Implications of seasonal control of PM2.5-bound PAHs: An integrated approach for source apportionment, source region identification and health risk assessment

Accepted Manuscript Implications of seasonal control of PM2.5-bound PAHs: An integrated approach for source apportionment, source region identification and health risk assessment Sihong Chao, Jianwei Liu, Yanjiao Chen, Hongbin Cao, Aichen Zhang PII:

S0269-7491(18)34249-0

DOI:

https://doi.org/10.1016/j.envpol.2018.12.074

Reference:

ENPO 12014

To appear in:

Environmental Pollution

Received Date: 19 September 2018 Revised Date:

23 December 2018

Accepted Date: 23 December 2018

Please cite this article as: Chao, S., Liu, J., Chen, Y., Cao, H., Zhang, A., Implications of seasonal control of PM2.5-bound PAHs: An integrated approach for source apportionment, source region identification and health risk assessment, Environmental Pollution (2019), doi: https://doi.org/10.1016/ j.envpol.2018.12.074. 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

coal combustion

vehicle emission

RH

petroleum volatilization, natural gas and biomass combustion

Spring

Autumn

M AN U

Summer

SC

O3

Rainfall

Heating season

AC C

EP

Source apportionment by PMF Source region analysis by PSCF-CPF

TE D

WD/WS

RI PT

T

PM2.5-bound PAHs

source-attributed cancer risk was a better index for ranking priority control sources

ACCEPTED MANUSCRIPT 1

Title Page Implications of seasonal control of PM2.5-bound PAHs: An integrated

3

approach for source apportionment, source region identification and

4

health risk assessment

5

Sihong Chao#, Jianwei Liu#, Yanjiao Chen, Hongbin Cao*, Aichen Zhang

6

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

7

#

8

authors

RI PT

2

SC

These authors contributed equally to this work and should be regarded as co-first

9 E-mail address:

11

Sihong Chao: [email protected]

12

Jianwei Liu: [email protected]

13

Yanjiao Chen: [email protected]

14

Aichen Zhang: [email protected]

TE D

15

M AN U

10

* Corresponding author: Hongbin Cao

17

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

18

E-mail address: [email protected].

20 21

AC C

19

EP

16

22 23 24 1

ACCEPTED MANUSCRIPT Implication of seasonal control of PM2.5-bound PAHs: An integrated approach

26

for source apportionment, source region identification and health risk

27

assessment

28

Abstract: PM2.5-bound PAHs are ubiquitous in urban atmospheres and are characterized as

29

carcinogenic, teratogenic and mutagenic upon inhalation. A total of 218 daily PM2.5 samples were

30

collected during one year in the urban district of Beijing, China. Analysis showed that the annual

31

mean concentration of total PAHs (TPAHs) was 66.2 ng/m3, with benzo(a)pyrene (BaP)

32

accounting for 12.4%. High-molecular-weight (HMW, 4-6 rings) PAHs were the dominant

33

components. Seasonal TPAH concentrations decreased in the order of heating season (156 ng/m3) >

34

autumn (20.4 ng/m3) > spring (16.0 ng/m3) > summer (12.5 ng/m3) and were related to

35

meteorological conditions and source emission intensity. The source-attributed mass contribution

36

and source regions of three sources (i.e., (1) vehicle emissions; (2) coal combustion; and (3)

37

petroleum volatilization, natural gas and biomass combustion) were identified by integrating the

38

positive matrix factorization (PMF), potential source contribution function (PSCF) and conditional

39

probability function (CPF). Vehicle emissions contributed the most mass (54.6%), followed by

40

coal combustion (29.8%), on an annual basis. Combined with actual regional emissions, vehicle

41

emissions were mainly derived from local sources, while coal combustion mainly came from

42

regional transport from surrounding areas. Vehicle emissions and coal combustion have much

43

higher mass contributions in the heating season. The source-attributed cancer risk was further

44

evaluated based on source profiles and inhalation unit risk. Vehicle emissions contributed the

45

largest risk (2.8×10-6, accounting for 71%) as a result of 30 years of exposure for local residents,

46

exceeding the acceptable level (10-6). The heating season showed the most risk, especially in

47

response to vehicle emissions and coal combustion. Overall, the source-attributed cancer risk was

48

regarded as the better index for the development of a control strategy of PM2.5-bound PAHs for

49

protecting residents. Based on this index, priority control sources in each season were identified to

50

supply a more effective management solution.

51

Capsule

52

An integrated PMF-PSCF-CPF model was proposed to decrease the uncertainty of quantitative

53

source apportionment of PM2.5-bound PAHs and identify potential source regions, and

AC C

EP

TE D

M AN U

SC

RI PT

25

2

ACCEPTED MANUSCRIPT source-attributed health risk was suggested as a better index than mass contribution for ranking

55

priority control sources of PM2.5-bound PAHs.

56

Keywords

57

PAHs, Seasonal variation, Positive matrix factorization, Source-attributed cancer risk, Source

58

region

59

1. Introduction

RI PT

54

PAHs are ubiquitous in urban atmospheres. Low-molecular-weight (LMW) PAHs are

61

predominantly present in the gaseous phase. With the increase of molecular weight, PAHs tend to

62

exist in particulate phase (Chen et al., 2018). PM2.5 (aerodynamic dia. ≤2.5 µm)-bound PAHs can

63

penetrate deep into the lungs and blood streams unfiltered, inducing various adverse effects on the

64

human body, such as causing skin, bladder, lung and kidney cancer (Armstrong et al., 2004; Abbas

65

et al., 2018; Zhang et al., 2009). PAHs can be derived from multiple sources of predominantly

66

incomplete combustion and pyrogenic decomposition, such as coal combustion, vehicle emissions,

67

coking, biomass burning, and petroleum volatilization (Wang et al., 2013; Peng et al., 2011). It is

68

important to identify the sources, potential source regions, and health risk contributions for the

69

efficient control of PAH emissions from different sources.

TE D

M AN U

SC

60

Multiple methods can be used to identify these sources (Larsen et al., 2003). For example,

71

positive matrix factorization (PMF) has many advantages over other methods (e.g., principal

72

component analysis (PCA), principal component analysis/absolute principal component scores

73

(PCA/APCS), Unmix and chemical mass balance (CMB)), such as applying non-negative

74

constraints to the factor matrixes, setting up uncertainty profiles of the input data, presenting no

75

limitations on source numbers, requiring no specific emission profiles of sources prior to analysis,

76

and allowing better treatment of missing values or values below the detection limit (Paatero and

77

Tapper, 1993; Heo et al., 2009; Gao et al., 2014). Thus, PMF has been widely used in the source

78

apportionment of atmospheric PAHs (Callén et al., 2014; Liu et al., 2015; Yu et al., 2018). Because

79

atmospheric PAHs can reach the receptor site via regional transport (Lang et al., 2008; Macdonald

80

et al., 2000; Sofowote et al., 2011), to improve pollutant emission management, source region

81

identification according to the conditional probability function (CPF) and the potential source

82

contribution function (PSCF) is essential but has rarely been reported in the above studies.

AC C

EP

70

3

ACCEPTED MANUSCRIPT 83

Furthermore, combined with the actual source emissions, the CPF and PSCF are useful tools for

84

validating the PMF results (Wang et al, 2016; Sofowote et al., 2011; Chen et al., 2016). Different PAH sources can release distinct profiles. Low-molecular-weight (LMW, 2-3 rings)

86

PAHs originate mainly from volatilized oil (De Luca et al., 2004; Marr et al., 1999; Liu et al.,

87

2015). Large amounts of 3-4 ring PAHs are released mainly from coal combustion (Bragato et al.,

88

2012; Larsen and Baker, 2003) and biomass combustion (Sun et al, 2018). Higher concentrations

89

of high-molecular-weight (HMW, 4-6 rings) PAHs are emitted from vehicles than from other

90

sources (Harrison et al., 1996; Guarieiro et al., 2014; Marr et al., 1999). Different PAH species

91

possess different toxicities (Nisbet and LaGoy, 1992), and 16 PAHs have been listed as priority

92

pollutants by the US EPA (see Table 1). Among them, seven HMW PAH compounds have been

93

classified

94

benzo(b)fluoranthene,

95

indeno(1,2,3-cd)pyrene (USEPA, 2002, 2008). Consequently, even when the mass contribution to

96

PM2.5-bound PAHs of one source is the same as another, their contribution to health risk may be

97

substantially different because of the variability in emission profile. Therefore, the

98

source-attributed health risk, rather than the source-specific mass contribution, is vital to

99

determining the priority of emission sources for control and management in terms of protecting

human

carcinogens:

SC

probable

benz(a)anthracene,

M AN U

as

benzo(k)fluoranthene,

chrysene,

benzo(a)pyrene,

dibenz(ah)anthracene,

and

TE D

100

RI PT

85

public health (Liu et al, 2018).

Seasonal variations of PAHs have been widely reported (Callén et al., 2014; Kong et al., 2010;

102

Ma et al., 2018). These studies focused on PAH concentrations, sources, atmospheric behavior and

103

interactions under specific source emission and meteorological conditions. However, the

104

source-attributed health risk in different seasons has received little attention.

AC C

105

EP

101

Beijing suffers from serious atmospheric PAH pollution, which has led to health burdens in

106

recent years, especially during cold or heating seasons (Cao et al., 2018; Lin et al., 2015; Yin et al.,

107

2018). Source apportionment indicated that coal combustion, vehicle emission and biomass

108

burning were the main sources due to the high fossil fuel consumption and large number of

109

vehicles in the region (Lin et al., 2015; Yin et al., 2018; Yu et al., 2018). The above studies mainly

110

focused on severe pollution periods, such as haze days or the heating season (Cao et al., 2018; Yin

111

et al., 2018; Zhang et al., 2017a), specific months (Jiang et al., 2009) or major events, such as the 4

ACCEPTED MANUSCRIPT 112

APEC meeting (Xie et al., 2017; Yu et al., 2018; Zhang et al., 2017b) and the Beijing Olympic

113

Games (Wang et al., 2011), seldom covering an entire year or the whole seasons, and the missing

114

information will inevitably hinder accurate source apportionment and health risk assessment as

115

well as the realization of providing priority control emission sources strategies for different

116

seasons. This study investigated 16 priority PAHs bound on PM2.5 in a typical urban site in Beijing,

118

China. A total of 218 daily PM2.5 samples were collected, covering all months for a one-year

119

period in 2016. The uncertainty of the source apportionment was reduced by the application of the

120

PMF-PSCF-CPF model. Seasonal variations of PAHs in terms of concentration, source, and

121

source mass contribution were systematically analyzed. Finally, the source-attributed health risks

122

in different seasons were estimated and the priority control sources in each season were

123

determined.

124

2. Materials and Methods

125

2.1 Sampling site and sample collection

M AN U

SC

RI PT

117

Beijing is a typical northern mega-city in China with 21.73 and 12.48 million inhabitants in

127

the entire administrative district and urban district, respectively, and the number of motor vehicles

128

reached 5.72 million in 2016 (BMBS., 2017). The sampling site was a typical urban site under the

129

impact of residential, traffic and commercial activities (see Fig. S1). In Beijing, the heating season

130

spans from the middle of November to the middle of the following March, during which each day

131

has analogous meteorological conditions and energy use structures. Therefore, the entire year was

132

divided into four seasons, namely, heating season (Jan. 1st to Mar. 15th and Nov. 15th to Dec. 31st),

133

spring (Mar. 16th to May 31st), summer (Jun. 1st to Aug. 31st) and autumn (Sep. 1st to Nov. 14th).

EP

AC C

134

TE D

126

The PM2.5 sampling method is described in detail in our previous study (Liu et al., 2018). In

135

brief, a total of 218 daily PM2.5 samples were collected from Jan. 14th to Dec. 31st in 2016 by a

136

high-volume aerosol sampler (TH-1000CⅡ, Wuhan Tianhong Co., Wuhan, China), except for the

137

days of extreme weather events, sampler failure and cleaning. A quartz-fiber filter (QFF;

138

area=8×10 in) was used for daily sampling. The PM2.5 mass was determined by weighing the QFF

139

before and after sampling using an electronic analytical balance with an accuracy of 0.01 mg 5

ACCEPTED MANUSCRIPT 140

(AX205, Mettler-Toledo International Trading Co., Ltd.).

141

2.2 Chemical analysis Each filter was cut into small pieces, which were then extracted with n-hexane and acetone

143

(1:1, v/v). Then, the extracts were concentrated using a vacuum rotary evaporator (R-201, IKA,

144

Germany) before being transferred to an alumina silica gel column for purification. The

145

pretreatment of samples followed the procedure introduced in our previous study (Cao et al., 2016)

146

(see Supporting Information (Text S1) for the details). The 16 PAHs (Table 1) were detected using

147

a gas chromatograph with a mass spectrometer detector (Bruker 450GC-320MS). A mix of

148

internal standard PAHs (2-fluoro-1,1’-biphenyl and Phenanthrene-d10, each 0.2 µg/mL in

149

n-hexane) was used for quantification. The IDL (Instrumental Detection Limits), MDL (Method

150

Detection Limits) and blank of each PAH congener are shown in Table S1 (Supporting

151

Information). A mixture of deuterated surrogate compounds, including anthracene-D10,

152

chrysene-D12 and perylene-D12, was used to determine the recovery ratio. The PAH recoveries of

153

the surrogates and the sixteen PAHs standard-spiked matrix recoveries were all within the

154

acceptable range from 70% to 130%.

155

2.3 PMF method

TE D

M AN U

SC

RI PT

142

The EPA PMF model (PMF 5.0, USEPA, 2014) was applied to 218 samples to identify the

157

main PAH source profiles and quantify their contributions. NAP was not used in the PMF model

158

for more undetectable values. Further details on parameter determination and species

159

categorization by data quality were included in our previous study (Cao et al., 2016). Overall,

160

three-factor analysis yielded the most robust run, and the detailed dataset and calibration

161

parameters for the PAHs are provided in Tables S2-3. Model uncertainties were estimated using

162

displacement (DISP) error estimation and bootstrap (BS) error estimation (Tables S4-5). The

163

correlation between the observed and predicted values is shown in Fig. S2.

164

2.4 Regional and directional contributions of apportioned sources

AC C

EP

156

165

PSCF and CPF methods were employed to identify the contributions of apportioned sources

166

of PM2.5-bound PAHs on regional and directional scales, respectively. These methods have been 6

ACCEPTED MANUSCRIPT introduced in detail in our previous study (Liu et al., 2018). In brief, air mass trajectories were

168

calculated in the selected domain over the sampling period and the PSCF value was the proportion

169

of trajectory endpoints that exceeded criteria out of the total number of trajectory endpoints for

170

each domain cell. Likewise, the CPF value was the proportion of the number of hours during

171

which wind moved from one direction sector that exceeded the criterion out of the total number of

172

hours during which wind moved from the same sector. Related parameters and applicability for

173

these two methods are included in the Supporting Information (Text S2).

174

2.5 Source-attributed cancer risk

RI PT

167

The source-attributed health risk of PAHs, i.e., the incremental lifetime lung cancer risk

176

(ILCR) of PAHs attributed to a specific source, was evaluated based on source profiles and

177

inhalation unit risk (IUR=6×10-7 per ng/m3 via inhalation exposure) (USEPA, 2017). The general

178

residents of Beijing were taken as the evaluation objects. It was assumed that PM2.5-bound PAHs

179

were maintained at the current level for the next 30 years (USEPA, 2009). Cancer risks caused by

180

long-term exposure to PAHs emitted by individual sources via the inhalation pathway were

181

evaluated based on the models developed by the US EPA (US EPA, 2011). The detailed

182

calculation method and parameters of ILCR are provided in the Supporting Information (Text S3).

183

2.6 Meteorological parameters, point-source distribution and statistical methods

TE D

M AN U

SC

175

Daily surface meteorological data, including temperature (T), relative humidity (RH), O3,

185

wind speed (WS) and wind direction (WD), were used for the correlation analysis. WS and WD

186

were also used for the CPF analysis. Reanalysis meteorological data were used in the PSCF

187

analysis. Point-source data, including fire points of straw burning, coal-fired thermoelectric plants

188

and petrochemical plants, were simulated using spatial distribution by vectoring in ArcMap

189

(ESRI® ArcGIS 10.1). Data sources of the above data are shown in Table S6.

AC C

190

EP

184

SPSS (IBM SPSS® software 20.0) was used for statistical analysis, including Pearson

191

correlation analysis, one-way ANOVA and LSD test.

192

3 Results and Discussion

193

3.1. PM2.5 and PAH concentrations

194

The annual mean concentration of PM2.5 was 104 ± 70.6 µg/m³, which exceeds the National 7

ACCEPTED MANUSCRIPT Ambient Air Quality Standards (NAAQS) of China (35 µg/m3) (Ministry of Ecology and

196

Environment, PRC, 2012) and the interim target-1 standard (10 µg/m3) recommended by the WHO.

197

In the present study, the annual mean concentration of a total of 16 priority PM2.5-bound PAHs

198

(TPAHs) was 66.2 ± 111 ng/m3, which is less than the values reported for cities in northern China,

199

e.g., 113 ng/m3 in Beijing (Li et al., 2013) and 174 ng/m3 in Zhengzhou (Wang et al., 2015), but

200

much higher than other values measured in Hong Kong (1.9 ng/m3, Liu et al., 2013), Malaysia

201

(2.79 ng/m3, Khan, et al., 2015), the USA (3.16 ng/m3, Li et al., 2009; 1.19 ng/m3, Pleil et al., 2004;

202

0.78 ng/m3, Fraser et al., 2002), and South Korea (26.3 ng/m3, Park et al., 2002). For individual

203

PAHs, HMW PAHs, i.e., BbF, BaP, BghiP, FLA, IcdP, CHR and PYR, were much more abundant

204

than LMW PAHs, i.e., NAP, ACY, ACE, FLO and ANT.

SC

RI PT

195

For seasonal trends, the PAH concentration was significantly higher in the heating season

206

than in the other three seasons (one-way ANOVA, p < 0.05, see Fig. S3). No significant difference

207

was observed among the other three seasons. Overall, the values decreased in the order of heating

208

season (156 ng/m3) > autumn (20.4 ng/m3) > spring (16.0 ng/m3) > summer (12.5 ng/m3). This

209

trend is consistent with those reported in previous studies in northern China (Ma et al., 2018;

210

Zhang et al., 2016a; Yan et al., 2017) and other cities in the world (Callén et al., 2014; Sin et

211

al.,2003; Lodovici et al., 2003). As shown in Fig. 1 and from the Pearson correlation analysis

212

(Table S7), meteorological factors (T, WS and O3) may have a critical influence on the

213

concentration of atmospheric PAHs; the other impact factor was seasonal variation in sources. The

214

detailed discussion is provided in Section 3.4.

EP

TE D

M AN U

205

As a carcinogenic congener, BaP ranks eighth in ATSDR’s substance priority list (SPL,

216

updated 2017) due to its frequency of occurrence, high toxicity and potential threat to human

217

health (https://www.atsdr.cdc.gov/spl/). In our study, its annual mean concentration was 8.19

218

ng/m3, accounting for 12.4% of TPAHs. The mean BaP concentration was much higher in the

219

heating season (20.4 ng/m3) than those in other seasons (spring: 1.59 ng/m3; summer: 0.84 ng/m3;

220

and autumn: 1.85 ng/m3). Moreover, BaP concentration exceeded the class-2 limit of 1 ng/m3

221

recommended by NAAQS, especially during the heating season.

222

3.2 Source identification of PAHs using diagnostic ratios

223

AC C

215

The diagnostic ratio method has been commonly used in many studies for qualitative source 8

ACCEPTED MANUSCRIPT identification (Wang et al, 2014; Pongpiachan et al, 2017a). The varied value of diagnostic ratio is

225

related to different sources. Four diagnostic ratios were chosen, and the value ranges of different

226

sources were determined by referring to several widely cited references (Zhang et al., 2015;

227

Yunker et al., 2002; Liu et al., 2009) (Fig. 2). For the one-year samples, the ratios of

228

FLA/(FLA+PYR) were mostly greater than 0.5 and those of BaA/(BaA+CHR) were greater than

229

0.35, showing that biomass and coal combustion was the largest source of PAHs (Pongpiachan,

230

2017a). However, the proportion of mixed source (petroleum or combustion) was non-negligible.

231

Moreover, the ratios of ANT/(ANT+PHE) indicated that combustion was the dominant source of

232

PAHs. The values of IcdP/(IcdP+BghiP) of most samples between 0.2 and 0.5 suggested that

233

liquid fossil fuel combustion was the most abundant source (Pongpiachan, 2017b).

234

3.3 Source apportionment of PAHs by PMF

M AN U

SC

RI PT

224

235

Full-year PAH data were introduced into the PMF model, and three factors were identified

236

(Fig. 3): vehicle emissions (54.6%); coal combustion (29.8%); and volatilization of petroleum,

237

natural gas and biomass combustion (15.6%).

Factor 1 explained 54.6% of the total PAHs. The profile was mainly characterized by BbF

239

(53%), BkF (92%), BaP (63%), IcdP (88%), DahA (86%) and BghiP (84%). BbF, BkF and BaP are

240

markers of gasoline emissions (Ravindra et al., 2008). BghiP is the main tracer of gasoline engines

241

(Duval and Friedlander, 1981), and BbF and BkF are markers of diesel emissions (Harrison et al.,

242

1996). Guarieiro et al. (2014) also suggested that HMW PAHs can be substantially emitted by

243

diesel engines. Therefore, this factor was attributed to vehicle emissions. Although PAHs

244

attributed to vehicle emission may be somewhat influenced by regions of Northwest China (Fig.

245

4a), local sources were considered the major contributors, as the number of vehicles, the highway

246

freight traffic volume, and the highway passenger traffic volume per sq. km. in Beijing are much

247

higher than those in Inner Mongolia and Shanxi (Liu et al., 2018). Zhang et al. (2016b) also

248

reported that the PAH emission intensity from transportation in the Beijing-Tianjin area was much

249

higher than in other areas in North China. The atmospheric PAH emission inventory also

250

confirmed this conclusion (Shen et al., 2013). However, because the sampling site is located in an

251

urban center with a dense traffic network, PAHs can be released from vehicles in a wide range of

252

wind directions, and thus, no dominant contribution directions were identified in the CPF plots

AC C

EP

TE D

238

9

ACCEPTED MANUSCRIPT 253

(Fig. 5a). Factor 2 explained 29.8% of the total PAHs. The profile was mainly characterized by large

255

quantities of FLA (52%), PYR (50%), BaA (48%), CHR (49%), BbF (36%), and BaP (30%). FLA

256

and PYR are typical markers of coal combustion (Harrison et al., 1996). PYR, BaA, CHR and BaP

257

are markers of coal combustion (Larsen and Baker, 2003; Ravindra et al., 2008). Therefore, factor

258

2 was attributed to coal combustion. From the PSCF plot (Fig. 4b), areas including northern

259

Shannxi, Hebei, Shanxi, and northwestern Inner Mongolia were the potential source regions.

260

These areas are well known for large-scale coal industries and contain numerous coal-fired

261

thermoelectric plants (Fig. S4). According to the CPF plot (Fig. 5b), coal combustion from WSW,

262

SW and NNE were the major source directions. From these directions, residential coal combustion

263

in rural Beijing was the major contributor, although limited residential coal combustion existed

264

within urban areas.

M AN U

SC

RI PT

254

Factor 3 explained 15.6% of the total PAHs. This factor mainly consisted of FLO (96%),

266

PHE (76%), ANT (94%), FLA (23%), and PYR (19%). FLO, PHE, ANT and FLA are LMW PAHs

267

and likely originate from petroleum spills (Hu et al., 2013). PHE, FLA and PYR are markers of

268

biomass combustion (McGrath et al., 2001; Singh et al., 2013). Jenkins et al. (1996) suggested that

269

high concentrations of PHE may be related to biomass burning. FLO, PHE, ANT and PYR are

270

related to natural gas combustion (Li et al., 1999). Therefore, factor 3 is attributed to sources

271

involving petroleum volatilization, natural gas and biomass combustion. Numerous sub-sources

272

can contribute to this source. Petrol stations and petrochemical plants can volatilize PAHs (mainly

273

LMW PAHs) during refueling and transportation processes. Natural gas is burned for residential

274

cooking and heating, public transportation (e.g., compressed natural gas (CNG) and liquified

275

natural gas (LNG) buses) and petrochemical plants in Beijing. Straw burning is also an important

276

sub-source of this factor. The only large petrochemical plant (Yanshan Petrochemical) in Beijing is

277

located southwest of the sampling site (Fig. S4). The contribution from this direction was limited,

278

as the CPF value was only 0.23 (Fig. 5c); therefore, this source is considered to be influenced by

279

these sub-sources jointly at the local scale. The PSCF plot (Fig. 4c) and the spatial distribution of

280

straw burning (Fig. S5) suggest that Beijing exhibits limited biomass burning, whereas regional

281

transport from surrounding areas, i.e., Hebei, Shandong, Henan and Shanxi, Inner Mongolia, was

AC C

EP

TE D

265

10

ACCEPTED MANUSCRIPT 282

identified as a major contribution. Zhang et al. (2016b) also indicated the contribution of biomass

283

burning to Beijing-Tianjin mainly from surrounding areas based on emission inventory and

284

back-trajectory analysis.

285

3.4 Seasonal variations in source-attributed mass contributions Seasonal source-attributed mass contributions of PM2.5-bound PAHs were also analyzed (Fig.

287

6 and Fig. S6). Seasonal trends mainly depend on differences in meteorological conditions and

288

source emission intensities among different seasons and hence vary from season to season (Zhang

289

et al., 2017b). In the heating season, the mass contribution of each source was higher than that in

290

the other seasons (i.e., spring, summer and autumn), particularly for vehicle emissions and coal

291

combustion.

SC

RI PT

286

Low temperature in the heating season can impact the PAHs gas/particle distribution, and

293

PAHs tend to exist in particle phase (Tasdemir et al., 2007; Tsapakis et al., 2005). Adverse

294

horizontal and vertical diffusion in the heating season can also lead to high PAH concentrations

295

(Kong et al., 2010; Tang et al., 2016). By contrast, in the other three seasons, as the temperature

296

increased, particulate PAHs were easier to volatilize and photo-chemical reaction of PAHs into

297

nitro-PAHs and oxy-PAHs was enhanced. O3 is an important oxidant in the photochemical

298

degradation of PAHs (Balducci et al., 2018; Khan et al., 2018). A higher average level of O3 (69.5

299

µg/m3), compared to 33.5 µg/m3 in the heating season, can further strengthen the photodegradation

300

reaction in the other three seasons. Rainfall washout can also reduce the PAH concentration (Kong

301

et al., 2010). A higher three-season average daily precipitation (0.9 mm) was observed compared

302

to 0.1 mm in the heating season. In addition, a higher WS (0.79 m/s vs. 0.59 m/s) was also

303

exhibited. All the above factors may decrease the concentration of PAHs in these three seasons.

304

The above findings were further verified by significant negative correlations (p < 0.01) between

305

meteorological factors (i.e., T, WS and O3) and source-attributed daily mass contribution

306

according to correlation analysis in our study (Table S7).

AC C

EP

TE D

M AN U

292

307

The source emission intensity was another important influencing factor. The daily average

308

mass contribution from vehicle emissions was 102.0 ng/m³ in the heating season, which is

309

approximately 50-fold higher than that in the other three seasons (less than 2. 0 ng/m³). Although

310

the sampling site was located in the urban center and no obvious seasonal variation in traffic 11

ACCEPTED MANUSCRIPT volume was expected, more PAHs would be emitted due to low combustion efficiency resulting

312

from the cold start of motor vehicles and poor performance of the engine exhaust emission control

313

devices under low temperatures during the heating season (Li et al., 2009; Yang et al., 2005; Zhou

314

et al., 2005). Moreover, ratios of (3+4) ring PAHs/(5+6) ring PAHs were commonly used as the

315

indicator of the origin of particulate PAHs in previous studies (Zhang, 2017b; Kong, 2015; Tan,

316

2011). The higher ratio implies a longer distance of transport, while a lower ratio suggests

317

emissions from local areas. In our study, the average ratio was 6.31 in the other three seasons and

318

0.83 in winter (heating season). The higher ratio during the other three seasons indicated a longer

319

transport distance of PAHs and a larger emission region than during the other three seasons, while

320

the lower ratio in the heating season suggested that most PAHs were likely from local emissions or

321

relatively short-range transport. Beijing is a well-known traffic-congested city in China, with over

322

5.72 million motor vehicles according to statistical data in 2016 (BMBS, 2017). Therefore, with

323

more relatively local emissions in winter, vehicle emissions were understandably proportionally

324

higher than in the other three seasons. In fact, for the same reasons noted above, a higher ratio of

325

winter/summer vehicle emissions contribution has been observed in another study (Wang, 2014).

SC

M AN U

In the similar trend, coal combustion contributed a higher concentration of PAHs (48.1 ng/m³)

TE D

326

RI PT

311

in the heating season than in the other three seasons (less than 8 ng/m³). Coal combustion in

328

Beijing and northern China was amplified in the heating season for approximately four months.

329

Although three of the four largest coal-fired power plants (Gaojing, Shijingshan and Guohua

330

coal-fired power plants) have been shut down or converted to natural gas-fired power plants in

331

Beijing, coal is still the major fuel used during the heating season in most parts of North China.

332

Nevertheless, according to the PSCF and CPF plots (Fig. S7 and Fig. S8), southern areas of

333

Beijing were one of the major contributors to coal combustion-attributed PAHs. In addition,

334

residential coal combustion was a major contributor to atmospheric pollutants in these areas

335

(Zhang et al. 2017b), and in view of the high pollutant emission rates under limited control

336

measures (Xue et al., 2016), this source should not be ignored.

AC C

EP

327

337

However, the seasonal variation in petroleum volatilization, natural gas and biomass

338

combustion sources was relatively low (spring: 3.9 ng/m³; summer: 9.7 ng/m³; autumn: 8.0 ng/m³;

339

heating season: 17.9 ng/m³). Gas stations can release LMW PAHs and can be regarded as the 12

ACCEPTED MANUSCRIPT major source of petroleum volatilization in urban areas (Taghvaee et al., 2018). In this study, SSE,

341

S, and SSW from the sampling site were identified as the major contribution directions, which

342

resulted in a large mass contribution of this source in summer due to sector-specific high wind

343

frequencies, high wind speed and the corresponding high average daily concentration of LMW

344

PAHs; see Table S8 and Fig. S9. In 2016, Beijing had three natural gas-fired power stations

345

producing heat and electric power. Statistical data showed that 9.74 billion cubic meters of natural

346

gas (60.8% of total consumption) was consumed for heat and power production in 2016 (BMBS.,

347

2017), and most natural gas was combusted for heat during the heating season

348

(http://www.sohu.com/a/203599512_257724). In addition, the cold start of CNG and LNG buses

349

in the heating season can also increase the contribution (Li et al., 2009; Zhou et al., 2005). As one

350

of the major sources of biomass combustion, straw burning during spring plowing is a universal

351

phenomenon in the surrounding areas of Beijing (Fig. S5). Moreover, from the straw burning

352

distribution, the trend was more intense in spring than in the other seasons in the surrounding areas

353

of Beijing (Fig. S5). Although we cannot separate these sub-sources well, it was speculated that

354

natural gas combustion contributed more during the heating season, biomass combustion during

355

the spring, and petroleum volatilization may contribute the most during the summer.

356

3.5 Source-attributed cancer risk

TE D

M AN U

SC

RI PT

340

As shown in Table 2, the annual cancer risk was ranked in order of vehicle emissions (2.8 ×

358

10-6) > coal combustion (7.5 × 10-7) > petroleum volatilization, natural gas and biomass

359

combustion (3.8 × 10-7). Vehicle emissions contributed the most to cancer risk (71%) with the

360

highest BaPeq concentration contribution (11.2 ng/m³) due to high HMW PAH (e.g., BkF, BaP,

361

IcdP, DahA, BghiP) emissions. The cancer risk was higher than the acceptable level (1 × 10-6) and

362

showed approximately 34 additional cancer cases in urban Beijing (a total of 1.25 million people

363

in the urban area of Beijing).

AC C

EP

357

364

Seasonally, the heating season showed a much higher cancer risk than the other three seasons

365

for all three sources due to the higher contribution of BaPeq concentration and the relatively long

366

exposure duration (4 months long) (Fig. 1). Vehicle emissions in the heating season had the largest

367

cancer risk (2.5 × 10-6) and exceeded the acceptable level, followed by coal combustion, which

368

provided a risk close to the acceptable level (5.8 × 10-7). By contrast, cancer risk due to exposure 13

ACCEPTED MANUSCRIPT in spring, summer and autumn were within the acceptable level, i.e., on the order of 1 × 10-6. For

370

the purpose of protecting public health, the source-attributed cancer risk was set as the criterion in

371

the source control priority ranking in this study, although the corresponding risk was relatively low.

372

The ranking showed that coal combustion in spring, petroleum volatilization, natural gas and

373

biomass combustion in both summer and autumn, as well as vehicle emission in the heating

374

season, were the priority control sources.

375

4. Conclusions

RI PT

369

Analysis of 218 daily PM2.5 samples in 2016 showed that the heating season had a much

377

higher PAH concentration, especially HMW PAHs, than the other three seasons because of the

378

seasonal variability in meteorological conditions (WS, T and O3) and source emission intensity.

379

The PMF-PSCF-CPF integrated method has been successfully employed in source apportionment

380

and source region identification, verified by the actual regional emissions. Among the three

381

identified sources, vehicle emissions was the greatest contributor (54.6%) and was mainly

382

influenced by the local scale in the heating season. Coal combustion (29.8%) was mostly

383

explained by regional transport from surrounding areas. Petroleum volatilization, natural gas and

384

biomass combustion (15.6%) were influenced by both local and regional transport. The emission

385

control strategy should be refined by season and by source.

TE D

M AN U

SC

376

The source-attributed cancer risk was further estimated based on the source profiles by

387

season, that is, vehicle emissions (71.2%); coal combustion (19.1%); and petroleum volatilization,

388

natural gas and biomass combustion (9.7%). Vehicle emissions produced a much higher cancer

389

risk contribution than mass contribution due to the high proportion of relatively more toxic PAHs

390

(i.e., BbF, BkF, BaP, IcdP, DahA and BghiP) released by this source. Seasonally, vehicle emissions

391

contributed the least mass with only 8.1% in the summer, while the corresponding cancer risk

392

ranked second and rose to 15%. Thus, to protect public health, the source-attributed cancer risk

393

rather than the mass contribution was considered a better index for the development of a priority

394

source control strategy.

395

Acknowledgements

396

AC C

EP

386

The National Natural Science Foundation of China (40871231) funded the research. Many 14

ACCEPTED MANUSCRIPT thanks to Prof. G.F. Wang for his support on samples collection and Prof. X.G. Liu for his sharing

398

of surface meteorological data.

399

References

400

Abbas, I., Badran, G., Verdin, A., Ledoux, F., Roumie, M., Courcot, D., Garcon, G., 2018. Polycyclic aromatic

401

hydrocarbon derivatives in airborne particulate matter: sources, analysis and toxicity. Environ Chem Lett, 16:

402

439-475. https://doi.org/10.1007/s10311-017-0697-0.

RI PT

397

Armstrong, B., Hutchinson, E., Unwin, J., Fletcher, T., 2004. Lung cancer risk after exposure to polycyclic

404

aromatic hydrocarbons: A review and meta-analysis. Environ. Health Perspect. 112(9): 970-978. doi:

405

10.1289/ehp.6895.

SC

403

Balducci, C., Cecinato, A., Paolini, V., Guerriero, E., Perilli, M., Romagnoli, P., Tortorella, C., Iacobellis, S., Giove,

407

A., Febo, A., 2018, Volatilization and oxidative artifacts of PM bound PAHs collected at low volume

408

sampling

409

https://doi.org/10.1016/j.chemosphere.2018.02.090.

411 412

Laboratory

and

field

evaluation,

Chemosphere,

200:

106-115.

BMBS., Beijing Municipal Bureau of Statistics, 2017. Beijing Statistical Yearbook 2016. China Statistics Press, Beijing.

TE D

410

(1):

M AN U

406

Bragato, M., Joshi, K., Carlson, J.B., Tenório, J.A.S., Levendis, Y.A., 2012. Combustion of coal, bagasse and

413

blends

414

https://doi.org/10.1016/j.fuel.2011.11.069.

thereof:

Part

II:

Speciation

of

PAH

emissions.

Fuel,

96:

51-58.

Callén, M.S., Iturmendi, A., López, J.M., 2014. Source apportionment of atmospheric PM2.5-bound polycyclic

416

aromatic hydrocarbons by a PMF receptor model. Assessment of potential risk for human health. Environ.

417

Pollut. 195: 167-177. https://doi.org/10.1016/j.envpol.2014.08.025.

AC C

EP

415

418

Cao, H., Chao, S., Qiao, L., Jiang, Y., Zeng, X., Fan, X., 2016. Urbanization-related changes in soil

419

PAHs and potential health risks of emission sources in a township in Southern Jiangsu, China.

420

Sci. Total Environ. 575, 692. https://doi.org/10.1016/j.scitotenv.2016.09.106.

421

Cao, R., Zhang, H., Geng, N., et al., 2018. Diurnal variations of atmospheric polycyclic aromatic hydrocarbons

422

(PAHs) during three sequent winter haze episodes in Beijing, China. Science of the Total Environment,

423

625:1486. https://doi.org/10.1016/j.scitotenv.2017.12.335.

424

Chen, Y.C., Chiang, H.C., Hsu, C.Y., Yang, T.T., Lin, T.Y., Chen, M.J., Chen, N.T., Wu, Y.S., 2016. Ambient

425

PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Changhua County, central Taiwan: Seasonal 15

ACCEPTED MANUSCRIPT 426

variation, source apportionment and cancer risk assessment. Environmental Pollution, 218: 372-382.

427

https://doi.org/10.1016/j.envpol.2016.07.016

428

Duval, M.M. and Friedlander, S.K. Source resolution of polycyclic aromatic hydrocarbons in the Los Angeles

429

atmosphere: application of a chemical species balance method with first order chemical decay. Final report

430

Jan-Dec 80. United States: N. p., 1981. Web. https://www.osti.gov/biblio/5480142. De Luca, G., Furesi, A., Leardi, R., Micera, G., Panzanelli, A., Costantina Piu, P. and Sanna, G., 2004. Polycyclic

432

aromatic hydrocarbons assessment in the sediments of the Porto Torres Harbor (Northern Sardinia, Italy), Mar.

433

Chem., 86: 15-32. https://doi.org/10.1016/j.marchem.2003.11.001

Fraser, M.P., Yue, Z.W., Tropp, R.J., Kohl, S.D., Chow, J.C., 2002. Molecular composition of organic fine

435

particulate

436

https://doi.org/10.1016/S1352-2310(02)00725-2.

in

Houston,

TX.

Atmospheric

Environment,

36(38):

5751–5758.

M AN U

matter

SC

434

RI PT

431

437

Gao, J., Tian, H., Cheng, K., Lu, L., Wang, Y., Wu, Y., Zhu, C., Liu, K., Zhou, J., Liu, X., Chen, J., Hao, J., 2014.

438

Seasonal and spatial variation of trace elements in multi-size airborne particulate matters of Beijing, China:

439

Mass concentration, enrichment characteristics, source apportionment, chemical speciation and bioavailability.

440

Atmos. Environ. 99: 257-265. https://doi.org/10.1016/j.atmosenv.2014.08.081. Guarieiro, A.L.N., Santos, J.V.S., Eiguren-Fernandez, A., Torres, E.A., da Rocha, G.O., de Andrade J.B., 2014.

442

Redox activity and PAH content in size-classified nanoparticles emitted by a diesel engine fuelled with

443

biodiesel and diesel blends, Fuel, 116: 490-497. https://doi.org/10.1016/j.fuel.2013.08.029

TE D

441

Harrison, R.M., Smith, D.J.T., Luhana, L., 1996. Source apportionment of atmospheric polycyclic aromatic

445

hydrocarbons collected from an urban location in Birmingham, U.K. Environ. Sci. Technol. 30, 825–832.

446

doi:10.1021/es950252d.

448

AC C

447

EP

444

Heo, J.B., Hopke, P.K., Yi, S.M., 2009. Source apportionment of PM2.5 in Seoul, Korea. Atmos. Chem. Phys. 9: 4957-4971. https://doi.org/10.5194/acp-9-4957-2009.

449

Hu, Y., Wen, J., Wang, D., Du, X., Li, Y., 2013. An interval dynamic multimedia fugacity (IDMF) model for

450

environmental fate of PAHs and their source apportionment in a typical oilfield, China. Chem. Ecol. 29 (5):

451

476–488. https://doi.org/10.1080/02757540.2013.769968.

452

Jenkins, B.M., Jones, A.D., Turn, S.Q., and Williams, R.B., 1996. Emission Factors for Polycyclic Aromatic

453

Hydrocarbons from Biomass Burning. Environ. Sci. Technol. 30(8): 2462-2469. doi: 10.1021/es950699m.

454

Jiang, Y., Hou, X., Zhuang, G., Li, J., Wang, Q.Z., Zhang, R., Lin, Y.F., 2009. sources and seasonal variations of 16

ACCEPTED MANUSCRIPT 455

organic compounds in PM₂.₅ in Beijing and Shanghai. Journal of Atmospheric Chemistry, 62(3):175-192.

456

https://doi.org/10.1007/s10661-015-4288-x.

457

Khan, M.F., Latif, M.T., Lim, C.H., Amil, N., Jaafar, S.A., Dominick, D., Nadzir, Mohd M.S., Sahani, M., Tahir,

458

N.M., 2015. Seasonal effect and source apportionment of polycyclic aromatic hydrocarbons in PM2.5. Atmos.

459

Environ. 106: 178-190. https://doi.org/10.1016/j.atmosenv.2015.01.077. Khan, B., Masiol, M., Bruno, C., Pasqualetto, A., Formenton, G.M., Agostinelli, C., Pavoni, B., 2018. Potential

461

sources and meteorological factors affecting PM2.5-bound polycyclic aromatic hydrocarbon levels in six

462

main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks.

463

Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-018-2841-1.

SC

RI PT

460

Kong, S.F., Ding, X., Bai, Z.P., Han, B., Chen, L., Shi, J.W., Li, Z.Y., 2010. A seasonal study of polycyclic

465

aromatic hydrocarbons in PM2.5 and PM2.5–10 in five typical cities of Liaoning Province, China. J. Hazard.

466

Mater. 183: 70–80. https://doi.org/10.1016/j.jhazmat.2010.06.107.

M AN U

464

Kong, S., Li, X., Li, L., Yin, Y., Chen, K., Yuan, L., Zhang, Y., Shan, Y., Ji, Y., 2015. Variation of polycyclic

468

aromatic hydrocarbons in atmospheric PM2.5 during winter haze period around 2014 Chinese Spring Festival

469

at Nanjing: Insights of source changes, air mass direction and firework particle injection. Environ. Sci.

470

Technol. 520, 59-72.10.1016/j.scitotenv.2015.03.001

TE D

467

Lang, C., Tao, S., Liu, W.X., Zhang, Y.X., Simonich, S., 2008. Atmospheric transport and outflow of polycyclic

472

aromatic hydrocarbons from China, Environ. Sci. Technol. 42(14): 5196-5201. doi: 10.1021/es800453n.

473

Larsen, R.K. and Baker, J.E., 2003. Source apportionment of polycyclic aromatic hydrocarbons in the urban

474

atmosphere: a comparison of three methods. Environ. Sci. Technol. 37(9): 1873–1881. doi:

475

10.1021/es0206184.

477

AC C

476

EP

471

Li, C., Mi, H., Lee, W., You, W.C., Wang, Y.F., 1999. PAH emission from the industrial boilers. J. Hazard. Mater. 69(1): 1-11. https://doi.org/10.1016/S0304-3894(99)00097-7.

478

Li, Z., Sjodin, A., Porter, E.N., Patterson, D.G., Needham, J.L.L., Lee, S., Russell, A.G., Mulholland, J.A., 2009.

479

Characterization of PM2.5-bound polycyclic aromatic hydrocarbons in Atlanta, Atmos. Environ. 43: 1043–

480

1050. https://doi.org/10.1016/j.atmosenv.2008.11.016.

481

Li, X., Wang, Y., Guo, X., Wang, Y., 2013. Seasonal variation and source apportionment of organic and inorganic

482

compounds in PM2.5 and PM10 particulates in Beijing, China. J. Environ. Sci., 25: 741-750.

483

https://doi.org/10.1016/S1001-0742(12)60121-1. 17

ACCEPTED MANUSCRIPT 484

Lin, Y., Ma, Y., Qiu, X., Li, R., Fang, Y., Wang, J., Zhu, Y., Hu, D., 2015. Sources, transformation, and health

485

implications of PAHs and their nitrated, hydroxylated, and oxygenated derivatives in PM2.5 in Beijing, J.

486

Geophys. Res. Atmos., 120, 7219–7228. doi:10.1002/2015JD023628. Liu, S.Z., Tao, S., Liu, W.X., Dou, H., Liu, Y. N., Zhao, J.Y., Little, M.G., Tian, Z.F., Wang, J.F., Wang, L.G., Gao,

488

Y., 2008. Seasonal and spatial occurrence and distribution of atmospheric polycyclic aromatic hydrocarbons

489

(PAHs) in rural and urban areas of the North Chinese Plain. Environ. Pollut. 156(3): 651–656.

490

https://doi.org/10.1016/j.envpol.2008.06.029.

RI PT

487

Liu, Y., Chen, L., Huang, Q., Li, W., Tang, Y., Zhao, J., 2009. Source apportionment of polycyclic aromatic

492

hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China. Sci. Total Environ. 407,

493

2931-2938.

SC

491

Liu, F., Xu, Y., Liu, J., Liu, D., Li, J., Zhang, G., Li, X., Zou, S., Lai, S., 2013. Atmospheric deposition of

495

polycyclic aromatic hydrocarbons (PAHs) to a coastal site of Hong Kong, South China. Atmos. Environ. 69:

496

265-272. https://doi.org/10.1016/j.atmosenv.2012.12.024.

M AN U

494

Liu, Y., Wang, S.Y., Lohmann, R., Yu N., Zhang, C.K., Gao, Y., Zhao, J.F., Ma, L.M., 2015. Source apportionment

498

of gaseous and particulate PAHs from traffic emission using tunnel measurements in Shanghai, China, Atmos.

499

Environ. 107:129-136. https://doi.org/10.1016/j.atmosenv.2015.02.041.

TE D

497

500

Liu B., Wu J., Zhang J., Wang L., Yang J, Liang D, Dai Q, Bi X, Feng Y, Zhang Y, Zhang Q. 2017.

501

Characterization and source apportionment of pm 2.5, based on error estimation from epa pmf 5.0 model at a

502

medium city in china. Environ. Pollut. 222, 10.

Liu, J.W., Chen, Y.J., Chao, S.H., Cao, H.B., Zhang, A.C., Yang, Y., 2018. Emission control priority of

504

PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Sci.

AC C

505

EP

503

Total Environ. 644: 20–30. https://doi.org/10.1016/j.scitotenv.2018.06.226.

506

Lodovici, M., Venturini, M., Marini, E., Grechib, D., Dolara, P., 2003. Polycyclic aromatic hydrocarbons air levels

507

in Florence, Italy, and their correlation with other air pollutants, Chemosphere, 50: 377–382.

508 509

https://doi.org/10.1016/S0045-6535(02)00404-6.

Ma, W.L., Liu, L.Y., Jia, H. L., Yang, M., Li, Y. F., 2018. PAHs in Chinese atmosphere Part I: Concentration,

510

source

511

https://doi.org/10.1016/j.atmosenv.2017.11.029.

512

and

temperature

dependence.

Atmos.

Environ.

173:

330-337.

Macdonald, R.W., Barrie, L.A., Bidleman, T.F., Diamond, M.L., Gregor, D.J., Semkin, R.G., Strachan, W.M.J., Li, 18

ACCEPTED MANUSCRIPT 513

Y.F., Wania, F., Alaee, M., Alexeeva, L.B., Backus, S.M., Bailey, R., Bewers, J.M., Gobeil, C., Halsall, C.J.,

514

Harner, T. Hoff, J.T., Jantunen, L.M.M., Lockhart, W.L., Mackay, D., Muir, D.C.G., Pudykiewicz, J., Reimer,

515

K.J., Smith, J.N., Stern, G.A., Schroeder, W.H., Wagemann, R., Yunker, M.B., 2000. Contaminants in the

516

Canadian Arctic: 5 years of progress in understanding sources, occurrence and pathways. Sci. Total Environ.

517

254(2-3): 93-234. https://doi.org/10.1016/S0048-9697(00)00434-4. Marr, L.C., Kirchstetter, T.W., Harley, R.A., Miguel, A.H., Hering, S.V., Hammond, S.K., 1999. Characterization

519

of Polycyclic Aromatic Hydrocarbons in Motor Vehicle Fuels and Exhaust Emissions, Environ. Sci. Technol.

520

33:3091–3099. doi: 10.1021/es981227l.

McGrath, T., 2002. An experimental investigation into the formation of polycyclic-aromatic hydrocarbons (PAH)

522

from

523

https://doi.org/10.1016/S0140-6701(02)86494-3

of

biomass

materials.

Fuel

and

Energy

Abstracts,

43(4):286.

M AN U

pyrolysis

SC

521

RI PT

518

524

Ministry of Ecology and Environment, PRC. National Ambient Air Quality Standards (GB 3095-2012), 2012.

525

Nisbet, I.C. and LaGoy, P.K., 1992. Toxic equivalency factors (TEFs) for polycyclic aromatic hydrocarbons

529 530 531 532 533 534

of error estimates of data values. Environmetrics 5: 111–126. https://doi.org/10.1002/env.3170050203.

TE D

528

Paatero, P. and Tapper, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization

Park, S.S., Kim, Y.J., Kang, C.H., 2002. Atmospheric polycyclic aromatic hydrocarbons in Seoul, Korea. Atmos. Environ. 36(17): 2917-2924. https://doi.org/10.1016/S1352-2310(02)00206-6. Peng, C., Chen, W., Liao, X., Wang, M., Ouyang, Z., Jiao, W., Bai, Y., 2011. Polycyclic aromatic hydrocarbons in

EP

527

(PAHs). Regul. Toxicol. Pharmacol. 16 (3): 290-300.

urban soils of Beijing: Status, sources, distribution and potential risk. Environ Pollut. 159, 802-808. Pleil, J.D., Vette, A.F., Rappaport, S.M., 2004. Assaying particle-bound polycyclic aromatic hydrocarbons from

AC C

526

archived PM2.5 filters. J. Chromatogr. A, 1033(1): 9–17. https://doi.org/10.1016/j.chroma.2003.12.074.

535

Pongpiachan, S., Hattayanone, M., Cao, J., 2017a. Effect of agricultural waste burning season on PM 2.5 -bound

536

polycyclic aromatic hydrocarbon (PAH) levels in Northern Thailand. Atmospheric Pollution Research 8,

537

1069-1080.

538

Pongpiachan, S., Hattayanone, M., Suttinun, O., Khumsup, C., Kittikoon, I., Hirunyatrakul, P., Cao, J., 2017b.

539

Assessing human exposure to PM 10 -bound polycyclic aromatic hydrocarbons during fireworks displays.

540

Atmospheric Pollution Research 8, 816-827.

541

Ravindra, K., Sokhi, R., Van Grieken, R.V., 2008. Atmospheric polycyclic aromatic hydrocarbons: source 19

ACCEPTED MANUSCRIPT 542

attribution,

543

https://doi.org/10.1016/j.atmosenv.2007.12.010.

emission

factors

and

regulation.

Atmos.

Environ.

42(13):

2895-2921.

Shen, H.Z., Huang, Y., Wang, R., Zhu, D., Li, W., Shen, G.F., Wang, B., Zhang, Y.Y., Chen, Y.C., Lu, Y., Chen, H.,

545

Li, T.C., Sun, K., Li, B.G., Liu, W.X., Liu, J.F. and Tao, S., 2013. Global atmospheric emissions of polycyclic

546

aromatic hydrocarbons from 1960 to 2008 and future predictions. Environ. Sci. Technol., 47(12): 6415-6424.

547

doi: 10.1021/es400857z

RI PT

544

Sin, D.W.M., Wong, Y.C., Choi, Y.Y., Lam, C.H., Louie, P.K.K., 2003. Distribution of polycyclic aromatic

549

hydrocarbons in the atmosphere of Hong Kong, J. Environ. Monit. 5(6): 989-996. doi: 10.1039/B310095B

550

Sofowote, U. M., Hung, H., Rastogi, A.K., Westgate, J.N., Deluca, P.F., Su, Y.S., McCarry B.E., 2011. Assessing

551

the long-range transport of PAH to a sub-Arctic site using positive matrix factorization and potential source

552

contribution function. Atmos. Environ. 45: 967-976. https://doi.org/10.1016/j.atmosenv.2010.11.005.

M AN U

SC

548

553

Sun J, Shen Z, Zeng Y, Niu X, Wang J, Cao J, Gong X, Xu H, Wang T, Liu H, Yang L. 2018. Characterization and

554

cytotoxicity of PAHs in PM2.5 emitted from residential solid fuel burning in the Guanzhong Plain, China.

555

Environ. Pollut. 241:359-368.

Taghvaee, S., Sowlat, M.H., Hassanvand, M.S., et al., 2018. Source-specific lung cancer risk assessment of

557

ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in central Tehran. Environment International,

558

120: 321-332. https://doi.org/10.1016/j.envint.2018.08.003.

559 560

TE D

556

Tan, J., Guo, S., Ma, Y., Duan, J., Cheng, Y., He, K., Yang, F., 2011. Characteristics of particulate PAHs during a typical haze episode in Guangzhou, China. Atmos Res 102, 91-98.10.1016/j.atmosres.2011.06.012 Tang, G., Zhang, J., Zhu, X., Song, T., Münkel, C., Hu, B., Wang, Y., 2016. Mixing layer height and its

562

implications for air pollution over Beijing, China. Atmos. Chem. Phys. 16 (4): 2459–2475.

AC C

563

EP

561

https://doi.org/10.5194/acp-16-2459-2016.

564

Tasdemir, Y. and Esen, F., 2007. Urban air PAHs: Concentrations, temporal changes and gas/particle partitioning at

565

a traffic site in Turkey. Atmospheric Research, 84(1):0-12. https://doi.org/10.1016/j.atmosres.2006.04.003.

566

Tsapakis, M. and Stephanou, E.G., 2005. Occurrence of gaseous and particulate polycyclic aromatic hydrocarbons

567

in the urban atmosphere: study of sources and ambient temperature effect on the gas/particle concentration

568

and distribution. Environmental Pollution, 2005, 133(1):0-156. https://doi.org/10.1016/j.envpol.2004.05.012.

569

US EPA., 1989. Risk Assessment Guidance for Superfund: Volume 1—Human Health Evaluation Manual (Part A);

570

Interim Final, EPA/540/1-89/002; U.S. Environmental Protection Agency, Office of Emergency and 20

ACCEPTED MANUSCRIPT 571

Remedial Response, Development: Washington, DC.

572

US EPA., 2002. Polycyclic Organic Matter. US Environmental Protection Agency.

573

US EPA., 2008. Polycyclic aromatic hydrocarbons (PAHs) —EPA fact sheet. Washington, DC: National Center for

574

Environmental Assessment, Office of Research and Development. US EPA, Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual

576

(Part F, Supplemental Guidance for Inhalation Risk Assessment, 2009).

577

US EPA, 2011. Exposure Factors Handbook 2011 Edition (Final Report). U.S. Environmental Protection Agency,

578

Washington, DC (EPA/600/R-09/052F).

RI PT

575

US EPA., 2017. Toxicological Review of Benzo[a]pyrene. Washington, DC.

580

Wang, W., Jariyasopit, N., Schrlau, J., et al., 2011. Concentration and Photochemistry of PAHs, NPAHs, and

581

OPAHs and Toxicity of PM2.5 during the Beijing Olympic Games. Environ. Sci. Technol., 45(16):6887. doi:

582

10.1021/es201443z.

M AN U

SC

579

583

Wang, X., Miao, Y., Zhang, Y., Li, Y., Wu, M., Yu, G., 2013. Polycyclic aromatic hydrocarbons (PAHs) in urban

584

soils of the megacity Shanghai: Occurrence, source apportionment and potential human healthKo risk. Sci.

585

Total Environ. 447, 80-89.

Wang, F., Lin, T., Li, Y., Ji, T., Ma, C., Guo, Z., 2014. Sources of polycyclic aromatic hydrocarbons in PM2.5 over

587

the East China Sea, a downwind domain of East Asian continental outflow. Atmos. Environ. 92, 484-492.

588

10.1016/j.atmosenv.2014.05.003.

589

TE D

586

Wang J., Li X., Jiang N., Zhang, W.K., Zhang, R.Q., Tang, X.Y., 2015. Long term observations of PM2.5-associated PAHs:

591

https://doi.org/10.1016/j.atmosenv.2015.01.026.

593 594

comparisons

between

AC C

592

EP

590

normal

and

episode

days.

Atmos.

Environ.

104:

228-236.

Wang Q, Min L, Yu Y, Li Y,. 2016. Characterization and source apportionment of PM2.5-bound polycyclic aromatic

hydrocarbons

from

Shanghai

city,

China.

Environ.

Pollut.

218:118-128.

https://doi.org/10.1016/j.envpol.2016.08.037.

595

Xie Y, Zhao B, Zhao Y, Luo Q, Wang S.X, Zhao B, Bai S.H. 2017. Reduction in population exposure to PM 2.5,

596

and cancer risk due to PM 2.5 -bound PAHs exposure in Beijing, China during the APEC meeting. Environ.

597

Pollut. 225:338-345. https://doi.org/10.1016/j.envpol.2017.02.059.

598

Xue, Y.F., Zhou, Z., Nie, T., Kun, W., Nie, L., Pan, T., Wu, X.Q., Tian, H.H., Zhong, L.H., Li, J., Liu, H.J., Liu,

599

S.H., Shao, P.Y., 2016. Trends of multiple air pollutants emissions from residential coal combustion in Beijing 21

ACCEPTED MANUSCRIPT 600

and its implication on improving air quality for control measures. Atmos. Environ. 142: 303-312.

601

http://dx.doi.org/10.1016/j.atmosenv.2016.08.004. Yan, Y.L., He, Q.S., Guo, L.L., Li, H.Y., Zhang, H.F., Shao, M., Wang, Y.H., 2017. Source apportionment and

603

toxicity of atmospheric polycyclic aromatic hydrocarbons by PMF: Quantifying the influence of coal usage in

604

Taiyuan, China. Atmos. Res. 193(1): 50-59. https://doi.org/10.1016/j.atmosres.2017.04.001.

606 607

Yang, H., Hsieh, L., Liu, H., Mi, H., 2005. Polycyclic aromatic hydrocarbon emissions from motorcycles. Atmos. Environ 39, 17-25. https://doi.org/10.1016/j.atmosenv.2004.09.054.

Yin, H., Xu, L.Y., 2018. Comparative study of PM10/PM2.5-bound PAHs in downtown Beijing, China:

608

Concentrations,

609

https://doi.org/10.1016/j.jclepro.2017.12.263

sources,

and

health

risks.

J.

Clean.

Prod.

177:

674-683.

SC

605

RI PT

602

Yu, W., Liu, R., Wang, J., Xu, F., Shen, Z., 2015. Source apportionment of PAHs in surface sediments using

611

positive matrix factorization combined with GIS for the estuarine area of the Yangtze River, China.

612

Chemosphere, 134: 263-271. https://doi.org/10.1016/j.chemosphere.2015.04.049.

M AN U

610

Yu, Q.Q., Yang, W.Q., Zhu, M., Gao, B., Li, S., Li, G.H., Fang, H., Zhou, H.S., Zhang, H.N., Wu, Z.F., Song, W.,

614

Tan, J.H., Zhang, Y.L., Bi, X.H., Chen, L.G., Wang, X.M., 2018. Ambient PM2.5-bound polycyclic aromatic

615

hydrocarbons (PAHs) in rural Beijing: Unabated with enhanced temporary emission control during the 2014

616

APEC summit and largely aggravated after the start of wintertime heating, Environ. Pollut. 238: 532-542.

617

https://doi.org/10.1016/j.envpol.2018.03.079.

TE D

613

Yunker, M.B., Macdonald, R.W., Vingarzan, R., Mitchell, R.H., Goyette, D., Sylvestre, S., 2002. PAHs in the

619

Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and composition.

620

ORGANIC GEOCHEMISTRY 33, 489-515

EP

618

Zhou, J.B., Wang, T.G., Huang, Y.B., Mao, T., Zhong, N.N., 2005. Size distribution of polycyclic aromatic

622

hydrocarbons in urban and suburban sites of Beijing, China, Chemosphere, 61(6): 792–799.

623

AC C

621

https://doi.org/10.1016/j.chemosphere.2005.04.002.

624

Singh, D.P., Gadi, R., Mandal, T.K., Saud, T., Saxena, M., Sharma, S.K., 2013. Emissions estimates of PAH from

625

biomass fuels used in rural sector of Indo-Gangetic Plains of India, Atmos. Environ. 68: 120-126.

626

https://doi.org/10.1016/j.atmosenv.2012.11.042.

627

Zhang, Y.X., Tao, S., Shen, H.Z., Ma, J.M., 2009. Inhalation exposure to ambient polycyclic aromatic

628

hydrocarbons and lung cancer risk of Chinese population. PNAS, 106(50): 21063-21067. doi: 22

ACCEPTED MANUSCRIPT 629

10.1073/pnas.0905756106.

630

Zhang, J., Wang, J., Hua, P., Krebs, P., 2015. The qualitative and quantitative source apportionments of polycyclic

631

aromatic hydrocarbons in size dependent road deposited sediment. Sci. Total Environ 505, 90-101.Yunker M

632

B, Macdonald R W, Vingarzan R, et al. PAHs in the Fraser River basin: a critical appraisal of PAH ratios as

633

indicators of PAH source and composition[J]. Organic Geochemistry, 2002, 33(4): 489-515. Zhang, M., Xie, J.F., Wang, Z.T., Zhao, L.J., Zhang, H., Li, M., 2016a. Determination and source identification of

635

priority polycyclic aromatic hydrocarbons in PM2.5 in Taiyuan, China. Atmos. Res. 178–179: 401–414.

636

http://dx.doi.org/10.1016/j.atmosres.2016.04.005

RI PT

634

Zhang, Y.J., Lin, Y., Cai, J., Liu, Y., Hong, L.A., Qin, M.M., Zhao, Y.F., Ma, J., Wang, X.S., Zhu, T., Qiu, X.H.,

638

Zheng, M., 2016b. Atmospheric PAHs in North China: Spatial distribution and sources. Sci. Total Environ.

639

565: 994–1000. http://dx.doi.org/10.1016/j.scitotenv.2016.05.104

M AN U

SC

637

640

Zhang, Z.Z., Wang, W.X., Cheng, M.M., Liu, S.J., Xu, L., He, Y.J.M Meng, F., 2017a. The contribution of

641

residential coal combustion to PM2.5 pollution over China's Beijing-Tianjin-Hebei region in winter. Atmos.

642

Environ. 159: 147-161. https://doi.org/10.1016/j.atmosenv.2017.03.054.

Zhang, Y., Chen, J., Yang, H., Li, R., Yu, Q., 2017b. Seasonal variation and potential source regions of

644

PM2.5-bound PAHs in the megacity Beijing, China: Impact of regional transport. Environ. Pollut. 231(1):

645

329-338. https://doi.org/10.1016/j.envpol.2017.08.025.

AC C

EP

TE D

643

23

ACCEPTED MANUSCRIPT 1

Table 1. Annual and seasonal concentrations (mean ± SD) of PM2.5 (µg/m3), 16 PAHs (ng/m3) and

2

BaPeq (ng/m3) in Beijing.

Abbreviation

PAH

Rings

Annual

Spring

Summer

Autumn

Heating season

(n=218)

(n=55)

(n=47)

(n=38)

(n=78)

0.89 ± 1.87

0.13 ± 0.26

TEFi

Naphthalene

2

0.001

0.31 ± 0.88

0.15 ± 0.32

0.32 ± 0.42

ACY

Acenaphthylene

3

0.001

0.06 ± 0.18

0.00 ± 0.01

0.00 ± 0.00

n.d.

0.16 ± 0.27

ACE

Acenaphthene

3

0.001

0.54 ± 1.31

0.78 ± 1.87

1.49 ± 1.51

0.02 ± 0.11

0.05 ± 0.09

FLO

Fluorene

3

0.001

0.46 ± 2.66

0.85 ± 5.22

0.27 ± 0.45

0.30 ± 0.42

0.38 ± 0.43

PHE

Phenanthrene

3

0.001

3.66 ± 4.30

1.95 ± 2.57

2.07 ± 1.17

2.38 ± 2.49

6.46 ± 5.57

ANT

Anthracene

3

0.01

0.57 ± 1.13

0.31 ± 1.40

0.36 ± 0.22

0.44 ± 0.99

0.95 ± 1.20

FLA

Fluoranthene

4

0.001

6.01 ± 9.34

1.47 ± 2.29

1.28 ± 1.43

2.26 ± 2.53

13.9 ± 11.8

PYR

Pyrene

4

0.001

5.02 ± 7.96

1.21 ± 2.04

0.90 ± 1.26

1.82 ± 2.10

11.75 ± 10.0

BaA

Benz(a)anthracene

4

0.1

4.17 ± 7.02

0.89 ± 2.16

0.38 ± 1.33

0.99 ± 1.31

10.3 ± 8.58

CHR

Chrysene

4

0.01

5.66 ± 8.87

1.47 ± 3.10

0.74 ± 2.05

1.98 ± 2.43

13.4 ± 10.7

BbF

Benzo(b)fluoranthene

5

0.1

14.1 ± 24.3

3.65 ± 6.78

2.34 ± 6.02

5.07 ± 5.32

33.0 ± 32.1

BkF

Benzo(k)fluoranthene

5

0.1

4.36 ± 8.67

0.31 ± 2.27

0.32 ± 2.14

1.05 ± 3.36

11.3 ± 11.1

BaP

Benzo(a)pyrene

DahA

TE D

M AN U

SC

RI PT

NAP

1

8.19 ± 14.9

1.59 ± 4.29

0.84 ± 3.62

1.85 ± 2.57

20.4 ± 19.0

Dibenzo(a,h)anthracene

5

5

5.72 ± 13.0

0.56 ± 2.60

0.47 ± 1.81

0.61 ± 0.60

15.0 ± 18.3

IcdP

Indeno(1,2,3-cd)pyrene

6

0.1

1.09 ± 3.10

0.06 ± 0.41

0.05 ± 0.30

0.04 ± 0.12

2.96 ± 4.61

BghiP

Benzo(g,h,i)perylene

0.01

6.30 ± 13.6

0.75 ± 2.85

0.61 ± 2.27

0.67 ± 1.03

16.4 ± 18.6

5.60 ± 6.28

4.04 ± 7.23

4.51 ± 2.44

4.03 ± 4.39

8.13 ±7.10

∑HMW

60.6 ± 107

12.0 ± 27.2

7.94 ± 21.9

16.3 ± 16.1

148 ± 138

TPAH

66.2 ± 111

16.0 ± 29.9

12.5 ± 22.4

20.4 ± 17.7

156 ± 144

BaPeq

16.6 ± 34.6

2.45 ± 7.23

1.44 ± 6.25

2.84 ± 3.81

42.4 ± 47.3

PM2.5

104 ± 70.6

102 ± 72.4

62.1 ± 26.0

99.9 ± 79.8

134 ± 69.3

6

AC C

∑LMW

EP

5

3

LMW: low molecular weight PAHs (2-3 rings).

4

HMW: high molecular weight PAHs (4-6rings).

5

TPAH: total 16 priority PAHs.

6

TEF: toxic equivalent factor (Nisbet and LaGoy, 1992). 1

ACCEPTED MANUSCRIPT 7

BaPeq: BaP equivalent concentration of 16 PAHs

8

n.d: not detected.

9 Table 2 Source-attributed BaPeq (ng/m3) and cancer risk in different seasons

RI PT

10

petroleum volatilization, vehicle emission

coal combustion

natural gas and biomass combustion

Cancer risk

BaPeq

Cancer risk

Annual

11.2

2.8E-06

3.03

7.5E-07

1.54

3.8E-07

Spring

1.01

5.2E-08

1.11

5.7E-08

0.55

2.8E-08

Summer

0.29

1.8E-08

Autumn

0.51

2.6E-08

Heating

30.0

2.5E-06

M AN U 1.2E-08

1.37

8.4E-08

1.07

5.5E-08

1.14

5.9E-08

7.05

5.8E-07

2.54

2.1E-07

EP AC C

11

Cancer risk

0.20

TE D

season

BaPeq

SC

BaPeq

2

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

1 2

AC C

EP

( a)

TE D

Fig. 1. Seasonal variations of PAHs and meteorological factors at the sampling site

3 4 5 6

ACCEPTED MANUSCRIPT

SC

RI PT

( b)

M AN U

7 8

Fig. 2. Diagnostic ratio plotting ANT/(ANT+PHE) vs FLA(FLA+PYR) (a), diagnostic ratios

9

plotting BaA/(BaA+CHR) vs IcdP/(IcdP+BghiP) (b)

AC C

EP

TE D

10

11 12

Fig. 3. Source profiles of vehicle emission, coal combustion, petroleum volatilization and natural

13

gas and biomass combustion, and their mass contribution on an annual basis

ACCEPTED MANUSCRIPT

RI PT

14

16

TE D

M AN U

SC

15

Fig.4. Annual PSCF analysis for (a) vehicle emission, (b) coal combustion, (c) petroleum

18

volatilization and natural gas and biomass combustion. The red star marks the location of the

20 21 22

AC C

19

EP

17

sampling site.

ACCEPTED MANUSCRIPT N NNW

N

0.4

NNE

0.3

NW

0.4

NNW NE

0.3

NW

0.2 ENE

WNW

ENE

0.1

0.1

SW

ESE

WSW

E

ESE

SW

SSE

SE

SSW

SSE

S

S

(a) factor 1: vehicle emission

(b) factor 2: coal combustion

N

NW

0.4

NNE

M AN U

NNW

SC

24

W

SE SSW

0

E

RI PT

0

WSW

23

NE

0.2

WNW

W

NNE

0.3

NE

0.2

WNW

ENE

0.1

0

W

WSW

E

ESE

TE D

SW

SSW

SSE

S

(c) factor 3: petroleum volatilization and natural gas and biomass combustion

27

Fig. 5. Annual CPF plots for (a) vehicle emission, (b) coal combustion, (c) petroleum

28

volatilization and natural gas and biomass combustion. The blue lines indicate wind frequency,

29

EP

26

AC C

25

SE

and the red lines indicate CPF values.

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

30 31

Fig. 6. Source-attributed mass contribution and corresponding proportion in different seasons

AC C

EP

TE D

32

ACCEPTED MANUSCRIPT PAH concentrations were much higher in the heating season than in the other seasons Seasonal variations were related to meteorological conditions and source emissions The PMF-PSCF-CPF model improved source apportionment and provided the source region Three sources were identified, and vehicle emission contributed ¾ of the cancer risk

AC C

EP

TE D

M AN U

SC

RI PT

Source-attributed cancer risk was suggested for ranking priority control sources.