Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau

Accepted Manuscript Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau Ben Liu, Mandy Minle He, Cheng Wu, Jinjian Li, Ying Li, Ngai Ting Lau, Jian Zhen Yu, Alexis K.H. Lau, Jimmy C.H. Fung, Ka In Hoi, Kai Meng Mok, Chak K. Chan, Yong Jie Li PII:

S1352-2310(18)30714-3

DOI:

10.1016/j.atmosenv.2018.10.024

Reference:

AEA 16321

To appear in:

Atmospheric Environment

Received Date: 22 December 2017 Revised Date:

30 September 2018

Accepted Date: 17 October 2018

Please cite this article as: Liu, B., He, M.M., Wu, C., Li, J., Li, Y., Lau, N.T., Yu, J.Z., Lau, A.K.H., Fung, J.C.H., Hoi, K.I., Mok, K.M., Chan, C.K., Li, Y.J., Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau, Atmospheric Environment (2018), doi: https:// doi.org/10.1016/j.atmosenv.2018.10.024. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Potential Exposure to Fine Particulate Matter (PM2.5) and Black Carbon on Jogging

RI PT

Trails in Macau

Ben Liu1, Mandy Minle He1, Cheng Wu2, Jinjian Li3, Ying Li4, Ngai Ting Lau3, Jian Zhen Yu3, Alexis K.H. Lau3, Jimmy C.H. Fung3, Ka In Hoi1, Kai Meng Mok1, Chak K. Chan5, Yong Jie

SC

Li1,* 1

M AN U

Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China

2

Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou 510632, China 3

Division of Environment, Hong Kong University of Science and Technology, Hong Kong, China

4

School of Energy and Environment, City University of Hong Kong, Hong Kong, China

EP

5

TE D

Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China

To Whom Correspondence Should be Addressed

AC C

*

Yong Jie Li: E11-3017, Faculty of Science and Technology, University of Macau, E11, Avenida da Universidade, Taipa, Macau, China Tel: (853) 8822-4943; Fax: (853) 8822-2426 1

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Email: [email protected]

2

ACCEPTED MANUSCRIPT

Abstract: The health effects of atmospheric particulate matter (PM) have become a major

2

environmental concern in urban areas. Most PM studies are mainly designed to measure the

3

“ambient” or “emitted” concentrations of PM. Some studies are specifically designed to address

4

exposure to PM for pedestrians and/or commuters on-board vehicles or at bus stops, but less

5

attention is paid to the exposure during physical exercise such as jogging. To this end,

6

concentrations of both fine particulate matter (PM2.5) and black carbon (BC) were measured

7

along three jogging trails in the densely populated city Macau in China. The three jogging trails

8

include the campus of University of Macau (UM), Guia Municipal Park (GP), and Saivan Lake

9

(SL). In our measurements, PM2.5 and BC ranged from 2.9 to 84.1 and 0.4 to 19.5 µg/m³,

10

respectively. BC/ PM2.5 ratio ranged from 0.016 to 0.448. Among all three jogging trails, the

11

highest BC concentration was found at SL (19.5 µg/m³), and the highest PM2.5 concentration was

12

found at UM (84.1 µg/m³). On the contrary, the BC and PM2.5 concentrations at the elevated

13

(about 50 m above sea level) GP trail were lower than those at the other two jogging trails. BC

14

and PM2.5 concentrations were generally lower in the night loops (21:30 – 23:00) than those in

15

the morning loops (07:30 – 09:00), which coincide with morning rush hours, with only a few

16

exceptions. The difference in geographical locations also affects the BC and PM2.5

17

concentrations measured, with locations near bus terminals, busy roads, or with congested street

18

canyons having higher concentrations. Doses of BC and PM2.5 after 60 min of exposure during

19

typical jogging exercise are also estimated to evaluate the exposure to PM pollution at these

20

three jogging trails when exercising. The results from the current studies provide information

21

both on personal choice for the time/venue for jogging exercise and on future abatement policy

22

to mitigate such risks of exposure to BC and PM2.5.

23

Key words: exposure, PM2.5, black carbon, jogging

AC C

EP

TE D

M AN U

SC

RI PT

1

3

ACCEPTED MANUSCRIPT

24

1.

Introduction Particulate matter (PM) pollution has become an important environmental concern in recent

26

years (Li et al., 2017). Fine particulate matter (PM2.5) that has a diameter of 2.5 micrometer (µm)

27

or less imposes great impacts on our environment by altering solar radiation budget and

28

obscuring light in the range of vision, leading to respective effects on global climate and

29

visibility degradation. Besides, PM2.5 also has great impacts on human health, because of its

30

capability to penetrate the respiratory system carrying hazardous substance (Bond et al., 2013;

31

Lei et al., 2016; Rao et al., 2013; Zhao et al., 2011). Within PM2.5, black carbon (BC) that is

32

operationally defined as the light-absorbing carbonaceous aerosol component (Janssen et al.,

33

2012), has been suggested to have a closer association with certain health effects than PM2.5 does

34

(Bell et al., 2009; Janssen et al., 2012; Li et al., 2016; Patel et al., 2009). The high toxicity

35

potent of BC might be due to its larger specific surface area with irregular aggregate-like

36

morphology, as well as the ability to penetrate into the deepest regions of the lung (Braniš et al.,

37

2010; Janssen et al., 2012; Suglia et al., 2008).

TE D

M AN U

SC

RI PT

25

There are many studies that investigated the exposure to PM2.5 and BC in different

39

microenvironments. Wilson et al. (2006) estimated an individual exposure to PM2.5 on a daily

40

basis, and found that the nonambient exposure, defined as the exposure to PM generated by

41

indoor sources and an individual's personal activities, was not related to the ambient

42

concentration of PM2.5 (R2 < 10-6). Health risk assessments of PM2.5 and BC were also

43

investigated in kindergartens in Hong Kong by Deng et al. (2016), which showed that cooking

44

events might have caused BC concentrations to rise both indoors and outdoors. The same finding

45

was documented by Jeong et al. (2017) that charbroiling meat presented exceedingly high

46

exposure to BC. Positive correlation between BC concentrations and traffic emissions was also

AC C

EP

38

4

ACCEPTED MANUSCRIPT

demonstrated in this study. Some other investigations focused on exposure to PM by commuters

48

in various transport microenvironments or travel modes (Che et al., 2016; Ham et al., 2017; Kaur

49

et al., 2005; Lei et al., 2016). Lei et al. (2016) evaluated the daily exposure to PM2.5 and BC in

50

Shanghai during various activities, and revealed that outdoor activities contributed the most to

51

PM2.5 and BC exposure, with transportation having higher BC exposure dose intensity than PM2.5.

52

In Hong Kong, Che et al. (2016) used a sequential measurement method to quantify the

53

variability in PM2.5 concentration during usage of public transportation. The authors found that

54

PM2.5 concentration in trains of Mass Transit Railway were the lowest and those at bus terminals

55

the highest. Kaur et al. (2005) examined pedestrian exposure to PM2.5 in the microenvironment

56

of commuting in Central London, indicating that pedestrians and cyclists experienced lower

57

concentration of PM2.5 compared to those inside vehicles. In the study by Ham et al. (2017), the

58

largest average concentrations for both PM2.5 and BC were measured during commuting by train,

59

while those during commuting by light-rail were the lowest. Exposure to BC was also studied in

60

other various environments (Fruin et al., 2004; Li et al., 2015; Rivas et al., 2016; Williams and

61

Knibbs, 2016). In a word, many studies suggested that commuters’ exposures to PM are strongly

62

related to the types of microenvironments in addition to emission strengths.

EP

TE D

M AN U

SC

RI PT

47

These studies, however, were designed to evaluate the exposure to PM pollutions for either

64

pedestrians, cyclists, passengers on board vehicles or waiting at bus stops (Gerharz et al., 2009;

65

Goel et al., 2015; Jinsart et al., 2002; Lei et al., 2016; Li et al., 2015; Martins et al., 2015;

66

Vilcassim et al., 2014; Weichenthal et al., 2014; Williams and Knibbs, 2016). Current studies

67

scarcely investigated quantitatively the exposure of individuals to PM while doing physical

68

exercises such as jogging in different microenvironments. In contrast to the original purpose,

69

exercise in highly polluted environments might exacerbate some of health conditions, rather than

AC C

63

5

ACCEPTED MANUSCRIPT

help improve them (de Hartog et al., 2010; Tainio et al., 2015). Among the factors that affect

71

total exposure, including (a) concentration level, (b) time spent, and (c) inhalation rate, the effect

72

of inhalation rate is seldom investigated. For people (e.g. 21-31 years old) who are exercising (i.e.

73

high intensity), the inhalation rate can be 11.9 times higher than that at rest (i.e.

74

sedentary/passive) (US EPA, 2011). Therefore, there is a need to understand how much

75

exercisers are exposed to PM at locations and for time periods that exercises are commonly

76

practiced.

SC

RI PT

70

Macau is an autonomous territory on the western side of the Pearl River Delta in China (see

78

Figure S1), with a very high population density (21400 people per km2). It is composed of the

79

Macau Peninsula, the Taipa Island, and the Coloane Island. Several studies have been conducted

80

for air pollution in Macau. Wu et al. (2002) measured vertical and horizontal profiles of PM10,

81

PM2.5 and PM1 near major roads in Macau. Song et al. (2014) performed chemical

82

characterization of PM2.5 at a near-road site in Macau and showed size-resolved chemical

83

composition in PM2.5. Shao et al. (2013) studied the toxicity of inhalable particulates in Macau

84

by plasmid DNA assay, and found that the oxidative capacity of PM10 in the Macau Peninsula

85

was higher than that of Taipa Island. However, studies on exposure to PM2.5 and BC are still

86

very limited in Macau, not to mention specifically for exposure to exercisers. Besides, not many

87

current investigations are performed in various typical microenvironments as it was carried out

88

in this study. Our study covered (1) a suburban site (at the University of Macau) with low traffic

89

volume, (2) an elevated green-shaded jogging trail in downtown area, and (3) a lakeside path that

90

strings together the hills, the city expressway (more traffic) and the bay (Figure 1). In this study,

91

we aim at (1) measuring the mass concentrations of PM2.5 and BC in a number of popular

92

jogging trails, including the campus of University of Macau (UM), Guia Municipal Park (GP),

AC C

EP

TE D

M AN U

77

6

ACCEPTED MANUSCRIPT

and Saivan Lake (SL), (2) quantifying the corresponding PM2.5/BC exposure when doing

94

outdoor exercises such as jogging. Factors such as geographical location, traffic volume and air

95

mass origin are also discussed. Findings presented in this study may be used for reference by

96

other regions with similarly high population densities, emission strengths, and various

97

geographical complexities.

98

2.

Methodology

99

2.1

Sites Description and Experiment Design

SC

RI PT

93

Figure 1a shows the three measurement locations that are popular jogging trails in Macau.

101

Table 1 lists the measurements schedule at each site. Measurements were carried out both in the

102

morning and at night. Morning loops (07:30 to 09:00) coincide with rush hours and morning

103

jogging, while night loops (21:30 to 23:00) cover the time for night jogging. The campus of

104

University of Macau (UM) is separated from the Taipa Island by a waterway, with the Hengqin

105

Mountain to the southwest. A 20-m tall lakeside tower (UT) on the campus was chosen as the

106

reference site to demonstrate the difference of Pearson correlation coefficient (PCC) for BC and

107

PM2.5 among all the sites (see Table 2). For this purpose, 5-hour measurements (19:00 to 23:59)

108

were carried out alone on 29th August and 12th September at the top of UT, respectively. At UM,

109

one single roadside measurement looped 3.9 km in length, and two loops were carried out both in

110

the morning and at night. Another measurement location is the Guia Fitness Trail in the Guia

111

Municipal Park (GP), which is roughly 50 m above sea-level. Three-loop measurements were

112

carried out at the green-shaded GP trail both in the morning and at night with 1.9 km per loop.

113

The third measurement location is the Saivan Lake (SL), one of the two man-made lakes at the

114

southern tip of Macau Peninsula. The lake fronts the sea to the west and south, with low hills to

115

the north. Two-loop measurements were carried out both in the morning and at night with 2.6 km

AC C

EP

TE D

M AN U

100

7

ACCEPTED MANUSCRIPT

per loop. Background data of meteorological parameters and PM2.5 mass concentrations were

117

acquired from Taipa Grande (TG) station, obtained from the Macao Meteorological and

118

Geophysical Bureau (SMG). The station is 160 m above sea-level. Both GP and SL in the Macau

119

Peninsula are surrounded by main traffic roads (see Figure 1b and 1c), while UM is considered

120

as a suburban area although measurements were also made along the campus road with much

121

less traffic compared to the other two trails. In total, 92 trails have been performed in the 19-day

122

campaign with half measurements conducted in mornings and the others at nights.

123

2.2

SC

RI PT

116

M AN U

Instruments

PM2.5 mass concentrations were measured with a battery-operated, light-scattering laser

125

photometer (DustTrak™ II Aerosol Monitor 8530, TSI, USA). The DustTrak measures PM

126

concentrations from 0.001 to 400 mg/m3. Since optical measurements based on scattering have

127

strong dependence on both particle shape, particle density, and refractive index of PM, a

128

correction factor is required to convert photometric signals to mass concentrations. The

129

manufacturer suggested a correction factor of 0.38 for ambient aerosols, but a comparison

130

measurement between DustTrak and a SHARP PM2.5 analyzer (Thermo Fisher Scientific, USA)

131

was conducted (see Supporting Material) and a correction factor of 0.29 was chosen (Figure S2a).

132

A portable micro-aethalometer (microAeth AE51, Aethlabs, USA) was used to measure the

133

concentrations of BC, with a measurement resolution of 0.001 µg/m3 and measurement precision

134

of ± 0.1 µg/m3. The aethalometer samples particles on a filter strip. A beam of light is directed

135

on a spot of the particle-loaded filter and the attenuation of transmitted light (wavelength 880 nm)

136

is continuously recorded. The optical absorption measured continuously is thus proportional to

137

the light-absorbing materials in PM collected (Hansen et al., 1984). The filter strip was changed

138

every 12 working-hours to minimize the loading effect. Comparison measurements between a

AC C

EP

TE D

124

8

ACCEPTED MANUSCRIPT

139

multi-wavelengths aethalometer Model AE31 (Magee Scientific, USA) and our AE51 were also

140

carried and a slope of nearly unity was found (see Supporting Material). A GPS device (Dora G120, UniStrong, China) was used to record the position information.

142

The time of all instruments was synchronized to the same computer each day. Instruments were

143

packed into a backpack for measurements and the time resolution of the DustTrak and AE51

144

were both 1 min. The flow rate was set at 3 L/min for DustTrak, and 50 mL/min for AE51

145

(connected to a PM2.5 Cyclone). Zero calibration was performed for DustTrak with a HEPA filter

146

attached to the inlets before each measurement. Meanwhile, 15-minutes warming-up-sampling

147

was conducted for AE51 before each measurement. Concentrations of PM2.5 and BC were

148

recorded while the investigator was walking along the jogging trail with the instruments inside a

149

backpack and the inlets set to a height near the breathing zone (~ 1.6 m above ground).

150

3.

Results and Discussion

151

3.1

Comparison between Jogging Trails

TE D

M AN U

SC

RI PT

141

Figure 2 gives the comparison of PM2.5 concentrations along the jogging trails and at the

153

background station (TG). Ratios of PM2.5 average concentrations between trails and TG were

154

shown in Table 4. In terms of mean values, the PM2.5 concentrations at the UM trail were mostly

155

higher than those at TG (the ratios of PM2.5 between UM and TG were mostly larger than 1),

156

while the reverse is mostly true for the GP trail (most ratios less than 1). The comparison

157

between the SL trail and the TG station was somewhere in between (see Table 4). Moreover, the

158

linear fitting is applied using hourly PM2.5 concentrations to estimate the impact of background

159

(TG) PM2.5 concentrations on jogging trails (see Figure S4). Compared to the TG values, slopes

160

for each trail are as follows: 1.22 ± 0.02 at UM, 0.97 ± 0.02 at GP and 1.10 ± 0.02 at SL. Located

AC C

EP

152

9

ACCEPTED MANUSCRIPT

in the downtown area, GP was surprisingly the least polluted jogging trail. This result thus

162

suggests the impact of geographic environments (e.g. elevation, vegetation and emission strength)

163

on the accumulation and dispersion of PM2.5, in spite of the anticipated high levels of local

164

emission from traffics. This finding is different from that by Brantley et al., (2014), in which

165

marked diurnal variance was observed in both particle (0.5–10 µm aerodynamic diameter)

166

number concentration and BC mass concentration monitored behind the tree stand that separated

167

a six-lane highway. The authors indicated a positive correlation between traffic volume and the

168

particle concentrations. The inconsistency could be partly attributed to the different time periods

169

of studies, i.e. morning and night measurements in our study versus continuous observation in

170

Brantley et al., (2014). In addition, the elevated terrain of GP (a hillside belt trail) significantly

171

differs from relatively flat terrain near the interstate highway investigated by Brantley et al.,

172

(2014). As dominant emission sources were always outside and beneath the hillside belt trail at

173

GP, diffusion of traffic-related pollutants may be significantly reduced by vegetation.

TE D

M AN U

SC

RI PT

161

For the measurements along jogging trails, mass concentrations of BC ranged from 0.4 to

175

19.5 µg/m³, with the average concentration and one standard deviation of 3.5 ± 2.3 µg/m³

176

(similarly hereinafter), and those of PM2.5 ranged from 2.9 to 84.1 µg/m³ (25.5 ± 14.9 µg/m³). In

177

comparison, the mass concentration of PM2.5 at the background station (TG) ranged from 1.0 to

178

77.0 µg/m³ (26.3 ± 15.3 µg/m³). The BC/PM2.5 mass ratio ranged from 0.016 to 0.448 (0.153 ±

179

0.078). GP had the lowest PM2.5 concentration (23.4 ± 13.1 µg/m³) and BC concentration (3.2 ±

180

1.7 µg/m³) among all three jogging trails (Table S3). Besides, the average PM2.5 concentration

181

(23.4 ± 13.1 µg/m³) at GP was lower than that of TG (26.3 ± 15.0 µg/m³). The average BC/PM2.5

182

mass ratio was the lowest at GP (0.145 ± 0.059) but the highest at UM (0.164 ± 0.095). The

183

variances of BC concentration, as shown by the coefficient of variance (CV) values, were lower

AC C

EP

174

10

ACCEPTED MANUSCRIPT

than those of PM2.5 at UM and GP, but the opposite is true for SL (Table S3). The higher

185

variance of BC compared to PM2.5 at SL can be attributed to the stronger traffic influence at SL,

186

which is adjacent to a busy arterial road (Avenida Panoramica do Lago Sai Van), as shown in

187

Figure 1c.

RI PT

184

Table 3 shows comparisons between morning loops and night loops for BC and PM2.5

189

concentrations, as well as BC/PM2.5 mass ratios. Also shown are the PM2.5 concentrations at the

190

background station (TG) for the same measurement periods as those for each jogging trail. For

191

most cases (13 measurements), mass concentrations of BC measured along all three jogging

192

trails (19 measurements in total) were higher during morning loops (which coincided with

193

morning rush hours) than those at night (see Figure 3 and Table 4). Only a few exceptions (6

194

measurements) were observed, which indicates that traffic emission governs the variance of BC

195

in jogging trails in Macau. The concentrations of BC at SL were less dynamic during night loops

196

than those from morning loops, as indicated by significantly smaller CV value (0.57 over 0.69).

197

The concentrations of PM2.5 at UM were more dynamic during night loops than those during

198

morning loops, as indicated by a significantly higher CV value (0.68 over 0.56). The BC/PM2.5

199

ratios were relatively constant between morning and night loops in terms of the averaging values.

200

One exception is that the BC/PM2.5 ratio at UM was much lower during night loops (0.119) than

201

in the morning loops (0.223). The high BC/PM2.5 ratio in the morning at UM could be attributed

202

to the traffic flow pattern on campus. The traffic volumes were relatively high in the morning

203

rush hours, but traffic was almost diminished at night at UM. This startling difference in traffic

204

volumes between morning and night does not occur for the other two jogging rails (GP and SL).

205

This observation thus suggests that traffic emission is a major contributor of BC in jogging trails

206

in Macau, which is consistent with the previous study conducted by Song et al. (2014b) in Macau.

AC C

EP

TE D

M AN U

SC

188

11

ACCEPTED MANUSCRIPT

In their work, day-night variation of BC was found to be in good accordance with the diurnal

208

variations of traffic flow. Similar conclusion was underlined by Liu et al. (2016) that traffic-

209

related emissions were the dominant source of BC in Beijing throughout the whole year.

210

Likewise, they also presented a large variation in equivalent BC/PM2.5 ratios (0.2% to 26.9%)

211

and attributed it to the seasonal differences in emissions.

212

3.2

SC

Spatial Distribution of BC and PM2.5

RI PT

207

The geographic location may affect either accumulation or dispersion of air pollutants, thus

214

making the distribution of BC and PM2.5 differs even within a single loop of measurement.

215

Figure 4 depicts the average concentrations of BC and PM2.5 along the jogging loops for UM, GP,

216

and SL (both morning loops and night loops). Figure 5 shows more detailed comparisons of BC

217

and PM2.5 at different locations within the same jogging trail. The locations are segments of

218

loops. At UM, location u2 had the highest average BC (4.0 µg/m³) and PM2.5 (28.7 µg/m³)

219

concentrations. This high PM concentration at location u2 of UM could be attributed to the

220

emissions from the bus terminal located at the northeast corner (location u2) of the campus, as

221

well as more suppressed diffusion of particles due to higher buildings nearby. The highest

222

average BC concentration was measured at location g2 (3.4 µg/m³) for the GP trail, but the PM2.5

223

concentrations remained more or less the same for the three locations at GP. Location g2 of the

224

GP trail is close to another busy arterial road (Avenida do Dr. Rodrigo Rodrigues, see Figure 1b),

225

which strongly affects the BC concentration even at this elevated site. PM2.5, on the other hand,

226

is less affected by the road, probably because PM2.5 is more homogeneous due to regional-scale

227

influence. The highest average BC concentration was measured at location s3 (4.8 µg/m³) for the

228

SL trail, and the highest average PM2.5 concentration was also measured at location s3 (29.0

229

µg/m³). The busy road (Avenida Panoramica do Lago Sai Van) is neighboring the south (location

AC C

EP

TE D

M AN U

213

12

ACCEPTED MANUSCRIPT

s2) to the east (location s3) of SL, but stacks of tall buildings on the east (location s3) may

231

prevent efficient dispersion of PM, resulting in higher concentrations of BC and PM2.5 at location

232

s3. It is thus clear from the above analyses that both emission strength and dispersion capability

233

affect the air pollutant concentration in urban settings.

234

3.3

RI PT

230

Exposure to PM2.5 and BC during Jogging Exercise

The term ‘human exposure’ was firstly introduced by Duan (1982) and Ott (1982) more than

236

thirty years ago. Currently, many studies use ‘average exposure’ to quantify the level of

237

pollutants in the ambient air over a specific period of time. On the other hand, ‘integrated

238

exposure’ is more appropriate to assess the inhalation of PM by joggers during physical activities.

239

Average exposure is deduced using average concentrations of pollutants within a time interval,

240

while integrated exposure is calculated by integrating the concentration over time (Monn, 2001).

241

The risk of exposure is then evaluated by further examining how much ‘dose’ of pollutants is

242

deposited in the body as a function of time spent.

TE D

M AN U

SC

235

Watson et al. (1988) suggested that exposure requires the simultaneous occurrence of two

244

events: a pollutant concentration at a particular place and time, and the presence of a person at

245

that place and time. On the other hand, dose is the amount of the pollutant that actually crosses

246

the boundaries and reaches the target tissue of the exposure individual. The relationship of

247

exposure and dose is defined by the following equation:

AC C

EP

243

=

248

×

(∆ )

249

where

represents the average inhalation dose, µg;

250

specified microenvironment and time, µg/m3;

251

the time people are exposed to the pollutant during a journey, h.

(∆ )

is the pollutant concentration at a

is the respiratory rate, m3/h; and indicates

13

ACCEPTED MANUSCRIPT

For the exposure estimation of this study, the measured average concentrations of BC and

253

PM2.5 were 3.5±2.3 µg/m³ and 25.5±14.9 µg/m³, respectively. These concentrations were in

254

broad agreement with those in other studies in London (Kaur et al., 2005), Athens and Barcelona

255

(Ostro et al., 2015), Nairobi (Ngo et al., 2015), Minneapolis (Hankey and Marshall, 2015),

256

Montreal (Weichenthal et al., 2014), New York (Vilcassim et al., 2014), Shanghai (Lei et al.,

257

2016; Li et al., 2015), and Brisbane (Williams and Knibbs, 2016). Table 5 shows the comparison

258

of the concentrations for BC and PM2.5 in different cities. Note that most of those studies were

259

not meant for investigations of potential exposure during physical exercise.

SC

RI PT

252

An inhalation rate of 1.62 m3/h (0.027 m3/min) was used in this study as recommended in the

261

Risk Information by the US Environmental Protection Agency for moderate intensity of activity

262

level (aging from 21 to 51 years old) (US EPA, 2011). The calculated dose for jogging for 60

263

min on each jogging trail were shown as Table 6. In this comparison, jogging at GP would have

264

the lowest dose for BC (5.7 µg in the morning and 4.5 µg at night), while the highest dose would

265

be expected at SL for both morning and night loops (7.9 µg and 5.7 µg, respectively). For PM2.5,

266

dose at SL would still be the highest (52.0 µg) in the morning. However, UM would have the

267

highest dose at night (51.6 µg) for PM2.5. In a previous study, the reported dose of BC for

268

travelling about 3.0 to 3.5 km were 1.58 ± 0.29 µg (walking), followed by bus, cycling and

269

subway, with inhalation doses of 1.50 ± 0.39 µg, 1.36 ± 0.37 µg, and 0.95 ± 0.29 µg, respectively

270

(Li et al., 2015). The noticeably lower value of inhalation dose in that study could mainly be

271

attributed to the significantly lower respiratory rates used (0.47, 0.63 and 0.70 m3/h while seated

272

or standing, walking and cycling, respectively). Outdoor exercising in polluted urban

273

environments thus can result in significant higher air pollution exposure than at rest.

274

4.

AC C

EP

TE D

M AN U

260

Conclusion 14

ACCEPTED MANUSCRIPT

This study presents multi-day (5 – 8 days per site, morning + night) measurements of

276

concentrations of BC and PM2.5 at three jogging trails in Macau for exposure estimation. It has

277

been revealed by many previously mentioned studies that the distribution of PM is primarily

278

governed by local emissions and meteorological conditions. We show in this study the potential

279

exposure to PM2.5 and BC in typical jogging trails in an urban environment when people are

280

exercising on those trails. Results showed that the green-shaded elevated Guia Park trail (GP)

281

was under less exposure risk. BC and PM2.5 concentrations were generally lower in the night

282

loops (21:30 – 23:00) than those in the morning loops (7:30 -9:00) that coincide with morning

283

rush hours. Observations from this study suggest that traffic emissions are the major contributor

284

to PM, especially BC, at all three studied jogging trails in Macau. Geographical locations as

285

related to the distance to major roads, together with the accumulation and dispersion mechanisms

286

result in a complex distribution pattern of PM pollutants in urban micro-environments. By

287

comparing the exposure to BC and PM2.5 among different locations, the dominant effect of

288

traffic emissions and geographical locations were revealed. Comparing the dose of BC and PM2.5

289

during outdoor exercising such as jogging to that at rest, higher exposure to these air pollutants is

290

expected during outdoor exercise.

EP

TE D

M AN U

SC

RI PT

275

This study was carried out over a limited number of days in summer and early autumn only.

292

In addition, a time resolution of 1 min was selected to minimize noise (especially for BC with the

293

AE51) during measurements. Additional studies can be designed and performed in other cities,

294

for other seasons, on other exercising facilities, and for shorter time resolutions to capture the

295

rapid changing PM pollution and to understand more comprehensively PM exposure during

296

exercise. Nevertheless, this study quantified the exposure risk and estimated exposure dose for

297

major outdoor activity venues in Macau, providing information both on personal arrangement for

AC C

291

15

ACCEPTED MANUSCRIPT

time/venue for exercise and on abatement policy to mitigate such risks. In addition, as all the

299

trails are typical venues for outdoor exerciser (especially joggers and walkers) in the highly

300

populated city of Macau, and similar situations may be expected in other highly populated cities.

301

Hence, this study would enrich our understanding of exercisers’ exposure to PM in many urban

302

areas of the world.

303

Acknowledgments

SC

RI PT

298

The work was supported by the Start-up Research Grant (SRG2015-00052-FST) and Multi-

305

Year Research grant (MYRG2017-00044-FST) from University of Macau. The authors are

306

grateful to the Macau Meteorological and Geophysical Bureau (SMG) for providing

307

meteorological data and background PM2.5 concentration data.

AC C

EP

TE D

M AN U

304

16

ACCEPTED MANUSCRIPT

Reference

309 310 311

Bell, M.L., Ebisu, K., Peng, R.D., Samet, J.M., Dominici, F., 2009. Hospital admissions and chemical composition of fine particle air pollution. Am. J. Respir. Crit. Care Med. 179, 1115–1120. doi:10.1164/rccm.200808-1240OC

312 313 314 315 316 317 318

Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., Deangelo, B.J., Flanner, M.G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P.K., Sarofim, M.C., Schultz, M.G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S.K., Hopke, P.K., Jacobson, M.Z., Kaiser, J.W., Klimont, Z., Lohmann, U., Schwarz, J.P., Shindell, D., Storelvmo, T., Warren, S.G., Zender, C.S., 2013. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos. 118, 5380–5552. doi:10.1002/jgrd.50171

319 320 321 322

Braniš, M., Vyškovská, J., Malý, M., Hovorka, J., 2010. Association of size-resolved number concentrations of particulate matter with cardiovascular and respiratory hospital admissions and mortality in Prague, Czech Republic. Inhal. Toxicol. 22 Suppl 2, 21–28. doi:10.3109/08958378.2010.504758

323 324 325

Brantley, H.L., Hagler, G.S.W., J. Deshmukh, P., Baldauf, R.W., 2014. Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Sci. Total Environ. 468–469, 120–129. doi:10.1016/j.scitotenv.2013.08.001

326 327 328

Che, W.W., Frey, H.C., Lau, A.K.H., 2016. Sequential Measurement of Intermodal Variability in Public Transportation PM2.5 and CO Exposure Concentrations. Environ. Sci. Technol. 50, 8760–8769. doi:10.1021/acs.est.6b01594

329 330

de Hartog, J.J., Boogaard, H., Nijland, H., Hoek, G., 2010. Do the health benefits of cycling outweigh the risks? Environ. Health Perspect. doi:10.1289/ehp.0901747

331 332 333 334

Deng, W.J., Zheng, H.L., Tsui, A.K.Y., Chen, X.W., 2016. Measurement and health risk assessment of PM2.5, flame retardants, carbonyls and black carbon in indoor and outdoor air in kindergartens in Hong Kong. Environ. Int. 96, 65–74. doi:10.1016/j.envint.2016.08.013

335 336

Duan, N., 1982. Models for human exposure to air pollution. Environ. Int. doi:10.1016/01604120(82)90041-1

337 338 339

Fruin, S.A., Winer, A.M., Rodes, C.E., 2004. Black carbon concentrations in California vehicles and estimation of in-vehicle diesel exhaust particulate matter exposures. Atmos. Environ. 38, 4123–4133. doi:10.1016/j.atmosenv.2004.04.026

340 341 342

Gerharz, L.E., Kr??ger, A., Klemm, O., 2009. Applying indoor and outdoor modeling techniques to estimate individual exposure to PM2.5 from personal GPS profiles and diaries: A pilot study. Sci. Total Environ. 407, 5184–5193. doi:10.1016/j.scitotenv.2009.06.006

343 344 345

Goel, R., Gani, S., Guttikunda, S.K., Wilson, D., Tiwari, G., 2015. On-road PM2.5 pollution exposure in multiple transport microenvironments in Delhi. Atmos. Environ. 123, 129–138. doi:10.1016/j.atmosenv.2015.10.037

346 347 348

Ham, W., Vijayan, A., Schulte, N., Herner, J.D., 2017. Commuter exposure to PM2.5, BC, and UFP in six common transport microenvironments in Sacramento, California. Atmos. Environ. 167, 335–345. doi:10.1016/j.atmosenv.2017.08.024

AC C

EP

TE D

M AN U

SC

RI PT

308

17

ACCEPTED MANUSCRIPT

Hankey, S., Marshall, J.D., 2015. On-bicycle exposure to particulate air pollution: Particle number, black carbon, PM2.5, and particle size. Atmos. Environ. 122, 65–73. doi:10.1016/j.atmosenv.2015.09.025

352 353 354

Hansen, A.D.A., Rosen, H., Novakov, T., 1984. The aethalometer — An instrument for the realtime measurement of optical absorption by aerosol particles. Sci. Total Environ. doi:10.1016/0048-9697(84)90265-1

355 356

Janssen, N.A., Gerlofs-Nijland, M.E., Lanki, T., Salonen, R.O., Cassee, F., Hoek, G., Fischer, P., Brunekreef, B., Krzyzanowski, M., 2012. Health effects of black carbon. Copenhagen.

357 358 359

Jeong, H., Park, D., 2017. Contribution of time-activity pattern and microenvironment to black carbon (BC) inhalation exposure and potential internal dose among elementary school children. Atmos. Environ. 164, 270–279. doi:10.1016/j.atmosenv.2017.06.007

360 361 362

Jinsart, W., Tamura, K., Loetkamonwit, S., Thepanondh, S., Karita, K., Yano, E., 2002. Roadside particulate air pollution in Bangkok. J. Air Waste Manag. Assoc. 52, 1102–1110. doi:10.1080/10473289.2002.10470845

363 364 365

Kaur, S., Nieuwenhuijsen, M.J., Colvile, R.N., 2005. Pedestrian exposure to air pollution along a major road in Central London, UK. Atmos. Environ. 39, 7307–7320. doi:10.1016/j.atmosenv.2005.09.008

366 367 368

Lei, X., Xiu, G., Li, B., Zhang, K., Zhao, M., 2016. Individual exposure of graduate students to PM2.5 and black carbon in Shanghai, China. Environ. Sci. Pollut. Res. 23, 12120–12127. doi:10.1007/s11356-016-6422-x

369 370 371

Li, B., Lei, X. ning, Xiu, G. li, Gao, C. yuan, Gao, S., Qian, N. sheng, 2015. Personal exposure to black carbon during commuting in peak and off-peak hours in Shanghai. Sci. Total Environ. 524–525, 237–245. doi:10.1016/j.scitotenv.2015.03.088

372 373 374

Li, Y., Henze, D.K., Jack, D., Henderson, B.H., Kinney, P.L., 2016. Assessing public health burden associated with exposure to ambient black carbon in the United States. Sci. Total Environ. 539, 515–525. doi:10.1016/j.scitotenv.2015.08.129

375 376 377

Li, Y.J., Sun, Y., Zhang, Q., Li, X., Li, M., Zhou, Z., Chan, C.K., 2017. Real-time chemical characterization of atmospheric particulate matter in China: A review. Atmos. Environ. doi:10.1016/j.atmosenv.2017.02.027

378 379 380

Liu, Q., Ma, T., Olson, M.R., Liu, Y., Zhang, T., Wu, Y., Schauer, J.J., 2016. Temporal variations of black carbon during haze and non-haze days in Beijing. Sci. Rep. 6. doi:10.1038/srep33331

381 382 383

Martins, V., Moreno, T., Minguillón, M.C., Amato, F., de Miguel, E., Capdevila, M., Querol, X., 2015. Exposure to airborne particulate matter in the subway system. Sci. Total Environ. 511, 711–722. doi:10.1016/j.scitotenv.2014.12.013

384 385 386

Monn, C., 2001. Exposure assessment of air pollutants: A review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone. Atmos. Environ. 35, 1–32. doi:10.1016/S1352-2310(00)00330-7

387 388 389

Ngo, N.S., Gatari, M., Yan, B., Chillrud, S.N., Bouhamam, K., Kinney, P.L., 2015. Occupational exposure to roadway emissions and inside informal settlements in sub-Saharan Africa: A pilot study in Nairobi, Kenya. Atmos. Environ. 111, 179–184.

AC C

EP

TE D

M AN U

SC

RI PT

349 350 351

18

ACCEPTED MANUSCRIPT

390

doi:10.1016/j.atmosenv.2015.04.008 Ostro, B., Tobias, A., Karanasiou, A., Samoli, E., Querol, X., Rodopoulou, S., Basagaña, X., Eleftheriadis, K., Diapouli, E., Vratolis, S., Jacquemin, B., Katsouyanni, K., Sunyer, J., Forastiere, F., Stafoggia, M., 2015. The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project. Occup. Environ. Med. 72, 123–129. doi:10.1136/oemed-2014-102184

396 397

Ott, W.R., 1982. Concepts of human exposure to air pollution. Environ. Int. doi:10.1016/01604120(82)90104-0

398 399 400 401

Patel, M.M., Chillrud, S.N., Correa, J.C., Feinberg, M., Hazi, Y., Deepti, K.C., Prakash, S., Ross, J.M., Levy, D., Kinney, P.L., 2009. Spatial and temporal variations in traffic-related particulate matter at New York City high schools. Atmos. Environ. 43, 4975–4981. doi:DOI 10.1016/j.atmosenv.2009.07.004

402 403 404

Rao, S., Pachauri, S., Dentener, F., Kinney, P., Klimont, Z., Riahi, K., Schoepp, W., 2013. Better air for better health: Forging synergies in policies for energy access, climate change and air pollution. Glob. Environ. Chang. 23, 1122–1130. doi:10.1016/j.gloenvcha.2013.05.003

405 406 407 408

Rivas, I., Donaire-Gonzalez, D., Bouso, L., Esnaola, M., Pandolfi, M., de Castro, M., Viana, M., Àlvarez-Pedrerol, M., Nieuwenhuijsen, M., Alastuey, A., Sunyer, J., Querol, X., 2016. Spatiotemporally resolved black carbon concentration, schoolchildren’s exposure and dose in Barcelona. Indoor Air 26, 391–402. doi:10.1111/ina.12214

409 410 411

Shao, L.Y., Shen, R.R., Wang, J., Wang, Z.S., Tang, U., Yang, S.S., 2013. A toxicological study of inhalable particulates by plasmid DNA assay: A case study from Macao. Sci. China Earth Sci. 56, 1037–1043. doi:10.1007/s11430-013-4581-x

412 413 414

Song, S., Wu, Y., Zheng, X., Wang, Z., Yang, L., Li, J., Hao, J., 2014. Chemical characterization of roadside PM2.5 and black carbon in Macao during a summer campaign. Atmos. Pollut. Res. 5, 381–387. doi:10.5094/APR.2014.044

415 416 417

Suglia, S.F., Gryparis, A., Schwartz, J., Wright, R.J., 2008. Association between traffic-related black carbon exposure and lung function among urban women. Environ. Health Perspect. 116, 1333–1337. doi:10.1289/ehp.11223

418 419 420

Tainio, M., de Nazelle, A.J., Götschi, T., Kahlmeier, S., Rojas-Rueda, D., Nieuwenhuijsen, M.J., de Sá, T.H., Kelly, P., Woodcock, J., 2015. Can air pollution negate the health benefits of cycling and walking? Prev. Med. (Baltim). 87, 233–236. doi:10.1016/j.ypmed.2016.02.002

421

US EPA, 2011. Exposure Factors Handbook.

422 423 424

Vilcassim, M.J.R., Thurston, G.D., Peltier, R.E., Gordon, T., 2014. Black Carbon and Particulate Matter (PM 2.5 ) Concentrations in New York City’s Subway Stations. Environ. Sci. Technol. 48, 14738–14745. doi:10.1021/es504295h

425 426

Watson, A.Y., Bates, R.R., Kennedy, D., 1988. Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models, Air Pollution, the Automobile, and Public Health.

427 428 429 430

Weichenthal, S., Hatzopoulou, M., Goldberg, M.S., 2014. Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study. Part Fibre Toxicol 11, 70. doi:10.1186/s12989-014-0070-4

AC C

EP

TE D

M AN U

SC

RI PT

391 392 393 394 395

19

ACCEPTED MANUSCRIPT

Williams, R.D., Knibbs, L.D., 2016. Daily personal exposure to black carbon: A pilot study. Atmos. Environ. 132, 296–299. doi:10.1016/j.atmosenv.2016.03.023

433 434 435

Wilson, W.E., Brauer, M., 2006. Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study. J. Expo. Sci. Environ. Epidemiol. 16, 264–274. doi:10.1016/j.jhin.2005.11.001

436 437 438

Wu, Y., Hao, J., Fu, L., Wang, Z., Tang, U., 2002. Vertical and horizontal profiles of airborne particulate matter near major roads in Macao, China. Atmos. Environ. 36, 4907–4918. doi:10.1016/S1352-2310(02)00467-3

439 440 441

Zhao, P., Zhang, X., Xu, X., Zhao, X., 2011. Long-term visibility trends and characteristics in the region of Beijing, Tianjin, and Hebei, China. Atmos. Res. 101, 711–718. doi:10.1016/j.atmosres.2011.04.019

SC

RI PT

431 432

442

AC C

EP

TE D

M AN U

443

20

ACCEPTED MANUSCRIPT

444

Tables

445

Table 1. A summary of the measurement periods. 24/08/2016

19/09/2016

05/11/2016

End

03/09/2016

03/11/2016

Loop 1

07:32:00 - 08:18:00

07:40:00 - 08:02:00

07:30:00 - 07:58:30

Loop 2

08:18:00 - 09:04:00

08:02:00 - 08:25:00

07:59:00 - 08:27:30

Loop 3

21:32:00 - 22:18:00

08:25:00 - 08:47:00

21:30:00 - 21:58:30

Loop 4

22:18:00 - 23:04:00

21:40:00 - 22:02:00

21:59:00 - 22:27:30

Loop 5

-

22:02:00 - 22:25:00

-

Loop 6

-

22:25:00 - 22:47:00

-

21/11/2016

Table 2. Pearson correlation coefficient (Rpr), 2-tailed sigma (σ) and number of data points (N) for BC and PM2.5 among all the sites. Numbers with “**” denotes that correlation is significant at the 0.01 level (2-tailed).

**

567 ** .882 .000 567

.882 .000 567 1

BC 1

775 .653** .000 775

TE D

PM2.5

Rpr σ N Rpr σ N

UT PM2.5

BC 1

567

UM PM2.5 .653** .000 775 1 775

BC 1

1078 .651** .000 1078

GP PM2.5 .651** .000 1078 1 1078

BC 1 639 .625** .000 639

SL PM2.5 .625** .000 639 1 639

EP

BC

449

Start

AC C

446 447 448

SL

RI PT

Measurements Schedule

GP

SC

Measurements Period

UM

M AN U

Schedule

21

ACCEPTED MANUSCRIPT

Table 3. Summary of BC and PM2.5 mass concentrations (µg/m³), as well as BC/PM2.5 mass ratios, between morning loops and night loops at various sites. UM stands for University of Macau, GP stands for Guia Municipal Park, SL stands for Saivan Lake, and TG stands for the Taipai Grande Station which is regarded as the background station. CV

Morning

Night

Morning

Night

Morning

Night

Morning

Night

BC

0.4

0.4

13.0

12.7

3.9

3.3

2.6

2.3

0.66

0.70

PM2.5

2.9

3.5

35.4

84.1

19.5

31.5

10.9

21.4

0.56

0.68

0.019

0.020

0.448

0.438

0.223

0.119

0.10

0.06

0.45

0.51

0.5

0.4

13.8

8.2

3.5

2.8

1.9

1.4

0.54

0.51

15.7

8.7

63.5

55.4

26.0

0.025

0.016

0.438

0.402

0.146

BC

0.5

0.4

19.5

17.4

4.8

PM2.5

8.4

5.2

58.3

52.5

0.016

0.017

0.432

1.0

1.0

77.0

PM2.5 BC/PM2.5

BC/PM2.5 TG

SD

Night

BC

SL

Average

Morning

BC/PM2.5

GP

Max

PM2.5

20.9

14.0

11.6

0.54

0.55

0.144

0.07

0.05

0.47

0.34

3.5

3.3

2.0

0.69

0.57

31.8

24.1

13.0

9.3

0.41

0.38

0.427

0.154

0.155

0.08

0.08

0.55

0.49

51.7

29.3

23.9

17.20

12.88

0.59

0.54

AC C

EP

TE D

454

SC

UM

Min

RI PT

Location

M AN U

450 451 452 453

22

ACCEPTED MANUSCRIPT

Table 4. Summary of BC and PM2.5 mass concentrations (µg/m³) for each measurement. UM stands for University of Macau, GP stands for Guia Municipal Park, and SL stands for Saivan Lake BC (mean ± SD)

SL

458

PM2.5 Ratio (Trails / TG) Morning Night

Night

Morning

Night

2016/08/24

4.4±2.5

2.0±0.0

18.8±4.1

20.0±1.8

19.7

19.0

0.95

1.05

2016/08/25

4.9±2.5

2.2±0.0

26.8±4.7

29.8±4.5

23.7

23.3

1.13

1.28

2016/08/28

5.4±2.2

4.0±0.0

30.6±2.3

28.9±5.0

22.0

31.0

1.39

0.93

2016/09/02

1.0±0.5

1.0±0.1

3.9±1.0

5.3±0.8

-

-

-

-

2016/09/03

2.8±1.4

6.7±0.0

8.6±2.9

68.7±5.0

3.0

47.7

2.87

1.44

overall

3.9±2.6

3.3±0.1

19.5±10.9

31.5±21.4

13.5

24.4

1.44

1.29

2016/09/19

3.2±1.3

2.0±0.5

19.9±0.8

12.5±3.4

37.0

17.0

0.54

0.74

2016/09/22

3.1±1.8

3.5±0.8

20.5±1.5

20.3±2.2

24.0

18.7

0.85

1.09

2016/09/24

3.0±1.7

2.1±0.9

17.4±0.8

23.2±1.0

19.7

26.7

0.88

0.87

2016/09/26

3.7±1.7

5.7±1.3

31.5±1.5

49.1±2.4

40.3

51.7

0.78

0.95

2016/09/28

6.0±2.5

3.0±1.0

60.3±2.1

19.2±0.5

77.0

-

0.78

-

2016/10/23

3.1±1.0

2.0±0.5

18.9±0.9

14.3±0.9

15.7

11.3

1.20

1.27

2016/10/26

3.4±1.5

1.8±0.6

19.3±1.0

10.4±0.6

24.0

12.0

0.95

0.87

2016/11/03

2.6±0.8

2.4±0.2

18.5±0.6

17.6±0.4

21.3

17.0

0.87

1.04

overall 2016/11/05 2016/11/07

3.5±1.9 7.8±3.9 6.2±2.9

2.8±1.4 3.7±1.5 2.3±1.0

26.0±14.0 49.8±4.4 40.1±3.1

20.9±11.6 28.0±3.2 22.7±1.6

32.4 51.3 42.0

23.4 29.7 15.7

0.80 0.97 0.95

0.89 0.94 1.45

2016/11/09

2.8±1.5

3.1±1.2

19.2±0.7

16.3±1.0

18.7

15.7

1.03

1.04

2016/11/16 2016/11/18 2016/11/21 overall

5.1±3.6 4.4±1.9 1.9±1.0 4.8±3.3

4.1±2.3 5.3±2.3 1.8±0.8 3.5±2.0

31.5±2.2 34.6±2.0 9.5±0.8 31.8±13.0

28.9±3.7 35.2±6.4 6.9±2.0 24.1±9.2

35.0 43.0 4.0 32.3

31.7 31.7 5.3 21.6

0.90 0.80 2.38 0.98

0.91 1.11 1.30 1.12

SC

RI PT

Morning

TE D

GP

PM2.5 of TG (mean) Morning Night

AC C

UM

PM2.5 (mean ± SD)

Date

EP

Location

M AN U

455 456 457

23

ACCEPTED MANUSCRIPT

Table 5. Comparisons of BC and PM2.5 mass concentrations in urban areas from different studies. In this study, the measured average concentrations of BC and PM2.5 were 4.0±2.6 µg/m³ and 25.7±13.7 µg/m³, respectively for morning loops, 3.1±1.9 µg/m³ and 25.7±13.7 µg/m³, respectively for night loops.

London, UK

PM2.5 (µg/m³)

2004

BC (µg/m³)

37.7±16.4

-

Kaur, Nieuwenhuijsen, and Colvile 2005

2008-2009

22.0 (5.0-62.5)

0.7 (0.05-3.3)

Ostro et al. 2015

Barcelona, Spain

2009-2010

17.7 (1.9-59.0)

2.0 (0.06-8.7)

Ostro et al. 2015

69.7±21.6

30.0±12.7

Minneapolis, US

2012 (morning rushhour) 2012 (afternoon rushhour) 2013

2.5±1.4

Hankey and Marshall 2015

9.1±6.3

0.7±0.6

1.7±1.4

Weichenthal et al. 2014

9.8±4.8

2.3±1.2

Vilcassim et al. 2014

210±48

5.19±1.58

144.0±53.2

5.7±3.4

Lei et al. 2016

-

1.1±3.8

Williams & Knibbs 2016

2016 (morning loops)

25.7±13.7

4.0±2.6

2016 (night loops)

25.2±15.9

3.1±1.9

2013-2014 2014

Shanghai, China

2014-2015 2015

TE D

Macau, China

10.5±4.8

14.2±13

Shanghai, China

Brisbane, Australia

Ngo et al. 2015

SC

2011

M AN U

Nairobi, Kenya

New York, US

Li et al. 2015

This study

Table 6. Dose of BC and PM2.5 quantified according to jogging speed (8 km/h) and inhalation rate (0.027 m3/min) for 60 minutes at each site. UM

Jogging Trail

AC C

Morning

465

Reference

Athens, Greece

Montreal, Canada

463 464

Study Year

RI PT

City

EP

459 460 461 462

GP Night

Morning

SL Night

Morning

Night

BC (µg)

6.6

5.3

5.7

4.5

7.9

5.7

PM2.5 (µg)

32.9

51.6

42.3

33.8

52.0

39.3

24

ACCEPTED MANUSCRIPT

Figures

475

Figure 1. (a) Locations of the three jogging trails and the background station in Macau. UM stands for University of Macau, GP stands for Guia Municipal Park, SL stands for Saivan Lake, and TG stands for the Taipai Grande Station which is regarded as the background station, UT stands for the 20-m tall lakeside tower on the campus which was chosen as the reference site for the Pearson correlation coefficient demonstration. Also given are the segment labels of each jogging trail. (b) and (c) are the locations of GP and SL, that are both adjacent to one of the city’s busiest road.

EP

468 469 470 471 472 473 474

AC C

467

TE D

M AN U

SC

RI PT

466

25

EP

Figure 2. Comparison of PM2.5 concentrations along the jogging trails and at the TG station. The measurement periods s for each jogging trails are: UM (2016-8-24 to 2016-09-03), GP (2016-0919 to 2016-11-03), SL (2016-11-05 to 2016-11-21). The box-whisker plot here presents five sample statistics: the minimum, the lower quartile, the median, the upper quartile and the maximum. Dots present values that are 1.5-interquartile-range (IQR) smaller or larger than the lower quartile or upper quartile, respectively. Stars present values that are 3-IQR smaller or larger than the lower quartile or upper quartile, respectively.

AC C

476 477 478 479 480 481 482 483 484

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

26

485 486 487 488 489 490 491

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure 3. Comparison of BC and PM2.5 concentrations, and the ratio of BC/PM2.5 between morning and night loops. The box-whisker plot here presents five sample statistics: the minimum, the lower quartile, the median, the upper quartile and the maximum. Dots present values that are 1.5-interquartile-range (IQR) smaller or larger than the lower quartile or upper quartile, respectively. Stars present values that are 3-IQR smaller or larger than the lower quartile or upper quartile, respectively. 27

492 493 494 495

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure 4. The average concentrations of BC ((µg/m³)) and PM2.5 ((µg/m³)) between morning and night loops along the jogging trails in UM, GP and SL, respectively. Time interval for each data point is 60s. Also given is the label for the specified locations of each jogging trail.

28

EP

497 498 499 500 501 502

Figure 5. BC (a) and PM2.5 (b) concentrations sampled at different locations of the loops for each jogging trail. See Figure 1a and Figure 4 for the labels of the locations. The box-whisker plot here presents five sample statistics: the minimum, the lower quartile, the median, the upper quartile and the maximum. Dots present values that are 1.5-interquartile-range (IQR) smaller or larger than the lower quartile or upper quartile, respectively. Stars present values that are 3-IQR smaller or larger than the lower quartile or upper quartile, respectively.

AC C

496

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

29

ACCEPTED MANUSCRIPT

RI PT

SC M AN U TE D

-

EP

-

We measured concentrations of PM2.5 and black carbon (BC) at three typical jogging trails in Macau, China. We showed that in addition to emission strengths and meteorological conditions, geographical locations and terrains are also important for air pollutant dispersion in urban microenvironments. We also demonstrated higher integrated exposures to PM2.5 and BC when performing physical exercise than at rest.

AC C

-