor used here has been evaluated through laboratory and fieldintercomparisons with other techniques such as gas chromatog-raphy and aerosol mass spectroscopy reporting consistent results(e.g. Chetwittayachan et al., 2002; Marr et al., 2006).

Similar to the metrics discussed above, the traffic characteristicscaused large spikes at all locations. Mean concentrations rangedfrom 112 ng m�3 at BS-4 to 446 ng m�3 at BS-1. The mean con-centration recorded inside the tunnel (165 ng m�3) and bus inter-change (154 ng m�3) were generally lower. The emission profiles ofpPAHs vary among engine type; diesel vehicles have larger emis-sion factors than gasoline cars, but their emissions contain morelow-molecular-weight PAHs, whereas gasoline cars are the prin-cipal source of high-molecular-weight PAHs (Marr et al., 1999). ThepPAHs at bus stops contain therefore a mix of low- and high-molecular PAHs. However, a major exposure to low-molecularPAHs can be expected due to the commuters' close proximity tothe accelerating buses. This is particularly true when the plumesexpelled by the buses fill up completely the stop's space, as it isdisplayed on the continuous spikes in the time series (Fig. 2c).

Using the same PC monitor Houston et al. (2013) reported pPAHconcentrations of 123 ± 141 ng m�3 in bus stops of Los Angeles, US,while Velasco et al. (2004) found concentrations of484 ± 445 ng m�3 in two busy and chaotic bus interchanges ofMexico City. Cheng et al. (2012) reported 405 ng m�3 as meanconcentration next to a road of Hong Kong with heavy traffic(>6000 vehicles h�1) and close to an intersection and a bus stop.Similarly, Brachtl et al. (2009) reported an hourly mean concen-tration of 340 ng m�3 next to a road (970 vehicles h�1) and 50 maway from a bus stop of Quito, Ecuador. Despite the differences inlocation, traffic characteristics and vehicular fleet, the pPAH con-centrations observed in Singapore's bus stops were similar orhigher than those reported in such cities. The highest concentrationof 446 ± 216 observed at BS-1 is similar to those reported forMexico City and Hong Kong. The main contributor in Mexico Citywas old diesel buses while in Hong Kong, a mix of gasoline (60%),diesel (30%) and liquefied petroleum gas (10%) vehicles. The trafficat BS-1 during that particular period was composed of 73% and 27%of gasoline and diesel vehicles. The traffic flow was lower at BS-1(4320 ± 960 vehicles h�1), but the number of buses and heavyduty diesel vehicles was higher. At the Hong Kong site 180 largediesel vehicles were counted per hour, while in Singapore themeanwas 350. In the case of Quito, the high concentration is explained bythe old vehicular fleet of which only 45% counts with catalyticconverter and a major proportion of buses (20%) in the traffic alongthe monitored road. The traffic speed in those locations was slowand intermittent as in Singapore's bus stops (<50 km h�1).

3.2.6. PC/DC ratioA plot of the pPAHs concentration and ASA obtained from con-

current measurements yields a fairly linear relationship for anytype of combustion and has been described as a fingerprint forindividual types of particles (Bukowiecki et al., 2002; Matter et al.,1999). Both variables address characteristics of the particles' sur-face: the PC monitor responds to PAHs on the surface material, andthe DC monitor measures the fraction of the geometrical surface incontact with the carrier gas. The ratio between these two variablesis called the PC/DC ratio. Ott and Siegmann (2006) measured aslope of 0.15 for cigarette smoke, 0.13 for incense, 0.29 for wood

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 259

smoke, 1.8 for candle smoke, and 0.42e0.58 ng mm�2 along roadsof California. Note that particles from other than combustionsources can generally not be charged photoelectrically because ofthe absence of PAHs. Hence, such particles will produce a signalonly in diffusion charging, with a PC/DC z 0.

The PC/DC ratio found at the different bus stops of Singaporewas consistently high and varied from 1.11 to 1.73 ng mm�2 (Fig. 4).The ratio inside the tunnel was 1.36 ng mm�2 and at the businterchange 1.33 ng mm�2. The strongest correlation (r2 ¼ 0.84)corresponded to the tunnel measurements, where buses and heavyduty vehicles are banned and plume dispersion and chemical ac-tivity is limited. The scatter in the pPAHs versus ASA plot, as well asthe difference among locations is explained by variations in thefleet composition and traffic conditions. Previous studies haveshown that high emissions of pPAHs are generally related to hardacceleration, possibly due to incomplete combustion (e.g., Tanget al., 2001). Congested and slow moving traffic produces lesspPAHs and, therefore, smaller PC/DC ratios than typical runningtraffic with continuous periods of acceleration and deceleration(Velasco et al., 2004). Compared to ratios reported for traffic inother cities, Singapore's bus stops are among the highest, withvalues similar to those heavily influenced by diesel vehicles. Ratiosbetween 0.35 and 0.58 ng mm�2 are typical for traffic pollution incities of US and Mexico, where diesel vehicles are generally limitedto public buses and large trucks. Heavy-duty diesel vehicles pro-duce ratios ~1 ng mm�2 (Ott and Siegmann, 2006; Velasco et al.,2004). In cities where tax laws favor light-duty vehicles poweredby diesel, ratios >1 ng mm�2 can be expected. Siegmann et al.(1999) reported a maximum PC/DC ratio of 1.83 ng mm�2 for aroad of Zurich heavily influenced by diesel traffic.

The high concentrations of pPAHs and PC/DC ratios >1 ng mm�2

in Singapore's bus stops are unequivocal indicators of the stronginfluence of public buses on the commuters' exposure to airbornetoxics. The mutagenic and carcinogenic potential of PAHs has beenwidely documented by epidemiological and toxicological studies(see Ravindra et al., 2008).

3.2.7. Particles average sizeAssuming spherical particles, the average size of the particles

Fig. 4. Relationships between pPAHs concentration and ASA as measured by the PCand DC monitors at the five studied bus tops, tunnel and bus interchange. The datainclude samples collected during the morning and evening rush hours. The solid linesindicate the best average ratio for all samples collected in each location with theirlengths representing the range of observed values. The average ratios correspond tothe slopes of the linear-least-square fits to the measurements, with the y-intercept ofthe line forced through zero. The numbers in parenthesis at the right of the slopes inthe figure's annotation indicate the relationships' strength by the coefficients ofdetermination (r2). Slopes reported for roadways in other cities with the sameinstrumentation taken from Ott and Siegmann (2006) and Velasco et al. (2004) aredepicted by dashed lines for comparison purposes.

can be determined by the diameter of average surface (DAver,S) asproposed by Kittelson et al. (2000) from the concurrent and inde-pendent measurements of PN concentration and ASA. Nucleationmode particles encompass over 90% of the particle number incombustion exhaust plumes and, therefore, DAver,S can be used asan indicator of such particles. It represents the diameter of a hy-pothetical monodisperse particle that has the same ASA as themeasured polydisperse particle. Fig. 5 shows the DAver,S distributionat the bus stops through a probability density plot. The particlessize ranged from 15 to 40 nmwith a mean of 27 nm, essentially therange reported for typical diesel particles from modern engines(Euro IV or newer).With low-emission engines nucleation becomesmore likely, especially when catalytic after-treatment devices(catalytic traps, fuel additives or catalytic converters) are used.These devices are efficient removing solid particles, but not volatilematerial in the gas phase. After passing the catalytic device the lackof solid surface to condense on results in high supersaturation thatleads to homogeneous nucleation of the volatile material(Burtscher, 2005). Gasoline vehicles also contribute to the occur-rence of nucleation mode particles in the range between 5 and50 nm (Bukowiecki et al., 2002).

Old diesel engines without catalytic trap emit particles in thesize range of 30e300 nm including a visible contribution in theaccumulation mode. This contribution was not observed by theDAver,S at Singapore's bus stops. Filters reduce by about two ordersof magnitude the number of particles in the accumulation mode(Burtscher, 2005). Although a few public buses in Singapore still fallunder Euro I, II and III emission standards (Public Transport SG,2014), the observed DAver,S suggests that the majority of busescomply with higher emission standards. Since 2014, all newlyregistered heavy vehicles in Singapore must adhere to the Euro Vemission standards.

3.2.8. PC/DC ratio versus DAver,STo investigate the presence and absence of nucleation mode

particles and accumulation mode particles bonding PAHs, we canplot, as did Bukowiecki et al. (2002), the PC/DC ratio versus DAver,S.Fig. 6 includes all data points collected at all locations in this study.Three extreme situations or vertices can be observed. The vertex atthe top (1) represents data points with large PC/DC ratio, but smallDAver,S. The number of data points in this situation was small, giventhat pPAHs (i.e. particles causing high PC signal) are predominately

Fig. 5. Particle size distribution at each studied location based on the DAver, S calculatedfrom the concurrent readings of PN concentration and ASA. The distributions includedata collected during the morning and evening rush hour periods. The gray areacorresponds to the particles size distribution at all bus stops. The median DAver, S atSingapore's bus stops was 27 nm.

Fig. 6. PC/DC versus DAver, S for all locations sampled in this study. The observed dis-tribution follows a triangle-like empirical distribution described in the text.

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263260

associated with fresh accumulation mode particles. Nucleationmode particles consist of material inhibiting the PC response (i.e.other than PAHs). The vertex at the right corner (2) is attributed tolow pPAHs and low PN concentrations. It may result from agedaccumulation mode particles or a particular engine condition inwhich a high emission of accumulationmode particles acts as a sinkfor volatile species that in turn suppresses the PAHs photoemission.Vertex (3) at the left is dominated by nucleation mode particles inthe range from 10 to 30 nm and composed of non-photoemittingmaterial such as hydrocarbons and sulfuric acid.

The bulk of data points collected at the bus stops, bus inter-change, and tunnel falls in the middle of those extreme situationswith some bias to vertex (3). This bias is in agreement with a majorparticle contribution from buses with modern engines thatenhance nucleation after the catalytic device instead of agglomer-ation or condensation on primary emitted particles in the accu-mulation mode (Burtscher, 2005). Nevertheless, the presence ofaccumulation mode particles is important, as it was exhibited bythe high level of pPAHs.

Although these observations are qualitative and the PC/DC ratioand DAver,S depend both on the ASA data, we can provide insight onthe chemical and physical properties of the particles to whichcommuters are exposed at bus stops in Singapore. Commuters areexposed to fresh UFP in both modes, including pPAHs and particlescomposed of volatile material, in addition to carbonaceous ag-glomerates coated by condensable species such as semi-volatilehydrocarbons, nitrates, and sulfates. In term of health effects, par-ticle chemistry should be considered, in particular that of the ma-terial at the surface, including its potential volatilization onceinhaled.

3.2.9. Carbon monoxideIn urban environments, vehicular traffic is the main source of

CO, especially vehicles with gasoline engines. However, the tight-ness in emission regulations in modern cities like Singapore hasbeen effective in controlling its ambient concentration well belowinternational standards (e.g., 20 ppm in 1-h, California AirResources Board, 2009). The actual ambient mixing ratios inSingapore are usually below 2 ppm (Ministry of the Environmentand Water Resources, 2015). The mean mixing ratios recorded atbus stops were similar to those at ambient level. Frequent short-lived plumes >2 ppm were observed, as well as sporadic plumes

>4 ppm. The highest spikes were recorded at BS-3 with mixingratios of ~10 ppm. These spikes were a consequence of the heavytraffic and accelerating pattern, and coincided with spikes in par-ticles concentration. However, beyond such spikes no significantcorrelations (r2 < 0.2) were observed between CO and the particlesdata due to the low records of CO. During the last three decadesmore progress has been achieved in controlling emissions of COthan particles and other exhaust gases (e.g., Harley et al., 2005).

Health guidelines indicate that the exposure to CO should notproduce levels of carboxyhemoglobin (COHb, CO binding with he-moglobin) > 2.5% to no reduce the oxygen-carrying capacity of theblood. A maximum exposure to 90 ppm for 15 min ensures suchthreshold (World Health Organization (WHO), 2000). Hence, theCO exposure in Singapore's bus stops does not represent a healththreat, in contrast to the particles towhich commuters are exposed.

3.2.10. NoiseIn five set of measurements, noise was monitored as an addi-

tional stressor to which commuters are exposed while waiting for abus. The equivalent continuous sound level (Leq) ranged from 76.6to 83.9 dBA at the three monitored bus stops. Instantaneous noiselevels >85 dBA were frequent and >90 dBA occurred (e.g., Fig. 2d).The recorded noise level classifies as “very annoying” by mostscales of reference (e.g. Ouis, 2001), having a significant impact onpeople's mood. The World Health Organization (WHO) recom-mends a 24-h Leq < 70 dBA with instantaneous and occasionalnoises < 110 dBA to avoid hearing impairment in traffic areas(World Health Organization (WHO), 1999). Singapore's legislationmarks as maximum permissible a 12-h Leq of 65 dBA during day-time in residential neighborhoods and a maximum of 75 dBA incommercial premises for periods no longer than 5 min (SingaporeGovernment, 2008). Considering the current local transport regu-lation that stipulates a waiting time no longer than 10 min duringpeak hours for any bus route (Land Transport Authority, 2013), therecorded noise level at bus stops clearly exceeds the 5-min Leqestablished for commercial premises.

Even though traffic-related noise and particles increase the riskof cardiovascular disease and may cause oxidative stress, a syner-gistic mechanism between them on adverse health effects has yetto be found (Hemmingsen et al., 2015b; Stansfeld, 2015). However,there is sufficient evidence from epidemiological studies of theirindependent impacts, and both must, therefore, be consideredwhen assessing public health risk.

3.2.11. Temperature and humidityConditions were hot and humid at all monitored locations. At

bus stops the temperature varied from 25 to 31 �C, contrasting with20e22 �C inside the buses. Boarding a bus represents a quick anddrastic change of 5e10 �C. This thermal transient creates an un-pleasant sensation (negative alliesthesia, de Dear, 2011) and ironi-cally forces commuters to carry sweaters everywhere. Relativehumidity (RH) ranged generally between 60% and 75%. In hotconditions, the loss of metabolic heat is dominated by evaporation,and increased humidity exacerbates thermal discomfort (Nicol,2004).

3.2.12. ConclusionsThe exposure concentration to particles at bus stops during the

morning and evening rush hours was evaluated through the use ofportable sensors. The time spent at bus stops in Singapore is usuallyshort (�20 min considering one roundtrip per day). However, thecommuters' close proximity to exhaust plumes of diesel and gas-oline vehicles under acceleration makes waiting for a bus a periodof high exposure to fresh particles. A typical commute represents amean PM2.5 exposure of 23e57 mg m�3, including very probably a

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 261

puff of >100 mg m�3. Dismissing these short-lived spikes caused bysmoky buses, commuters experience concentrations 1.5e3 timeshigher than those reported by the local air quality monitoringnetwork at ambient level.

One might argue, that a short exposure to such PM2.5 massconcentration is not alarming. However, the scientific evidenceindicates the opposite. Very short exposure to traffic particles ex-acerbates existing pulmonary and cardiovascular diseases,including increase in heart rate, myocardial ischemia, decline inexpiratory flow and lung inflammatory responses (see referencescited above). Ultimately, the daily exposure to particles at bus stopsbecomes a chronic problem. Consider a roundtrip per day (e.g.,home-office), five days per week and the waiting time mentionedabove, as commuter youwill be exposed 1.7 h to such particles levelduring a week, 6.7 h in a month, and 3.3 days over a year.

As described throughout the article, current metrics based onPM2.5 and PM10 mass concentration are apparently not sufficient incontrolling exposure to traffic particles. The nucleation modedominates the number of particles at bus stops. Based on the DAver,Sthe particles size ranges from 15 to 40 nm, coinciding with therange reported for particles emitted by modern diesel engines andgasoline vehicles. Their mass is relatively negligible, and parame-ters such PN concentration and ASA become therefore morerepresentative. The nucleationmode particles are formed as the hotexhaust gases cool and condense after passing the emission controldevices. They are composed of volatile material, such as hydrocar-bons and sulfuric acid, which may volatilize once inhaled, andtrigger additional health effects.

The measurements of BC and pPAHs revealed the presence alsoof accumulation mode particles. These are mostly agglomerates ofsolid phase carbonaceous material of 50 nm to 1 mm that can becoated by condensable species such as semi-volatile hydrocarbons,nitrates, and sulfates. PAHs of four or more rings also condense overexisting particles as the exhaust gases cool. These compounds are ofmajor relevance because of their mutagenic and carcinogenicpotential.

Although the physical and chemical properties of the particlesreported here correspond to Singapore's bus stops, results are ex-pected to raise concern in any city where public fossil-fuel poweredbuses represent a major mode of transportation. In terms of publichealth assessments and mobility plans the most relevant findingsof this study are:

� All particles metrics were highly variable as a consequence ofthe continuous traffic variations and accelerating pattern. Spikesup to one order of magnitude larger than the median werefrequent.

� Traffic exhaust particles comprise almost exclusively particles<1 mm. PM1 represents >97% of PM2.5. Similarly, the fraction ofPM2.5 in PM10 varies from 89% to 94%.

� At bus stops BC contributes on average 60% to PM1. Measure-ments inside a tunnel where buses and heavy-duty vehicles arebanned reported a contribution of 40%, thus, plumes expelled bybuses under acceleration explain the additional 20% of BC in PM1at bus stops.

� Considering the PN concentration as a representative metric toquantify UFP, the average commuter exposure concentration toUFP was 78 � 103 # cm�3, varying from 45 � 103 to 158 � 103 #cm�3. In comparison to ambient levels, the exposure to UFP atbus stops is between two and seven times higher, with a meanfactor of 3.5.

� Readings of ASA >100 mm2 m�3 were generally observed. Asexpected, ASA and PN concentration showed good and constantcorrelations among all monitored bus stops, with a mean ASA toPN concentration ratio of 2238 nm2 #�1.

� The concentration of pPAHs observed in Singapore's bus stopswas similar or higher than those reported for roadsides withheavy traffic of cities such as Mexico City and Hong Kong.

� Using the pPAHs concentration to ASA ratio (PC/DC ratio) as afingerprint for individual types of combustion particles, theratio > 1 ng mm�2 observed at bus stops is an unequivocal in-dicator of the strong influence of public buses on the com-muters' exposure to airborne toxics.

� Assuming spherical particles, the concurrent readings of PNconcentration and ASA were used to estimate a mean averageparticle size of 27 nm, with 90% of the particles being smallerthan 40 nm. Over 60% of these particles reach the alveolar sacs,from where can translocate to the blood stream and reach or-gans such as liver, spleen, bone marrow and heart (Oberd€orsteret al., 2005).

� Although commuters are exposed to UFP of both modes,nucleation and accumulation, the former is the dominant modeas a consequence of the close proximity to the fresh plumesexpelled by buses equipped with catalytic traps in most cases.

� The exposure to CO does not represent a health threat. Theobserved concentrations are well below international standardsand health guidelines.

� The recorded noise level at bus stops classifies as “veryannoying.”

Throughout a given day, citizens are exposed to different levelsand mixtures of airborne pollutants depending on the microenvi-ronments in which they spend their time, their proximity toemission sources and their occupational exposures. In moderncities like Singapore, transport microenvironments represent hot-spots of emissions and personal exposure. Thus, a large proportionof exposuremay occur during daily commuting trips. Because of thebus stops location at curbsides and in many cases before in-tersections, results unsurprisingly show that a short waiting for abus every morning or evening may represent a disproportionatelyhigh exposure, especially of fresh UFP.

Waiting for a bus in a hot, humid, noisy and polluted environ-ment discourages the use of public transport. A better design of thebus stops would help. Setting bus shelters a few meters back andafter passing intersections would reduce the exposure to freshfumes rich in UFP emitted by passing, idling and accelerating ve-hicles. Accurate and friendly timetables of the buses' schedules ateach stop would reduce the waiting time, and thus the exposure.Similarly, larger shelters would protect better from the sun, andfans could be installed to improve the commuters' comfort.

In the long term, investments in electric public transport, suchas electric buses and trams, in concert with programs promotingcycling and walking as a means to cover the so called first and lastmiles (distances that commuters must cover in getting to and frompublic transportation) will improve urban mobility, whileproviding cleaner air to thousands of citizens.

Acknowledgments

This study was supported by the gs1:National Research Foun-dation Singapore under its Campus for Research Excellence andTechnological Enterprise program. The Center for EnvironmentalSensing and Modeling is an interdisciplinary research group of theSingapore-MIT Alliance for Research and Technology. The authorsthank the participation of M. Quak and V. Lim in the field mea-surements, and the comments and suggestions of L. Norford fromthe Massachusetts Institute of Technology and M. Roth from theDept. of Geography of the National University of Singapore (NUS).The assistance provided by R. Balasubramanian and R. Betha fromthe Dept. of Civil and Environmental Engineering of NUS to validate

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263262

the response of the DustTrak monitor for measuring particles massconcentration is much appreciated.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2016.07.054.

References

Apte, J.S., Kirchstetter, T.W., Reich, A.H., Deshpande, S.J., Kaushik, G., Chel, A.,Marshall, J.D., Nazaroff, W.W., 2011. Concentrations of fine, ultrafine, and blackcarbon particles in auto-rickshaws in New Delhi, India. Atmos. Environ. 45,4470e4480.

Asmi, E., Antola, M., Yli-Tuomi, T., Jantunen, M., Aarnio, P., Makela, T., Hillamo, R.,Hameri, K., 2009. Driver and passenger concentration to aerosol particles inbuses and trams in Helsinki, Finland. Sci. Total Environ. 407 (8), 2860e2867.

Bluhm, H., Siegmann, H.C., 2009. Surface science with aerosols. Surf. Sci. 603,1969e1978.

Brachtl, M.V., Durant, J.L., Perez, C.P., Oviedo, J., Sempertegui, F., Naumova, E.N.,Griffiths, J.K., 2009. Spatial and temporal variations and mobile source emis-sions of polycyclic aromatic hydrocarbons in Quito, Ecuador. Environ. Pollut. 157(2), 528e536.

Brook, R.D., Franklin, B., Cascio, W., Hong, Y., Howard, G., Lipsett, M., Luepker, R.,Mittleman, M., Samet, J., Smith, S.C., Tager, I., 2004. A statement for healthcareprofessionals from the expert panel on population and prevention science ofthe American Heart Association. Circulation 109 (21), 2655e2671.

Bukowiecki, N., Kittelson, D.B., Watts, W.F., Burtscher, H., Weingartner, E.,Baltensperger, U., 2002. Real-time characterization of ultrafine and accumula-tion mode particles in ambient combustion aerosols. J. Aerosol Sci. 33 (8),1139e1154.

Buonanno, G., Fuoco, F.C., Stabile, L., 2011. Influential parameters on particleexposure of pedestrians in urban microenvironments. Atmos. Environ. 45 (7),1434e1443.

Burtscher, H., 2005. Physical characterization of particulate emissions from dieselengines: a review. J. Aerosol Sci. 36 (7), 896e932.

California Air Resources Board, 2009. California ambient Air Quality Standards(CAAQS). California Environmental Protection Agency, California, USA. http://www.arb.ca.gov/research/aaqs/caaqs/caaqs.htm. visited on 29 Jan 2016.

Cheng, Y., Ho, K.F., Wu, W.J., Ho, S.S.H., Lee, S.C., Huang, Y., Zhang, Y.W., Yau, P.S.,Gao, Y., Chan, C.S., 2012. Real-time characterization of particle-bound polycyclicaromatic hydrocarbons at a heavily trafficked roadside site. Aerosol Air Qual.Res. 12, 1181e1188.

Chetwittayachan, T., Shimazaki, D., Yamamoto, K., 2002. A comparison of temporalvariation of particle-bound polycyclic aromatic hydrocarbons (pPAHs) con-centration in different urban environments: Tokyo, Japan, and Bangkok,Thailand. Atmos. Environ. 36 (12), 2027e2037.

Chio, C.-P., Liao, C.-M., 2008. Assessment of atmospheric ultrafine carbon particle-induced human health risk based on surface area dosimetry. Atmos. Environ.42, 8575e8584.

Dales, R., Liu, L., Szyszkowick, M., Dalipaj, M., Willey, J., Kulka, R., Ruddy, T.D., 2007.Particulate air pollution and vascular reactivity: the bus stop study. Int. ArchivesOccup. Environ. Health 81, 159e164.

de Dear, R., 2011. Revisiting an old hypothesis of human thermal perception:alliesthesia. Build. Res. Inf. 39 (2), 108e117.

El Haddad, I., Marchand, N., Dron, J., Temime-Roussel, B., Quivet, E., Wortham, H.,Jaffrezo, J.L., Baduel, C., Voisin, D., Besombes, J.L., Gille, G., 2009. Comprehensiveprimary particulate organic characterization of vehicular exhaust emissions inFrance. Atmos. Environ. 43 (39), 6190e6198.

Harley, R.A., Marr, L.C., Lehner, J.K., Giddings, S.N., 2005. Changes in motor vehicleemissions on diurnal to decadal time scales and effects on atmosphericcomposition. Environ. Sci. Technol. 39, 5356e5362.

Heal, M.R., Kumar, P., Harrison, R.M., 2012. Particles, air quality, policy and health.Chem. Soc. Rev. 41 (19), 6606e6630.

Hemmingsen, J.G., Rissler, J., Lykkesfeldt, J., Sallsten, G., Krisiansen, J., Møller, P.,Loft, S., 2015a. Controlled exposure to particulate matter from urban street air isassociated with decreased vasodilation and heart rate variability in overweightand older adults. Part. Fibre Toxicol. 12 (6) http://dx.doi.org/10.1186/s12989-015-0081-9.

Hemmingsen, J.G., Møller, P., Jantzen, K., J€onsson, B.A., Albin, M., Wierzbicka, A.,Gudmundsson, A., Loft, S., Rissler, J., 2015b. Controlled exposure to dieselexhaust and traffic noiseeEffects on oxidative stress and activation in mono-nuclear blood cells. Mutat. Research/Fundamental Mol. Mech. Mutagen. 775,66e71.

Hess, D.B., Ray, P.D., Stinson, A.E., Park, J.Y., 2010. Determinants of exposure to fineparticulate matter (PM2.5) for waiting passengers at bus stops. Atmos. Environ.44, 5174e5182.

Houston, D., Wu, J., Yang, D., Jaimes, G., 2013. Particle-bound polycyclic aromatichydrocarbon concentrations in transportation microenvironments. Atmos. En-viron. 71, 148e157.

Janssen, N.A.H., Hoek, G., Simic-Lawson, M., Fischer, P., van Bree, L., ten Brink, H.,Keuken, M., Atkinson, R.W., Anderson, H.R., Brunekreef, B., Cassee, F.R., 2011.

Black Carbon as an additional indicator of the adverse health effects of airborneparticles compared with PM10 and PM2.5. Environ. Health Perspect. 119,1691e1699.

Kaur, S., Clark, R.D.R., Walsh, P.T., Arnold, S.J., Colvile, R.N., Nieuwenhuijsen, M.J.,2006. Exposure visualisation of ultrafine particle counts in a transport micro-environment. Atmos. Environ. 40 (2), 386e398.

Keller, A., Fierz, M., Siegmann, K., Siegmann, H.C., Filippov, A., 2001. Surface sciencewith nanosized particles in a carrier gas. J. Vac. Sci. Technol. A 19 (1), 1e8.

Kirchstetter, T.W., Novakov, T., 2007. Controlled generation of black carbon particlesfrom a diffusion flame and application in evaluating black carbon measurementmethods. Atmos. Environ. 41, 1874e1888.

Kittelson, D.B., 1998. Engines and nanoparticles: a review. J. Aerosol Sci. 29,575e588.

Kittelson, D., Johnson, J., Watts, W., Wei, Q., Drayton, M., Paulsen, D., Bukowiecki, N.,2000. Diesel Aerosol Sampling in the Atmosphere (No. 2000-01-2212). SAETechnical Paper.

Kittelson, D.B., Watts, W.F., Johnson, J.P., 2004. Nanoparticle emissions on Minne-sota highways. Atmos. Environ. 38, 9e19.

Klems, J.P., Pennington, M.R., Zordan, C.A., Johnston, M.V., 2010. Ultrafine particlesnear a roadway intersection: origin and apportionment of fast changes inconcentration. Environ. Sci. Technol. 44 (20), 7903e7907.

Knibbs, L.D., Cole-Hunter, T., Morawska, L., 2011. A review of commuter exposure toultrafine particles and its health effects. Atmos. Environ. 45, 2611e2622.

Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M.,Harrison, R.M., Norford, L., Britter, R., 2014. Ultrafine particles in cities. Environ.Int. 66, 1e10.

Land Transport Authority, 2013. Land Transport Master Plan 2013. Singapore Gov-ernment. http://www.lta.gov.sg. visited on 15 July 2015.

Land Transport Authority, 2014. Land Transport Statistics in Brief 2014. SingaporeGovernment. http://www.lta.gov.sg. visited on 15 July 2015.

Land Transport Authority, 2015. Monthly Vehicle Statistics. Singapore Government.http://www.lta.gov.sg. visited on 13 Oct 2015.

Langan, L., 2006. Langan Model T15n Carbon Monoxide Measurer: a CompleteMiniature Instrument for Portable High-resolution Measurements of CarbonMonoxide and Temperature. Langan Products Inc., CA USA.

Lanki, T., Hoek, G., Timonen, K.L., Peters, A., Tiittanen, P., Vanninen, E., Pekkanen, J.,2008. Hourly variation in fine particle exposure is associated with transientlyincreased risk of ST segment depression. Occup. Environ. Med. 65, 782e786.

Lim, S., Dirks, K.N., Salmond, J.A., Xie, S., 2015. Determinants of spikes in ultrafineparticle concentration whilst commuting by bus. Atmopsheric Environ. 112,1e8.

Marr, L.C., Kirchstetter, T.W., Harley, R.A., Miguel, A.H., Hering, S.V., Hammond, S.K.,1999. Characterization of polycyclic aromatic hydrocarbons in motor vehiclefuels and exhaust emissions. Environ. Sci. Technol. 33, 3091e3099.

Marr, L.C., Dzepina, K., Jimenez, J.L., Reisen, F., Bethel, H.L., Arey, J., Gaffney, J.S.,Marley, N.A., Molina, M.J., 2006. Sources and transformations of particle-boundpolycyclic aromatic hydrocarbons in Mexico City. Atmos. Chem. Phys. 6 (6),1733e1745.

Matter, U., Siegmann, H.C., Burtscher, H., 1999. Dynamic field measurements ofsubmicron particles from diesel engines. Environ. Sci. Technol. 33, 1946e1952.

McCreanor, J., Cullinan, P., Nieuwenhuijsen, M.J., Stewart-Evans, J., Malliarou, E.,Jarup, L., Harrington, R., Svartengren, M., Han, I.-K., Ohman-Strickland, P.,Chung, K.F., Zhang, J., 2007. Respiratory effects of exposure to diesel traffic inpersons with asthma. N. Engl. J. Med. 357 (23), 2348e2358.

McDonald, B.C., Goldstein, A.H., Harley, R.A., 2015. Long-term trends in Californiamobile source emissions and ambient concentrations of black carbon andorganic aerosol. Environ. Sci. Technol. 49 (8), 5178e5188.

Michaels, R.A., Kleinman, M.T., 2000. Incidence and apparent health significance ofbrief airborne particle excursions. Aerosol Sci. Technol. 32 (2), 93e105.

Ministry of the Environment and Water Resources, 2015. Key Environmental Sta-tistics 2015. Singapore Government, Singapore.

Moore, A., Figliozzi, M., Monsere, C.M., 2012. Air quality at bus stops: empiricalanalysis of exposure to particulate matter at bus stop shelters. TransportationResearch Record. J. Transp. Res. Board 2270, 76e86.

Morawska, L., Ristovski, Z., Jayaratne, E.R., Keogh, D.U., Ling, X., 2008a. Ambientnano and ultrafine particles from motor vehicle emissions: characteristics,ambient processing and implications on human exposure. Atmos. Environ. 42,8113e8138.

Morawska, L., Keoght, D.G., Thomas, S.B., Mengersen, K., 2008b. Modality inambient particle size distribution and its potential as a basis for developing airquality regulation. Atmos. Environ. 42, 1617e1628.

Nicol, F., 2004. Adaptive thermal comfort standards in the hotehumid tropics.Energy Build. 36 (7), 628e637.

Oberd€orster, G., Oberd€orster, E., Oberd€orster, J., 2005. Nanotoxicology: an emergingdiscipline evolving from studies of ultrafine particles. Environ. Health Perspect.113, 823e839.

Ott, W.R., Siegmann, H.C., 2006. Using multiple continuous fine particle monitors tocharacterize tobacco, incense, candle, cooking, wood burning, and vehicularsources in indoor, outdoor, and in-transit settings. Atmos. Environ. 40 (5),821e843.

Ouis, D., 2001. Annoyance from road traffic noise: a review. J. Environ. Psychol. 21,101e120.

Pallavi, P., Harrison, R.M., 2013. Estimation of the contribution of road trafficemissions to particulate matter concentrations from field measurements: areview. Atmos. Environ. 77, 78e97.

E. Velasco, S.H. Tan / Atmospheric Environment 142 (2016) 251e263 263

Peters, A., von Klot, S., Heier, M., Trentinaglia, I., H€ormann, A., Wichmann, H.E.,L€owel, H., 2004. Exposure to traffic and the onset of myocardial infarction.N. Engl. J. Med. 351 (17), 1721e1730.

Polidori, A., Hu, S., Biswas, S., Delfino, R.J., Sioutas, C., 2008. Real-time character-ization of particle-bound polycyclic aromatic hydrocarbons in ambient aerosolsand from motor-vehicle exhaust. Atmos. Chem. Phys. 8 (5), 1277e1291.

Public Transport SG, 2014. European Emission Standards. Public Transport SG.https://publictransportsg.wordpress.com/2014/02/04/european-emission-standards/. visited on 3 December 2015.

Ramachandran, G., Adgate, J.L., Pratt, G.C., Sexton, K., 2003. Characterizing indoorand outdoor 15 minutes average PM 2.5 concentrations in urban neighborhoods.Aerosol Sci. Technol. 37 (1), 33e45.

Ravindra, K., Sokhi, R., Van Grieken, R., 2008. Atmospheric polycyclic hydrocarbons:source attribution, emission factors and regulation. Atmos. Environ. 42,2895e2921.

Rose, D., Wehner, B., Ketzel, M., Engler, C., Voigtl€ander, J., Tuch, T., Wiedensohler, A.,2006. Atmospheric number size distributions of soot particles and estimation ofemission factors. Atmos. Chem. Phys. 6 (4), 1021e1031.

Schauer, J.J., 2003. Evaluation of elemental carbon as a marker for diesel particulatematter. J. Expo. Analysis Environ. Epidemiol. 13, 443e453.

Sehlstedt, M., Behnding, A.F., Boman, C., Blomberg, A., Sandst€om, Pourazar, J., 2010.Airway inflammatory response to diesel exhaust generated at urban cyclerunning conditions. Inhal. Toxicol. 22 (10), 1144e1150.

Shields, K.N., Cavallari, J.M., Hunt, M.J., Lazo, M., Molina, M., Molina, L., Holguin, F.,2013. Traffic-related air pollution exposures and changes in heart rate vari-ability in Mexico City: a panel study. Environ. Health 12 (7).

Siegmann, K., Scherrer, L., Siegmann, H.C., 1999. Physical and chemical properties ofairborne nanoscale particles and how to measure the impact on human health.J. Mol. Struct. THEOCHEM 458 (1), 191e201.

Singapore Government, 2008. Environmental Protection Management Act. Chapter94A, Section 77: Boundary Noise Limits for Factory Premises. G.N. No. S 156/1999 Revised Edition 2008, Singapore.

Stansfeld, S.A., 2015. Noise effects on health in the context of air pollution exposure.Int. J. Environ. Res. Public Health 12 (10), 12735e12760.

Tan, S.H., 2015. Commuter Exposure to Aerosol Pollution on Public Transport ofSingapore. Master degree Thesis. National University of Singapore, Singapore.

Tang, S., Johnson, R., Lanni, T., Webster, W., 2001. Monitoring of PM-bound Poly-cyclic Aromatic Hydrocarbons from Diesel Vehicles by Photoelectric AerosolSensor (PAS). SAE Technical Paper Series No. 2001-01-3578.

Thorpe, A., Harrison, R.M., 2008. Sources and properties of non-exhaust particulatematter from road traffic: a review. Sci. Total Environ. 400, 270e282.

Tsai, D.H., Wu, Y.H., Chan, C.C., 2008. Comparisons of commuter's concentration toparticulate matters while using different transportation modes. Sci. Total En-viron. 405 (1e3), 71e77.

Upadhyay, S., Ganguly, K., Stoeger, T., 2014. Inhaled ambient particulate matter andlung health burden. Eur. Med. J. e Respir. 2, 88e95.

U.S. Environmental Protection Agency, 2006. The Master List of Compounds Emittedby Mobile Sources. EPA420-B-06e002, Feb. U.S. Environmental ProtectionAgency, Washington, D.C.

Van Atten, C., Brauer, M., Funk, T., Gilbert, N.L., Graham, L., Kaden, D., Miller, P.J.,Bracho, L.R., Wheeler, A., White, R.H., 2005. Assessing population exposures tomotor vehicle exhaust. Rev. Environ. Health 20, 195e214.

Velasco, E., Siegmann, P., Siegmann, H.C., 2004. Exploratory study of particle-boundpolycyclic aromatic hydrocarbons in different environments of Mexico City.Atmos. Environ. 38 (29), 4957e4968.

Wehner, B., Uhrner, U., von L€owis, S., Zallinger, M., Wiedensohler, A., 2009. Aerosolnumber size distribution within the exhaust plume of a diesel and a gasolinepassenger car under on-road conditions and determination of emission factors.Atmos. Environ. 43, 1235e1245.

World Health Organization (WHO), 1999. In: Berglund, B., Lindvall, T., Schwela, D.H.(Eds.), Guidelines for Community Noise. World Health Organization, Genova.

World Health Organization (WHO), 2000 (chapter 5).5 carbon monoxide. In: WHOAir Quality Guidelines for Europe, second ed. WHO Regional Office for Europe,Copenhagen, Denmark.

Xu, Y., Barregard, L., Nielsen, J., Gudmundsson, A., Wierzbicka, A., Axmon, A.,J€onsson, B.A.G., Kåredal, M., Maria Albin, 2013. Effects of diesel exposure onlung function and inflammation biomarkers from airway and peripheral bloodof healthy volunteers in a chamber study. Part. Fibre Toxicol. 10, 60.

Zuurbier, M., Hoek, G., Oldenwening, Meliefste, K., van den Hazel, P., Brunekreef, B.,2011. Respiratory effects of commuters' exposure to air pollution in traffic.Epidemiology 22 (2), 219e227.