A systematic analysis of PM2.5 in Beijing and its sources from 2000 to 2012

Accepted Manuscript A Systematic Analysis of PM2.5 in Beijing and its Sources from 2000 to 2012 Baolei Lv, Bin Zhang, Yuqi Bai PII:

S1352-2310(15)30373-3

DOI:

10.1016/j.atmosenv.2015.09.031

Reference:

AEA 14112

To appear in:

Atmospheric Environment

Received Date: 15 February 2015 Revised Date:

7 September 2015

Accepted Date: 8 September 2015

Please cite this article as: Lv, B., Zhang, B., Bai, Y., A Systematic Analysis of PM2.5 in Beijing and its Sources from 2000 to 2012, Atmospheric Environment (2015), doi: 10.1016/j.atmosenv.2015.09.031. 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

2

A Systematic Analysis of PM2.5 in Beijing and its Sources from 2000 to 2012

3

Baolei Lv1,2, Bin Zhang1,2, Yuqi Bai1,2,3 *

1

4

1

5

Science, Tsinghua University, Beijing 100084, China

6

2

Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

7

3

State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex,

8

Beijing 100084, China

SC

M AN U

9 10 11

Corresponding author. E-mail address: [email protected] (Y. Bai); Tel.: 86 10 62795269; fax: 86 10 62795269.

16 17 18 19

EP

15

AC C

14

TE D

12 13

RI PT

Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System

20 21

1 / 32

ACCEPTED MANUSCRIPT 22

Abstract Particulate matter with an aerodynamic diameter of 2.5 micrometers or less (PM2.5) is the main

24

air pollutant in Beijing. To have a comprehensive understanding of concentrations, compositions and

25

sources of PM2.5 in Beijing, recent studies reporting ground-based observations and source

26

apportionment results dated from 2000 to 2012 in this typical large city of China are reviewed.

27

Statistical methods were also used to better enable data comparison. During the last decade, annual

28

average concentrations of PM2.5 have decreased and seasonal mean concentrations declined through

29

autumn and winter. Generally, winter is the most polluted season and summer is the least polluted

30

one. Seasonal variance of PM2.5 levels decreased. For diurnal variance, PM2.5 generally increases at

31

night and decreases during the day. On average, organic matters, sulfate, nitrate and ammonium are

32

the major compositions of PM2.5 in Beijing. Fractions of organic matters increased from 2000 to

33

2004, and decreased afterwards. Fractions of sulfate, nitrate and ammonium decreased in winter and

34

remained largely unchanged in summer. Concentrations of organic carbon and elemental carbon were

35

always higher in winter than in summer and they barely changed during the last decade.

36

Concentrations of sulfate, nitrate and ammonium exhibited significant increasing trend in summer

37

but in reverse in winter. On average they were higher in winter than in summer before 2005, and took

38

a reverse after 2005. Receptor model results show that vehicle, dust, industry, biomass burning, coal

39

combustion and secondary products were major sources and they all increased except coal

40

combustions and secondary products. The growth was decided both changing social and economic

41

activities in Beijing, and most likely growing emissions in neighboring Hebei province. Explicit

42

descriptions of the spatial variations of PM2.5 concentration, better methods to estimate secondary

43

products and ensemble source apportionments models to reduce uncertainties would remain being

AC C

EP

TE D

M AN U

SC

RI PT

23

2 / 32

ACCEPTED MANUSCRIPT 44

open questions for future studies.

45 46

Keywords: PM2.5; Beijing; carbonaceous materials; inorganic ions; source apportionments

48

RI PT

47

1. Introduction

As the capital of China, Beijing is a typical rapidly growing large city. Heavy local and regional

50

emissions, as well as geographical conditions (as shown in Fig. 1) result in severe air pollutions in

51

Beijing, which is regarded as one of the most polluted cities in the world (WHO, 2011). Since 2000,

52

especially after Beijing won the bid to host the 29th Olympic Games, the most rigorous ever local

53

emission control measures have been implemented to abate air pollutions (Wang et al., 2009a; Zhou

54

et al., 2010; Schleicher et al., 2012). Since Beijing is the most developed large city in China, and it

55

has an uncompromising emission control policy, understandings of how emissions in Beijing could

56

be controlled may provide valuable insights for the evaluation of air quality in other large cities in

57

China.

TE D

M AN U

SC

49

Based on ground measurements, particulate matter with an aerodynamic diameter of 2.5

59

micrometers or less (PM2.5) is the main air pollutant in Beijing. Major components of PM2.5 include

60

sulfate, nitrate, ammonium, organic carbon, elemental carbon, and mineral dust. According to

61

epidemiologic studies, PM2.5 can be inhaled and absorbed into lungs, causing adverse effects to

62

human health and increase of cardiopulmonary mortality and morbidity (Pope III et al., 2009; Fann

63

et al., 2012; Huang et al., 2012). Particulate matter can also influence local and regional weather and

64

climate by upwardly reflecting and absorbing incoming solar radiations and altering cloud properties

65

(Charlson et al., 1992). The hygroscopicity, optical property and reactivity of PM2.5 are significantly

AC C

EP

58

3 / 32

ACCEPTED MANUSCRIPT determined by compositions. Concentrations and compositions of PM2.5 have complex seasonal

67

variations. They are greatly influenced by emission sources and weather conditions. As climatic

68

conditions in a specific region barely changed within a decade or two, emissions played the most

69

important factor for variations of PM2.5 concentrations and compositions within that period. Thus,

70

source apportionments are essential to make PM2.5 control policy. Intensive measurements of PM2.5

71

concentrations and compositions in Beijing have been undertaken in the past decade. However, the

72

durations of these observations are mostly under 12 months, which is insufficient for understanding

73

its long-term trend and achieving a comprehensive evaluation. Hence a comprehensive summaries of

74

these PM2.5 observation studies are necessary and none have been carried out yet to date, to the best

75

of our knowledge.

M AN U

SC

RI PT

66

The main purpose of this paper is to review over sixty reported studies of PM2.5 in Beijing over

77

the last decade, with a focus on the concentrations, compositions, and sources of PM2.5. Details of the

78

data used in these studies are summarized in the supplementary materials.

79

2. Concentrations of PM2.5

80

2.1 Observational Studies and Methods

EP

TE D

76

The observational studies and their sampling and chemical analysis methods are specified in the

82

table ST.1 of the supplementary material. Generally, medium or mini volume samplers were used for

83

collecting ambient PM2.5 on Teflon filters or pre-baked quartz fiber filters. Teflon filters were

84

conditioned under constant temperature (~20℃) and relative humidity (~40%) for 24 hours or longer

85

before being weighed by analytical gravimeters. PM2.5 mass concentrations were determined by the

86

difference in the weight of filters before and after sampling. PM2.5 concentrations could also be

87

measured by Tapered Element Oscillating Microbalance (TEOM). In the TEOM instrument, the

AC C

81

4 / 32

ACCEPTED MANUSCRIPT sample stream was preheated to 50℃ and as a result semi-volatiles such as ammonium nitrate were

89

not measured (Sciare et al., 2007). Therefore to compensate for this loss, PM2.5 mass concentrations

90

determined by TEOM were multiplied with a factor of 1.3 (Green et al., 2001; Liu et al., 2014) in

91

this paper. Quartz filters were frequently used for organic and elemental carbon (OC) analysis using

92

the thermal/optical reflectance (TOR) method. Further organic speciation is determined by Gas

93

Chromatograph-Mass Spectrometer (GC/MS). Teflon filters were used for inorganic ions analyses by

94

ion chromatography (IC). Elements were usually ascertained with Teflon filters by X-ray

95

fluorescence (XRF), proton-induced X-ray emission (PIXE), Inductively Coupled Plasma-Atomic

96

Emission Spectrometry (ICP-AES) or Inductively Coupled Plasma – Mass Spectrometry (ICP-MS).

97

Although difference of errors with these observational studies is beyond the scope of this paper, to

98

quantify the trend of concentrations of PM2.5 mass, its compositions and sources contributions, we

99

used a robust non-parametric Theil–Sen estimator. The estimator is resistant to the presence of

100

outliers and it also does not rely on the distributions of the data. The significance of the estimated

101

trend (slope) was evaluated through P-value of the estimation, and P-value <0.05 was chosen as the

102

criteria of statistical significance in this paper.

103

2.2 PM2.5 Trends from 2000 to 2010

AC C

EP

TE D

M AN U

SC

RI PT

88

104

The PM2.5 concentrations observed from the studies that lasted for an entire year or longer are

105

exhibited in Fig.2a. The corresponding time-points are determined by the exact midpoint of these

106

studies. Observed annual average PM2.5 mass concentrations decreased during the last decade with

107

very strong statistical significance (P-value ~ 0). The first long-term observational studies in Beijing

108

were conducted by He et al. (2001) from September 1999 to September 2000 with a weekly

109

frequency. As shown in Fig.2a, the PM2.5 mass concentrations varied from 72.2µg/m3 in 2010, to 147 5 / 32

ACCEPTED MANUSCRIPT 110

µg/m3 observed in 2002. The annual average PM2.5 concentrations observed prior to and after 2005

111

(inclusive) were 112.7(±21.1) and 92.9(±14.3) µg/m3, respectively. After 2008, PM2.5 mass

112

concentrations have stayed at a level of around 90µg/m3. All observations far exceed the national standard level 2, i.e. annual average 35 µg/m3,

114

according to Ambient Air Quality Standards (GB3095-2012) issued in 2012, let alone the air quality

115

guideline of an annual average of 10 µg/m3 by the World Health Organization (WHO, 2005).

116

2.3 Seasonal and Diurnal Variations

SC

RI PT

113

The results from the studies lasted for over two weeks in each season are summarized in Fig.2b.

118

Seasonal average PM2.5 concentrations had exhibited significant (P-value < 0.05) decreasing trend in

119

winter and autumn. However, the absolute slope was smaller than that in the annual average in Fig.2a.

120

It even appeared an increasing trend in summer despite of being statistically insignificant. The reason

121

for the difference might be that serious pollution episodes with very high PM2.5 concentrations

122

seldom happened after 2005, which could happen in spring, autumn and winter before 2005 as in

123

Fig.2b. Disappeared pollution episodes in spring should be attributed to the establishment of the

124

Three North Shelterbelt (Zhang et al., 2010b). Before 2005, dust storms frequently occurred in spring

125

and during these events, PM2.5 mass concentrations can reach up to >500 µg/m3 with proportions of

126

mineral aerosols significantly increased (Han et al., 2005). Decreased concentrations in autumn and

127

winter are probably the result of strict controlling measures on coal combustions and biomass

128

burning. These measures should be the main causes of unchanged annual average concentrations of

129

SO2 as shown in Fig.3. Barely changed concentrations in summer is probably resulted from rapidly

130

growing vehicle ownership, as shown in Fig.4. Stable annual average NO2 concentrations during the

131

last decade, as in Fig.3, also verify the significance of vehicle emissions.

AC C

EP

TE D

M AN U

117

6 / 32

ACCEPTED MANUSCRIPT Average PM2.5 mass concentrations are generally at the highest level in winter and at the lowest

133

level in summer. From 2000 to 2010, the values were 123.3(±57.48), 92.1(±24.7), 108.9(±31.0) and

134

140.1(±42.5) µg/m3 in spring, summer, autumn and winter, respectively. However, higher

135

concentrations in summer were also monitored in 2001 (Wang et al., 2004), 2005 and 2006 (Wang et

136

al., 2008; Yu et al., 2011), which might be due to drastic secondary photochemical reactions (Yao et

137

al., 2002) and hygroscopic growth (Han et al., 2014). The seasonal variation of average PM2.5

138

concentrations in 2006-2010 reduced compared to that in 2000-2005, as shown in Fig.5. The ratio of

139

PM2.5 to PM10 is a useful indicator of contributions of secondary products. The ratio was smaller

140

(about 0.4-0.6) in spring and summer and larger (>0.7) in winter and during heavy haze events. The

141

larger ratio suggests larger contributions from secondary products, such as secondary organic carbon

142

and inorganic ions.

M AN U

SC

RI PT

132

Generally speaking, diurnal PM2.5 mass concentrations kept increasing during the night and

144

decreasing during the day (Wang et al., 2004; Zhao et al., 2009; Liu et al., 2014). The average

145

variations in 2014 in an urban site are depicted in Fig.6. The highest mass concentration is usually

146

observed at midnight as a result of decreasing boundary layer height (BLH) (Guinot et al., 2006;

147

Miao et al., 2009) and low wind speed. The lowest mass concentration was often observed at around

148

15:00 in the mid-afternoon at the urban site. However, at the background site, the lowest

149

concentration occurred in the morning. At the urban site, PM2.5 concentration increased slightly at

150

about 9:00 (Zhao et al., 2009) during morning rush hours and decreased sharply in the afternoon,

151

because of increasing BLH. These diurnal variations are similar to what was observed in other large

152

cities (Perez et al., 2002).

153

AC C

EP

TE D

143

Short-term and drastic variations of PM2.5 concentrations were significantly influenced by 7 / 32

ACCEPTED MANUSCRIPT meteorological factors. For instance, 1-min average PM2.5 concentrations exhibited a negative

155

correlation with wind speed and a positive relationship with relative humidity (Zhang et al., 2010c).

156

One another study by Wang et al. (2010) showed that under appropriate meteorological conditions,

157

particulate concentrations could keep increasing even for days. However, relations between

158

particulate pollution and meteorological conditions were typically discussed in qualitative methods

159

(Chen et al., 2008; Wang et al., 2010). Studies on quantitative analysis between variations of PM2.5

160

mass concentrations and meteorological parameters deserve more attentions.

161

2.4 Spatial Variations

M AN U

SC

RI PT

154

Comparisons of PM2.5 mass concentrations between urban and rural areas had been conducted

163

by Shi et al. (2003), Zheng et al. (2005), Song et al (2006a), Wang et al. (2006), Dong et al. (2009)

164

and Zhao et al. (2009). Generally, their findings indicate that PM2.5 mass concentrations in urban

165

areas were higher than those in rural or background areas. However, limited number of sampling

166

sites in these studies may lead to unconvincing, even inaccurate conclusions. For example, no

167

significant difference of pollution levels between rural and urban areas was found by Wang et al.

168

(2006) after 3-year long sampling activities. To better investigate spatial variation within urban areas,

169

more observation stations are required. For example, Zhang et al., (2013a) concluded that air quality

170

of Beijing deteriorates from north to south by interpolating PM2.5 data of 31 stations in autumn of

171

2012. Considering that primary pollutant emissions are mainly distributed in the urban areas of

172

Beijing (Zhao et al., 2012), the spatial variation indicated strong southbound regional transport of

173

particulate pollutants. Moreover, diverse data sources such as remote sensing products, land use data

174

and meteorological data could help better spatially interpolate PM2.5 mass concentrations. For

175

example, it has been proved to be effective by adopting Aerosol Optical Depth (AOD) data to obtain

AC C

EP

TE D

162

8 / 32

ACCEPTED MANUSCRIPT finer spatial distributions of PM2.5 (Liu et al., 2005; Schaap et al., 2009; Ma et al., 2015). In Beijing,

177

Zheng et al (2013) developed a new method to model the spatial variations of PM2.5 within urban

178

areas at a high resolution of 10km by assimilating multiple data sources such as points of interest,

179

meteorology and traffic flow data.

180

3. Species of PM2.5

RI PT

176

Mass balances of PM2.5, as shown in Fig.7, are either obtained directly from references or

182

reconstructed with chemical compositions by the method given by Yang et al (2011b). In Yang’s

183

method, compositions of PM2.5 were determined by eight types of speciation, which are organic

184

materials (OM), elemental carbon (EC), SO42-, NO3-, NH4+, crustal species, trace species and

185

unidentified species. OM is obtained by multiplying organic carbon (OC) by a factor of 1.4. Crustal

186

species are derived by multiplying corresponding enrichment factor to compensate for the weight of

187

oxygen in the mineral oxides. The explicit data for the figures in this section are shown in the tables

188

of ST.2, 3 and 4 in the supplementary material. In Fig.7, mass balances of PM2.5 are averaged if they

189

were observed within the same time frame but at different sites.

TE D

M AN U

SC

181

OM, EC and sulfate, nitrate and ammonium (SNA) are dominant species. On average they

191

constituted 67.1% of PM2.5 altogether during the last decade and, specifically, organic materials and

192

three SNA species respectively contributed 35.5 (±10.2), 11.7 (±2.2), 7.9(±1.3) and 5.2(±0.6) %,

193

when only the results of observations lasting for 1 year or longer were considered. Contributions of

194

OM increased before around 2004 and then decreased. Lu and his colleagues (2011) estimated that

195

biofuel and coal are the top two contributors among all fuel types. The field straw burning was

196

mostly located in central China and decreased since 2005 (Wang et al., 2015). Besides, coal

197

reductions in Beijing (Fig.3) also contributed the decreased OC fractions in Beijing since 2004.

AC C

EP

190

9 / 32

ACCEPTED MANUSCRIPT Factions of SNA have decreased in winter but changed by only a minimal margin in summer during

199

last decade. As for seasonal variations, OM constitutes larger fractions in winter than in summer,

200

which are respectively 35(±9.3) and 20.2(±6.2) %. Higher proportion of organic materials in winter

201

is mainly derived from coal combustions for domestic heating in North China (Yang et al., 2005; Lin

202

et al., 2009). For the three SNA species, they all constitute more in summer than in winter. On

203

average from 2000 to 2010, they constitute 13.2(±5.2), 9.0(±3.3) and 7.1(±3.2)% in winter and

204

18.2(±9.1), 14.1(±4.6) and 12.4(±8.0) in summer. Higher proportions of inorganic ions in summer

205

are primarily a result of accelerated aqueous heterogeneous reactions and in-cloud processes (Yao et

206

al., 2003), due to higher temperature and humidity in summer (Schleicher et al., 2010; Ianniello et al.,

207

2011). It is important to note that a higher proportion does not necessarily mean a higher

208

concentration. For example, some monitoring activities found a higher concentration of nitrate in

209

winter (Yao et al., 2002; Duan et al., 2006).

210

3.1 Carbonaceous Materials

TE D

M AN U

SC

RI PT

198

Carbonaceous materials, which include OM and EC, are usually the largest single fraction of

212

PM2.5 mass. Concentrations of OC and EC did not exhibit significant variation trend during the last

213

decade, with the P-value of the estimated slope much larger than 0.05. As shown in Fig.8,

214

concentrations of both OC and EC were higher in winter than those in other seasons. During the last

215

decade, average concentrations of EC were 5.8(±1.7) and 10.0(±4.1) µg/m3 in summer and in winter

216

respectively, and concentrations of OC were 15.4(±5.6) and 32.0(±8.6) µg/m3. Possible causes of the

217

variation include intensive coal combustions and unfavorable meteorological conditions. Low air

218

temperature in winter could condense the semi-volatile compounds. Low BLH and reduced

219

precipitations in winter could further increase the OC concentration. As for diurnal variations,

AC C

EP

211

10 / 32

ACCEPTED MANUSCRIPT concentrations of OC and EC were lower in the daytime than in the night, with the lowest occurring

221

in the afternoon and the peak at midnight. The higher concentration of OC and EC during nighttime

222

were mainly the result of heavy-duty diesel trucks and much lower BLH at night (Han et al., 2009;

223

Miao et al; 2009). OC is composed of primary organic carbon (POC) and secondary organic carbon

224

(SOC). Since the ratio of POC/EC is considered to be a constant in primary sources, higher ratio of

225

OC/EC in the daytime (Han et al., 2009) in Beijing probably indicates that 1) there is an increased

226

amount of SOC produced during the daytime, due to stronger solar radiation, and 2) more EC is

227

produced at midnight because of heavy diesel trucks. SOC is usually estimated using the following

228

equation (Lin et al., 2009), where (OC/EC)pri is the ratio of OC to EC in primary emissions.

M AN U

SC

RI PT

220

229

SOC = OC - POC=OC - EC×(OC/EC)pri

230

.

232

OC mainly comes from coal combustions (Yang et al., 2005), biomass burnings (Duan et al.,

233

2004), vegetative detritus (Dong et al, 2009), cooking (Wang et al., 2009b) and secondary reactions

234

(Lin et al., 2009). Biomass burning is widespread in Northern China. More OC was emitted through

235

biomass burning in autumn and early summer than that in other seasons. Its contribution to OC could

236

be up to 38%. Chinese cooking is believed to be another important source of OC. Its total OC

237

emissions was comparable to vehicular emissions (Wen and Hu, 2007). But in most source

238

apportionment studies, Chinese cooking was not included as a potential source. In a few studies

239

which considered Chinese cooking, their source apportionments for organic carbon showed that

240

Chinese cooking and coal combustion are major emission sources during summer and winter (Zheng

241

et al., 2005; Wang et al., 2009b). EC is mainly generated from coal combustions, vehicles and

AC C

EP

TE D

231

11 / 32

ACCEPTED MANUSCRIPT biomass burnings (Benjamin and Helene, 2006; Han et al., 2009). Correlations between

243

concentrations of OC and EC can be an indicator to help determine their potential sources. Strong

244

correlations indicate a greater probability that OC and EC are emitted from the same sources (Brook

245

et al., 2010). In Beijing, strong correlations appeared temporally in winter (Zhang et al., 2013) and

246

concentrated in urban areas (Yang et al., 2011a). A possible reason for this is that intensive coal

247

combustions in winter, widespread industrial emissions and vehicle exhausts in urban areas.

RI PT

242

Dan et al (2004) estimated that SOC constituted more than 50% of OC all the year round, while

249

Benjamin and Helene (2006) estimated the value to be 25% in winter and 45% in summer, Lin et al.

250

(2009) 19% in winter and 42% in summer, Chan et al (2005) 58% in summer. Generally,

251

concentrations of SOC in PM2.5 were higher in summer, as a result of stronger photochemical

252

reactions. However, high SOC concentration was also observed in winter by Dan et al. (2004) and it

253

was probably because of a large amount of precursors of semi-volatile organic compounds emitted

254

by intensive coal combustions. SOC usually contributed more in severe haze events, as revealed by a

255

number of intensive studies on severe haze pollutions in January, 2013 (Guo et al., 2014; Sun et al.,

256

2014). It worth noting that (OC/EC)pri varies in different sources and conditions (Yang et al., 2005).

257

Treating it as a constant, as most studies did, could derive significant uncertainties to estimate SOC.

AC C

EP

TE D

M AN U

SC

248

258

Organic species in PM2.5 mainly include n-alkanes, sugars, sterols, hopanes, aliphatic

259

dicarboxylic acids, polycyclic aromatic hydrocarbons (PAHs) and traces of other organic compounds

260

(Feng et al., 2005; He et al., 2006; Wang et al., 2009b). N-alkanes, which come from diverse sources,

261

were one of most abundant species of organics in PM2.5 and their concentrations were much higher in

262

winter, probably owing to coal combustions (He et al., 2006). Carbon preference index (CPI) was

263

employed to trace the sources of n-alkanes (Simoneit, 1986). CPI was found to be lower and close to 12 / 32

ACCEPTED MANUSCRIPT uniform (1.2 in winter, 1.8-2.0 in summer by Huang et al. (2006) and He et al.(2006 )) in winter,

265

revealing strong emissions from fossil fuels. Sugar species were also relatively rich in organics of

266

PM2.5 and their concentration was also higher in winter. Levoglucosan was widely used as an

267

indicator of biomass burning (Cheng et al., 2013) and its higher concentrations in autumn indicated

268

stronger emissions from biomass burning. PAHs are known, probably or possibly carcinogenic

269

(IARC class 1, 2A and 2B for most congeners) and its concentration was also much higher in winter,

270

as PAHs were mainly emitted from incomplete coal combustions (Ravindra et al., 2008). The

271

concentration of hopanes, often regarded as a tracer of coal combustion (Oros and Simoneit, 2000)

272

and motor vehicle exhaust (Simoneit, 1986), was also higher in winter.

273

3.2 Inorganic Ions

M AN U

SC

RI PT

264

SO42-, NO3- and NH4+ were three dominant inorganic ions in PM2.5 in Beijing. SO42- and NO3-

275

are mainly derived from secondary gas-phase (Yao et al., 2002; Wang et al., 2005) and aqueous-

276

phase (Sun et al., 2006; Zhou et al., 2012) oxidations of SO2 and NOx (Guinot et al., 2007). It has

277

been observed that their concentrations declined in winter and grew in summer over the last decade

278

except the insignificant positive slope for NO3- in summer, as shown in Fig.9. In summer, SO42- and

279

NO3- were mainly generated from strong photochemical reactions. They were tightly related to

280

intensive emissions from coal combustions and low dispersion conditions (Wang et al, 2005) in

281

winter. As shown in Fig.9, average concentrations of SNA species were significantly higher in winter

282

than in summer before 2005 with their P-values < 0.05 in t-tests. After 2005, concentrations of NH4+

283

became significantly higher in summer with a P-value of 0.026. For the observations after 2007,

284

concentrations of SO42- and NO3- were both significantly higher in summer than that in winter.

285

Specifically, average concentrations of SO42-, NO3- and NH4+ were 16.9(±4.3), 9.7(±4.3), 8.2(±2.8)

AC C

EP

TE D

274

13 / 32

ACCEPTED MANUSCRIPT µg/m3 in summer and 21.7(±7.4), 13.7(±3.5), 10.9(±4.6) µg/m3 in winter before (including) 2005.

287

After 2005, they were 22.4(±7.9), 11.7(±6.2) and 11.9(±4.7) µg/m3 in summer and 17.0(±12.5),

288

9.7(±4.1) and 6.8(±1.5) µg/m3 in winter respectively. Decreased concentrations of ions in winter

289

should be attributed to the practices to reduce emissions from coal burnings (Hao and Wang, 2005).

290

The reason for the increased concentrations of ions in summer is believed to be the rapid growth of

291

vehicle ownership in Beijing and neighboring provinces as shown in Fig.4.

RI PT

286

In terms of diurnal variations, SO42- usually kept increasing from early morning to late

293

afternoon, probably because of stronger solar radiations and higher concentrations of O3 in the

294

daytime. In urban areas, NO3- and NH4+ showed an inverse correlation and the maximum and

295

minimum concentration of NH4+ occurred in the morning and late afternoon, respectively, as a result

296

of accumulation of (NH4) NO3 in higher relative humidity (RH) conditions at night (Pathak et al.,

297

2012). However, in rural areas, concentrations of NO3- and NH4+ remained almost unchanged

298

throughout the day (Gao et al., 2013), because NOx, precursors of NO3-, is mainly emitted from

299

mobile sources (Lin et al., 2011). The similar diurnal variations were also reported by Hu et al.,

300

(2008) and Wu et al., (2009) in other temporal and spatial settings. During haze-fog events,

301

concentrations of SNA dramatically surged (Huang et al., 2014; Ji et al., 2014; Sun et al., 2014) and

302

sharply increasing concentrations of inorganic ions indicated strong secondary sulfate and nitrate

303

formations (Jung et al., 2009; Guo et al., 2014).

AC C

EP

TE D

M AN U

SC

292

304

Sulfur oxidation ratio (SOR) and NOx oxidation ratio (NOR, seen in ST.5 in the supplementary

305

material) have often been used to indicate the significance of the formation process of SO42- through

306

oxidations of SO2. Correlations between SOR or NOR and meteorological factors have been adopted

307

to reveal details about the secondary products formation reactions. In Beijing, the correlations were 14 / 32

ACCEPTED MANUSCRIPT strong between SOR and temperature, relative humidity and ozone. The noticeable correlations

309

between SOR and humidity were mainly because that the oxidation rate of SO2 was much higher in

310

aqueous-phase than in gas-phase (Newman, 1981). Therefore, hotness, humidity and intensive solar

311

radiation and increasing coal consumptions for steel production in neighboring province (Hebei

312

Province Statistic Yearbook, 2012) could be the reason for growing concentrations of SO42- in

313

summer in Beijing. A good correlation between NOR and NH4+ reveals that (NH4)NO3 was the main

314

form of NO3- in the particle phase. The reason for weak correlation between NOR and

315

meteorological factors was that higher temperature and humidity could not only influence oxidation

316

rate of NOx but also the partition of nitrite in gaseous, aqueous and solid phase (Ianniello et al., 2011).

317

NH4+ is formed largely through reactions of ammonia with nitric and sulfuric acid (Seinfield and

318

Pandis, 2006) and its precursor. NH3 is mainly emitted from anthropogenic sources, especially

319

agricultural activities and traffic (Asman et al., 1998; Meng et al., 2011). Therefore, increasing

320

concentrations of NH4+ in summer during last decade were associated with more traffic (Ianniello et

321

al., 2010), more frequent agricultural activities and accelerated animal metabolisms, which could be

322

revealed by growing grain yield per unit area and meat production in the neighboring Hebei province

323

(Hebei Statistic Yearbook, 2013).. As an alkali gas, NH3 in the atmosphere could be in order

324

neutralized by sulfuric, nitric and hydrochloric acids (Seinfield and Pandis, 2006), so that

325

correlations between NH4+ and major anions, including SO42-, NO3-, Cl- and their sum, could be used

326

to reveal neutralizations. When one anion had strong positive correlations with NH4+, the anion was

327

supposed to be completely neutralized by NH4+. In Beijing, SO42- was usually fully neutralized while

328

NO3- and Cl- could be partly neutralized (Yao et al., 2002; Ianniello et al., 2011). As for Cl- in PM2.5,

329

it was mainly associated with coal combustions. Its contributions from sea-salt particles were

AC C

EP

TE D

M AN U

SC

RI PT

308

15 / 32

ACCEPTED MANUSCRIPT minimal since urban areas of Beijing are distant (about 200km) from the sea. Concentrations of K+

331

were usually high in autumn and winter, which was ascribed to biomass burning. Atmospheric

332

chemical reactions are highly complex since various ion species and reactions are involved. There

333

are still many missing sciences in the field in spite of a large number of observations. The

334

elaboration of these chemical processes requires more intensive studies.

335

4. Source Apportionment of PM2.5

RI PT

330

There are many different methods to conduct source apportionments such as indicators and

337

models. Model methods can be further classified into receptor models, diffusion models and

338

atmospheric chemistry models. Indicators and receptor models are widely used because of their

339

modest complexity.

340

4.1 Indicators

M AN U

SC

336

Indicators are elements, ions, organics or their combinations used to identify and trace potential

342

sources, chemical processes or events of pollution. Since indicators are effective and efficient in

343

qualitative source apportionments, they are widely utilized in PM2.5 observational studies, as shown

344

in ST.1. One limitation with the indicators method is that they lack the ability to quantitatively

345

identify the sources. As there are too many indicators, they are summarized in the supplementary

346

material in ST5.

348

EP

AC C

347

TE D

341

4.2 Receptor Models

349

Three types of receptor models had been widely used to apportion the sources of PM2.5 in

350

Beijing since 2000. They are positive matrix factorization (PMF; Wang et al., 2005; Song et al., 16 / 32

ACCEPTED MANUSCRIPT 2006b; Song et al., 2006c; Wang et al., 2008; Yu et al., 2013; Zhang et al., 2013), chemical mass

352

balance model (CMB; Zheng et al., 2005; Zhu et al., 2005; Song et al., 2006b., Wang et al., 2009b)

353

and factor analysis (FA; Sun et al., 2004; Wang et al., 2005; Song et al., 2006a; Yu et al., 2011; Wang

354

et al., 2012). The percentage contributions to PM2.5 of potential sources in Beijing from these

355

receptor models are shown in Fig.10. Detailed data are shown in ST.6 of the supplementary material.

356

The source contributions to PM2.5 expressed as mass concentrations are also shown in SF.1 of the

357

supplementary material.

SC

RI PT

351

The annual average contributions from vehicles, dusts, industries, biomass burnings, coal

359

combustions and secondary products in the past decade were 7.5(±3.9), 14.6(±5.9), 9.1(±7.0),

360

9.8(±2.9), 15.4(±7.7) and 29.1(±11.0) %, respectively. Expressed in mass concentrations, they were

361

7.8(±2.3), 16.8(±8.66), 12.2(±9.8), 10.6(±4.0), 16.6(±7.8) and 32.2(±13.5) µg/m3. Even though the

362

year-long studies were in a small number, they focused on two periods, namely around 2000 and

363

2009-2010. Therefore, they could be used to investigate differences between the source contributions

364

respectively at the start and the end of a decade long timespan. Specifically, the annual average

365

contributions of vehicle exhausts prior to and after 2005 (a study from 2001 to 2006 was excluded in

366

both groups) were 6.8 and 10.6%, respectively. The increase was consistent with rapid growth of car

367

ownership in Beijing since 2005 as shown in Fig.4. Besides, vehicle exhausts contributed more in

368

summer than in other seasons, due to the strong solar radiation and high humidity. The industry

369

contribution grew from 6.9 (before 2005) to 15.5% (after 2005). Considering that large factories had

370

been gradually moved out of Beijing, emissions from neighboring booming steel industries in

371

neighboring Hebei province. For example, the steel production in Hebei increased from 12.3 million

372

tons in 2000 to 164.5 million tons in 2011. Dusts also contributed more after 2005 than before 2005,

AC C

EP

TE D

M AN U

358

17 / 32

ACCEPTED MANUSCRIPT with an annual average of 19% compared to 13.3% before 2005. The increase was probably derived

374

from expanding construction activities in Beijing which can be corroborated by rapid growth of floor

375

areas under construction as shown in Fig.4. Additionally, contributions of dusts to PM2.5 in spring,

376

32.4% (36.2µg/m3) on average, were much higher than that in other seasons (~14%, or 11.4µg/m3 on

377

average). This was apparently caused by high northwest wind speed, dry weather conditions and

378

occasional dust storms from the Gobi desert. In terms of biomass burning, its annual average

379

contribution slightly increased from 7.9% (before 2005) to 11.6% (after 2005) during the last decade.

380

Its contribution in autumn and winter was approximately 14% (14.8µg/m3), which is higher than 9%

381

(8.4µg/m3) observed in other seasons. The major reason for this seasonal variation could be wood

382

burnings for heating in winter and straw burnings for cropland cleaning in autumn in rural areas.

383

Annual average contributions from coal combustions remained almost unchanged, 15% on average

384

before 2005 and 15.5% afterwards. As discussed in Section 3, declined fractions of OM and SNA in

385

winter revealed reduced coal combustions in winter. However, after considering the booming steel

386

industries in Hebei province (Hebei Statistical Yearbook, 2013), it is understandable that annual

387

contributions from coal remained stable. Contributions of coal combustions in winter were 30.8%

388

(33.6µg/m3) on average, which is much higher than that of 7% (6.3µg/m3) in other seasons. This

389

seasonal variation corresponds well with that of OC concentrations, indicating coal combustions as

390

an important source of increased OC in winter. Contribution of secondary products was 41%

391

(39.7µg/m3) on average in summer, which is much higher than that of 26% (23.5µg/m3) in other

392

seasons, which was probably caused by strong solar radiation, high temperature and humidity in

393

summer. Contribution of vegetative detritus peaked (~0.9%) in autumn, probably as a result of fallen

394

leaves and crops harvest. Chinese cooking was regarded as the largest emission source in summer

AC C

EP

TE D

M AN U

SC

RI PT

373

18 / 32

ACCEPTED MANUSCRIPT 395

(Wang et al., 2009b). Although chemical compositions of emission from Chinese cooking have been

396

investigated (He et al., 2004; Zhao et al., 2007), the explicit emission volumes and contributions to

397

PM2.5 are not yet clear. Further studies on this aspect are highly expected. On account of deficient mathematical theories and unspecific source profiles in these methods,

399

source apportionment results of individual studies may exhibit significant differences even if the

400

studies are close in time (Zhang et al., 2015). Results of CMB model could be comparable because of

401

probable use of identical source profiles from EPA or studies in the US (Zheng et al., 2005). Source

402

apportionments by PMF and FA are partly subjective and could cause more uncertainties to inter-

403

compare. The major uncertainty of CMB method is caused by lack of local source profiles. FA and

404

PMF factors could be influence by the number of factors. Besides, unresolved factors also affect the

405

source apportionments results. Recent studies have shown that ensemble models encompassing

406

multiple methods exhibited better performance (Lee et al., 2009; Balachandran et al., 2012). These

407

models need to be considered in China to reduce uncertainties in PM2.5 source apportionments.

408

5. Conclusions

TE D

M AN U

SC

RI PT

398

In this paper, we reviewed over sixty studies on concentrations, compositions and source

410

apportionment of PM2.5 in Beijing during the last decade. We used proper statistical measurements to

411

reduce the uncertainties among these studies. The following conclusions are reached.

AC C

EP

409

412

• During the last decade, annual average concentrations of PM2.5 exhibited declining trend.

413

Seasonal average concentration decreased in autumn and winter with statistical significance.

414

Generally, winter was the most polluted season with average PM2.5 concentrations of

415

140.1(±42.5) µg/m3 and summer was the least polluted with 92.1(±24.7) µg/m3.

416

• Seasonal variance of PM2.5 concentrations decreased. For diurnal variance, PM2.5 generally 19 / 32

ACCEPTED MANUSCRIPT 417

tended to increase at night and decrease during the day. Morning peaks only occurred in urban

418

sites as a result of rush hour. Concentrations in urban sites were found higher than in rural

419

sites, but spatial variations of PM2.5 in Beijing requires further investigation. • OM, EC and SNA were identified as dominant species during last decade and they in total

421

constitute 67.1% of PM2.5 on average. Fractions of OM exhibit increased before around 2004

422

and then decreased, due to effective coal cleaning measures. Factions of SNA have decreased

423

in winter but has seen little change in summer. As for seasonal variations, OM constitutes

424

larger fractions in winter than in summer and SNA species constitute more in summer than in

425

winter.

M AN U

SC

RI PT

420

• Concentrations of OC and EC were higher in winter than in other seasons. They had barely

427

changed during last decade. The ratio of OC/EC was higher at daytime than that in nighttime,

428

revealing that SOC production was stronger during the daytime and the emission of EC was

429

intensive at night. Contributions of SOC to OC were around 20% in winter but 50% in

430

summer. The method to estimate SOC could cause uncertainties and need to be improved.

TE D

426

• Average concentrations of SNA species increased in summer and decreased in winter, with

432

relatively strong statistical significance. What’s more, before 2005 they were usually higher in

433

winter than in summer. After 2005, they took a reverse. The growth of SNA species in summer

434

could be both determined by emission changes in Beijing and neighboring province. SOR and

435

NOR could be used to reveal secondary reactions, but more explicit method need to be

436

developed.

AC C

EP

431

437

• Vehicle, dust, industry, biomass burning, coal combustion and secondary products were major

438

PM2.5 pollution sources. On average, their annual average contributions are higher before 2005 20 / 32

ACCEPTED MANUSCRIPT than those afterwards, except coal combustions and secondary products. The growth was

440

decided both changing social and economic activities in Beijing, and most likely growing

441

emissions in neighboring Hebei province. We suggest that future studies should focus on

442

building up more explicit and specific source profiles and ensemble source apportionment

443

models.

RI PT

439

In consideration that Beijing is one of a few the most developed large cities in China, it is

445

reasonable to assume that growing cities in northern China will generally follow the development

446

process that Beijing has experienced. Findings about the concentration, composition, and sources of

447

PM2.5 in Beijing is valuable in understanding and mitigating particulate pollutions in other growing

448

cities in North China.

449

Acknowledgement

M AN U

This study was supported by State Environmental Protection Key Laboratory of Sources and

TE D

450

SC

444

Control of Air Pollution Complex (No. SCAPC201406) and by Tsinghua University (20131089277,

452

553302001). The authors thank Dr. Yuxuan Wang for her helpful comments. We appreciate

453

anonymous reviewers for their constructive comments.

EP

451

AC C

454

455

Reference

456

Asman, W. A., Sutton, M. A., Schjørring, J. K., 1998. Ammonia: emission, atmospheric transport

457

and deposition. New Phytol. 139, 27-48.

458

Balachandran, S., Chang, H. H., Pachon, J. E., Holmes, H. A., Mulholland, J. A., Russell, A. G. 2013.

459

Bayesian-based ensemble source apportionment of PM2.5. Environ. Sci. Technol., 47(23),

460

13511-13518.

461

Benjamin, G., Helene, C. 2006. Characteristics of carbonaceous particles in Beijing during winter 21 / 32

ACCEPTED MANUSCRIPT 462 463 464

and summer 2003. Adv. Atmos. Sci, 23(3), 468-473. Brook, J., 2010. Temporal variability in fine carbonaceous aerosol over two years in two megacities: Beijing and Toronto. Adv. Atmos. Sci. 27, 705-714. Chen, Z. H., Cheng, S. Y., Li, J. B., Guo, X. R., Wang, W. H., Chen, D. S., 2008. Relationship

466

between atmospheric pollution processes and synoptic pressure patterns in northern China.

467

Atmos. Environ. 42, 6078-6087.

RI PT

465

Chen, Y., Schleicher, N., Chen, Y., Chai, F., & Norra, S. 2014. The influence of governmental

469

mitigation measures on contamination characteristics of PM 2.5 in Beijing. Sci. Total Environ.,

470

490, 647-658.

SC

468

Chan, C. Y., Xu, X. D., Li, Y. S., Wong, K. H., Ding, G. A., Chan, L. Y., Cheng, X. H., 2005.

472

Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in

473

Beijing. Atmos. Environ. 39, 5113-5124.

M AN U

471

Chan, C. K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42, 1-42.

475

Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., COAKLEY, J. J., Hansen, J. E., Hofmann,

478 479 480

Cheng, Y., Engling, G., He, K., Duan, F., Ma, Y., Du, Z., Liu, J., Zheng, M., Weber, R. J., 2013. Biomass burning contribution to Beijing aerosol. Atmos. Chem. Phys. 13, 7765-7781.

EP

477

D. J., 1992. Climate forcing by anthropogenic aerosols. Science 255, 423-430.

Dan, M., Zhuang, G., Li, X., Tao, H., Zhuang, Y., 2004. The characteristics of carbonaceous species

AC C

476

TE D

474

and their sources in PM2.5 in Beijing. Atmos. Environ. 38, 3443-3452.

481

Dong, X., Liu, D., Yuan, Y., Che, R., 2009. Spatial and Temporal Variations of Extractable Organic

482

Matter in Atmospheric PM10 and PM2.5 in Beijing. Environ. Sci. 30, 328-334. (in Chinese)

483

Duan, F., Liu, X., Yu, T., Cachier, H., 2004. Identification and estimate of biomass burning

484

contribution to the urban aerosol organic carbon concentrations in Beijing. Atmos. Environ. 38,

485

1275-1282.

486

Duan, F. K., He, K. B., Ma, Y. L., Yang, F. M., Yu, X. C., Cadle, S. H., Chan, T., Mulawa, P. A., 22 / 32

ACCEPTED MANUSCRIPT 487

2006. Concentration and chemical characteristics of PM2.5 in Beijing, China: 2001–2002. Sci.

488

Total Environ. 355, 264-275.

489 490

Fang, M., Chan, C. K., Yao, X., 2009. Managing air quality in a rapidly developing nation: China. Atmos. Environ. 43, 79-86. Fann, N., Lamson, A. D., Anenberg, S. C., Wesson, K., Risley, D., Hubbell, B. J., 2012. Estimating

492

the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk

493

Analysis 32, 81-95.

RI PT

491

Feng, J., Chan, C. K., Fang, M., Hu, M., He, L., Tang, X., 2005. Impact of meteorology and energy

495

structure on solvent extractable organic compounds of PM2.5 in Beijing, China. Chemosphere

496

61, 623-632.

M AN U

SC

494

Gao, X., Nie, W., Xue, L., Wang, T., Wang, X., Gao, R., Wang, W., Yuan, C., Gao, J., Ravi, K. P.,

498

2013. Highly Time-Resolved Measurements of Secondary Ions in PM2.5 during the 2008

499

Beijing Olympics: The Impacts of Control Measures and Regional Transport. Aerosol Air Qual.

500

Res. 13, 367-376.

503 504 505 506

the EU Stage 1 limit values for PM10. Atmos. Environ., 35(14), 2589-2593. Guinot, B., Roger, J., Cachier, H., Pucai, W., Jianhui, B., Tong, Y., 2006. Impact of vertical

EP

502

Green, D., Fuller, G., & Barratt, B. 2001. Evaluation of TEOM TM ‘correction factors’ for assessing

atmospheric structure on Beijing aerosol distribution. Atmos. Environ. 40, 5167-5180.

AC C

501

TE D

497

Guinot, B., Cachier, H., Sciare, J., Tong, Y., Xin, W., Jianhua, Y., 2007. Beijing aerosol: Atmospheric interactions and new trends. J. Geophys. Res.-Atmos. 112, D14314.

507

Guo, S., Hu, M., Wang, Z. B., Slanina, J., Zhao, Y. L., 2010. Size-resolved aerosol water-soluble

508

ionic compositions in the summer of Beijing: implication of regional secondary formation.

509

Atmos. Chem. Phys. 10, 947-959.

510

Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao. M., Zeng L.,

511

Molina, M., & Zhang, R. 2014. Elucidating severe urban haze formation in China. Proc. Natl. 23 / 32

ACCEPTED MANUSCRIPT 512

Acad. Sci. 111(49), 17373-17378.

513

Han, S., Kondo, Y., Oshima, N., Takegawa, N., Miyazaki, Y., Hu, M., Lin, P., Deng, Z., Zhao, Y.,

514

Sugimoto, N., 2009. Temporal variations of elemental carbon in Beijing. J. Geophys. Res.-

515

Atmos. (1984–2012) 114.

519 520 521

RI PT

518

characteristics of haze formation in Beijing. Atmos. Chem. Phys. 14, 10231-10248.

Hao, J., Wang, L. 2005. Improving urban air quality in China: Beijing case study. J Air Waste Manage, 55(9), 1298-1305.

SC

517

Han, X., Zhang, M., Gao, J., Wang, S., Chai, F., 2014. Modeling analysis of the seasonal

He, K., Yang, F., Ma, Y., Zhang, Q., Yao, X., Chan, C. K., Cadle, S., Chan, T., Mulawa, P., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35, 4959-4970.

M AN U

516

522

He, L. Y., Hu, M., Huang, X. F., Yu, B. D., Zhang, Y. H., & Liu, D. Q. 2004. Measurement of

523

emissions of fine particulate organic matter from Chinese cooking. Atmos. Environ., 38(38),

524

6557-6564.

526

He, L., Hu, M., Huang, X., Zhang, Y., Tang, X., 2006. Seasonal pollution characteristics of organic

TE D

525

compounds in atmospheric fine particles in Beijing. Sci Total Environ 359, 167-176. He, X., Li, C. C., Lau, A. K. H., Deng, Z. Z., Mao, J. T., Wang, M. H., Liu, X. Y., 2009. An

528

intensive study of aerosol optical properties in Beijing urban area. Atmos. Chem. Phys. 9, 8903-

529

8915.

AC C

EP

527

530

Hu, M., Wu, Z., Slanina, J., Lin, P., Liu, S., Zeng, L., 2008. Acidic gases, ammonia and water-

531

soluble ions in PM2.5 at a coastal site in the Pearl River Delta, China. Atmos. Environ. 42, 6310-

532

6320.

533 534 535 536

Huang, X., Hu, M., He, L., Tang, X., 2005. Chemical characterization of water-soluble organic acids in PM2.5 in Beijing, China. Atmos. Environ. 39, 2819-2827. Huang, X., He, L., Hu, M., Zhang, Y., 2006. Annual variation of particulate organic compounds in PM2.5 in the urban atmosphere of Beijing. Atmos. Environ. 40, 2449-2458. 24 / 32

ACCEPTED MANUSCRIPT 537

Huang, W., Cao, J., Tao, Y., Dai, L., Lu, S., Hou, B., Wang, Z., Zhu, T., 2012. Seasonal variation of

538

chemical species associated with short-term mortality effects of PM2.5 in Xi’an, a Central City

539

in China. Am J Epidemiol 175, 556-566. Huang, R. J., Zhang, Y., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y., et al. 2014. High secondary

541

aerosol contribution to particulate pollution during haze events in China. Nature, 514(7521),

542

218-222.

RI PT

540

Ianniello, A., Spataro, F., Esposito, G., Allegrini, I., Hu, M., Zhu, T., 2011. Chemical characteristics

544

of inorganic ammonium salts in PM2.5 in the atmosphere of Beijing (China). Atmos. Chem.

545

Phys. 11, 10803-10822.

SC

543

Ianniello, A., Spataro, F., Esposito, G., Allegrini, I., Rantica, E., Ancora, M. P., Hu, M., Zhu, T.,

547

2010. Occurrence of gas phase ammonia in the area of Beijing (China). Atmos. Chem. Phys. 10,

548

9487-9503.

M AN U

546

Ji, D., Li, L., Wang, Y., Zhang, J., Cheng, M., Sun, Y., Liu, Z., Wang, L., Tang, G., Hu, B., Chao, N.,

550

Wen, T., & Miao, H. 2014. The heaviest particulate air-pollution episodes occurred in northern

551

China in January, 2013: Insights gained from observation. Atmos. Environ., 92, 546-556.

TE D

549

Jung, J., Lee, H., Kim, Y. J., Liu, X., Zhang, Y., Hu, M., Sugimoto, N., 2009. Optical properties of

553

atmospheric aerosols obtained by in situ and remote measurements during 2006 Campaign of

554

Air Quality Research in Beijing (CAREBeijing

555

114.

2006). J. Geophys. Res.-Atmos. (1984–2012)

AC C

EP

552

556

Lee, D., Balachandran, S., Pachon, J., Shankaran, R., Lee, S., Mulholland, J. A., Russell, A. G. 2009.

557

Ensemble-trained PM2.5 source apportionment approach for health studies. Environ. Sci.

558

Technol., 43, 7023-7031.

559

Li, Xingru, Yuesi Wang, Xueqing Guo, and Yingfeng Wang. 2013. Seasonal variation and source

560

apportionment of organic and inorganic compounds in PM2.5 and PM10 particulates in Beijing,

561

China. J Environ Sci 25, no. 4 741-750. 25 / 32

ACCEPTED MANUSCRIPT 562

Lin, P., Hu, M., Deng, Z., Slanina, J., Han, S., Kondo, Y., Takegawa, N., Miyazaki, Y., Zhao, Y.,

563

Sugimoto, N., 2009. Seasonal and diurnal variations of organic carbon in PM2.5 in Beijing and

564

the estimation of secondary organic carbon. J. Geophys. Res.-Atmos. (1984–2012) 114.

567 568 569 570

the eastern United States using satellite remote sensing. Environ. Sci. Technol. 39, 3269-3278.

RI PT

566

Liu, Y., Sarnat, J. A., Kilaru, V., Jacob, D. J., Koutrakis, P., 2005. Estimating ground-level PM2.5 in

Lu, Z., Zhang, Q., Streets, D. G., 2011. Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010. Atmos. Chem. Phys. 11, 9839-9864.

Ma, Z., Hu, X., Huang, L., Bi, J., Liu, Y. 2014. Estimating ground-level PM2. 5 in China using

SC

565

satellite remote sensing. Environ. Sci. Technol., 48, 7436-7444.

Malm, W. C., Day, D. E., Carrico, C., Kreidenweis, S. M., Collett, J. L., McMeeking, G., Lee, T.,

572

Carrillo, J., Schichtel, B., 2005. Inter-comparison and closure calculations using measurements

573

of aerosol species and optical properties during the Yosemite Aerosol Characterization Study. J.

574

Geophys. Res.-Atmos. (1984–2012) 110.

M AN U

571

Meng, Z. Y., Lin, W. L., Jiang, X. M., Yan, P., Wang, Y., Zhang, Y. M., Jia, X. F., Yu, X. L., 2011.

576

Characteristics of atmospheric ammonia over Beijing, China. Atmos. Chem. Phys. 11, 6139-

577

6151.

TE D

575

Miao, S., Chen, F., LeMone, M. A., Tewari, M., Li, Q., Wang, Y., 2009. An observational and

579

modeling study of characteristics of urban heat island and boundary layer structures in Beijing.

580

J. Appl. Meteor. Climatol.. 48, 484-501.

582 583 584

AC C

581

EP

578

Newman, L., 1981. Atmospheric oxidation of sulfur dioxide: a review as viewed from power plant and smelter plume studies. Atmos. Environ. (1967) 15, 2231-2239. Oros, D. R., Simoneit, B. R. T. 2000. Identification and emission rates of molecular tracers in coal smoke particulate matter. Fuel, 79(5), 515-536.

585

Pathak, R. K., Wang, T., Wu, W. S., 2011. Nighttime enhancement of PM2.5 nitrate in ammonia-poor

586

atmospheric conditions in Beijing and Shanghai: Plausible contributions of heterogeneous 26 / 32

ACCEPTED MANUSCRIPT

588 589 590 591 592 593

hydrolysis of N2O5 and HNO3 partitioning. Atmos. Environ. 45, 1183-1191. Perez, P., Reyes, J., 2002. Prediction of maximum of 24-h average of PM10 concentrations 30h in advance in Santiago, Chile. Atmos. Environ. 36, 4555-4561. Pope III, C. A., Ezzati, M., Dockery, D. W., 2009. Fine-particulate air pollution and life expectancy in the United States. New Engl J Med 360, 376-386.

RI PT

587

Ravindra, K., Sokhi, R., Van Grieken, R., 2008. Atmospheric polycyclic aromatic hydrocarbons: source attribution, emission factors and regulation. Atmos. Environ. 42, 2895-2921.

Schleicher, N., Norra, S., Chai, F., Chen, Y., Wang, S., Stüben, D., 2010. Seasonal trend of water-

595

soluble ions at one TSP and five PM2.5 sampling sites in Beijing, China. Highway and Urban

596

Environment. Springer, pp. 87-95.

M AN U

SC

594

597

Schleicher, N., Norra, S., Chen, Y., Chai, F., Wang, S., 2012. Efficiency of mitigation measures to

598

reduce particulate air pollution—a case study during the Olympic Summer Games 2008 in

599

Beijing, China. Sci Total Environ 427, 146-158.

Sciare, J., Cachier, H., Sarda Estève, R., Yu, T., Wang, X., 2007. Semi

volatile aerosols in Beijing

TE D

600 601

(RP China): Characterization and influence on various PM2.5 measurements. J. Geophys. Res.-

602

Atmos. (1984

2012) 112.

Shi, Z., Shao, L., Jones, T. P., Whittaker, A. G., Lu, S., Berube, K. A., He, T., Richards, R. J., 2003.

604

Characterization of airborne individual particles collected in an urban area, a satellite city and a

605

clean air area in Beijing, 2001. Atmos. Environ. 37, 4097-4108.

607 608 609

AC C

606

EP

603

Simoneit, B. R., 1986. Characterization of Organic Constituents in Aerosols in Relation to Their origin and Transport: A Review. Int. J. Environ. An. Ch. 23, 207-237. Song, Y., Tang, X., Zhang, Y., Hu, M., Fang, C., Zen, L., Wang, W., 2002. Effects on Fine Particles by the Continued High Temperature Weather in Beijing. Environ. Sci. 23, 33-36. (in Chinese)

610

Song, Y., Zhang, Y., Xie, S., Zeng, L., Zheng, M., Salmon, L. G., Shao, M., Slanina, S., 2006a.

611

Source apportionment of PM2.5 in Beijing by positive matrix factorization. Atmos. Environ. 40, 27 / 32

ACCEPTED MANUSCRIPT 612

1526-1537.

613

Song, Y., Xie, S., Zhang, Y., Zeng, L., Salmon, L. G., Zheng, M., 2006b. Source apportionment of

614

PM2.5 in Beijing using principal component analysis/absolute principal component scores and

615

UNMIX. Sci Total Environ 372, 278-286.

618 619

RI PT

617

Song, Y., Tang, X., Xie, S., Zhang, Y., Wei, Y., Zhang, M., Zeng, L., Lu, S., 2006c. Source apportionment of PM2.5 in Beijing in 2004. J. Hazard Mater. 146, 124-130.

Streets, D. G., Fu, J. S., Jang, C. J., Hao, J., He, K., Tang, X., Zhang, Y., Wang, Z., Li, Z., Zhang, Q., 2007. Air quality during the 2008 Beijing Olympic Games. Atmos. Environ. 41, 480-492.

SC

616

Sun, Y., Zhuang, G., Wang, Y., Han, L., Guo, J., Dan, M., Zhang, W., Wang, Z., Hao, Z., 2004. The

621

air-borne particulate pollution in Beijing—concentration, composition, distribution and sources.

622

Atmos. Environ. 38, 5991-6004.

623 624

M AN U

620

Sun, Y., Zhuang, G., Tang, A., Wang, Y., An, Z., 2006. Chemical characteristics of PM2.5 and PM10 in haze-fog episodes in Beijing. Environ. Sci. Technol. 40, 3148-3155. Sun, Y., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., Yin, Y., 2014. Investigation of the sources and

626

evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res.-

627

Atmos., 119(7), 4380-4398.

TE D

625

Wang, J., Xie, Z., Zhang, Y., Shao, M., Zeng, L., 2004. The Research on the Mass Concentration

629

Characteristics of Fine Particles in Beijing. Acta Meteorological Sinica 62, 104-111. (in

630

Chinese)

632

AC C

631

EP

628

Wang, Y., Zhuang, G., Tang, A., Yuan, H., Sun, Y., Chen, S., Zheng, A., 2005. The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771-3784.

633

Wang, J., Zhang, Y., Shao, M., Liu, X., Zeng, L., Cheng, C., Xu, X., 2006. Quantitative relationship

634

between visibility and mass concentration of PM2.5 in Beijing. J. Environ. Sci. 18, 475-481. (in

635

Chinese)

636

Wang, Y., Zhuang, G., Chen, S., An, Z., & Zheng, A. 2007. Characteristics and sources of formic, 28 / 32

ACCEPTED MANUSCRIPT 637

acetic and oxalic acids in PM 2.5 and PM 10 aerosols in Beijing, China. Atmos Res, 84(2), 169-

638

181. Wang, H., Zuang, Y., Wang, Y., Sun, Y., Yuan, H., Zhuang, G., Hao, Z., 2008. Long-term

640

monitoring and source apportionment of PM2.5/PM10 in Beijing, China. . Environ. Sci. 20, 1323-

641

1327. (in Chinese)

643 644 645

Wang, W., Primbs, T., Tao, S., Simonich, S. L. M., 2009a. Atmospheric particulate matter pollution during the 2008 Beijing Olympics. Environ. Sci. Technol. 43, 5314-5320.

Wang, Q., Shao, M., Zhang, Y., Wei, Y., Hu, M., Guo, S., 2009b. Source apportionment of fine

SC

642

RI PT

639

organic aerosols in Beijing. Atmos. Chem. Phys. 9, 8573-8585.

Wang, F., Chen, D. S., Cheng, S. Y., Li, J. B., Li, M. J., Ren, Z. H., 2010. Identification of regional

647

atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure

648

pattern analysis. Environ. Modell. Softw. 25, 927-934.

M AN U

646

Wang, W., Jariyasopit, N., Schrlau, J., Jia, Y., Tao, S., Yu, T., Dashwood, R. H., Zhang, W., Wang,

650

X., Simonich, S. L. M., 2011. Concentration and photochemistry of PAHs, NPAHs, and OPAHs

651

and toxicity of PM2.5 during the Beijing Olympic Games. Environ. Sci. Technol. 45, 6887-6895.

652

Wang, Z. J., Han, L. H., Chen, X. F., Cheng, S. Y., Li, Y., Tian, C., Xie, H., 2012. Characteristics

653

and sources of PM2.5 in typical atmospheric pollution episodes in Beijing. J. Safety and Environ

654

12, 122r-126r. (in Chinese)

AC C

EP

TE D

649

655

Wang, L., Xin, J., Li, X., Wang, Y., 2015. The variability of biomass burning and its influence on

656

regional aerosol properties during the wheat harvest season in North China. Atmos Res., 157,

657

153-163.

658

Wu, Z., Hu, M., Shao, K., Slanina, J., 2009. Acidic gases, NH3 and secondary inorganic ions in PM10

659

during summertime in Beijing, China and their relation to air mass history. Chemosphere 76,

660

1028-1035.

661

Yang, F. M., He, K. B., Ma, Y. L., Zhang, Q., 2004. Characteristics of Mass Balance of PM2.5 29 / 32

ACCEPTED MANUSCRIPT 662

Chemical Speciation in Beijing. Environ. Chem. 23, 326-333. (in Chinese)

663

Yang, F., He, K., Ye, B., Chen, X., Cha, L., Cadle, S. H., Chan, T., Mulawa, P. A., 2005. One-year

664

record of organic and elemental carbon in fine particles in downtown Beijing and Shanghai.

665

Atmos. Chem. Phys. 5, 1449-1457. Yang, F., Huang, L., Duan, F., Zhang, W., He, K., Ma, Y., Brook, J. R., Tan, J., Zhao, Q., Cheng, Y.,

667

2011a. Carbonaceous species in PM2.5 at a pair of rural/urban sites in Beijing, 2005–2008.

668

Atmos. Chem. Phys. 11, 7893-7903.

RI PT

666

Yang, F., Tan, J., Zhao, Q., Du, Z., He, K., Ma, Y., Duan, F., Chen, G., 2011b. Characteristics of

670

PM2.5 speciation in representative megacities and across China. Atmos. Chem. Phys. 11, 5207-

671

5219.

M AN U

SC

669

672

Yao, X., Chan, C. K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K., Ye, B., 2002. The water-

673

soluble ionic composition of PM2.5 in Shanghai and Beijing, China. Atmos. Environ. 36, 4223-

674

4234.

Yao, X., Lau, A. P., Fang, M., Chan, C. K., Hu, M., 2003. Size distributions and formation of ionic

676

species in atmospheric particulate pollutants in Beijing, China: 1—inorganic ions. Atmos.

677

Environ. 37, 2991-3000.

TE D

675

Yu, Y., Schleicher, N., Norra, S., Fricker, M., Dietze, V., Kaminski, U., Cen, K., Stüben, D., 2011.

679

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China. J. Environ.

680

Monitor 13, 334-346.

AC C

EP

678

681

Yu, L., Wang, G., Zhang, R., Zhang, L., Song, Y., Wu, B., Li, X., An, K., Chu, J., 2013.

682

Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing.

683

Aerosol Air Qual. Res. 13, 574-583.

684 685 686

Zhang, R., Xu, Y., Han, Z., 2003. Inorganic chemical composition and source signature of PM2.5 in Beijing during ACE-Asia period. Chinese Sci. Bull. 48, 1002-1005. Zhang, W., Sun, Y., Zhuang, G., Xu, D., 2006. Characteristics and seasonal variations of PM2.5, 30 / 32

ACCEPTED MANUSCRIPT 687 688 689

PM10, and TSP aerosol in Beijing. Biomed. Environ. Sci. 19, 461. Zhang, M., Song, Y., Cai, X., 2007. A health-based assessment of particulate air pollution in urban areas of Beijing in 2000–2004. Sci. Total Environ. 376, 100-108. Zhang, T., Claeys, M., Cachier, H., Dong, S., Wang, W., Maenhaut, W., Liu, X., 2008. Identification

691

and estimation of the biomass burning contribution to Beijing aerosol using levoglucosan as a

692

molecular marker. Atmos. Environ. 42, 7013-7021.

RI PT

690

Zhang, R., Shen, Z., Cheng, T., Zhang, M., Liu, Y., 2010a. The elemental composition of

694

atmospheric particles at Beijing during Asian dust events in spring 2004. Aerosol Air Qual. Res.

695

10, 67-75.

SC

693

Zhang, X. X., Shi, P. J., Liu, L. Y., Tang, Y., Cao, H. W., Zhang, X. N., Hu, X., Guo, L. L., Lue, Y.

697

L., Qu, Z. Q., 2010b. Ambient TSP concentration and dust fall in major cities of China: Spatial

698

distribution and temporal variability. Atmos. Environ. 44, 1641-1648.

700 701 702

Zhang, W., Xu, D., Zhuang, G., Wang, W., Guo, L., 2010c. Characteristics of ambient 1-min PM2.5 variation in Beijing. Environ. Monit. Assess. 165, 137-146.

TE D

699

M AN U

696

Zhang, A., Qi, Q., Jiang, L., Zhou, F., Wang, J., 2013a. Population Exposure to PM2.5 in the Urban Area of Beijing. PloS one 8, e63486.

Zhang, R., Jing, J., Tao, J., Hsu, S., Wang, G., Cao, J., Lee, C., Zhu, L., Chen, Z., Zhao, Y., 2013b.

704

Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective.

705

Atmos. Chem. Phys. 13, 7053-7074.

AC C

EP

703

706

Zhang Y J, Zheng, M., Cai, J., Yan, C., Hu, Y., Russell, A.G., Wang, X., Wang, S., Zhang. Y., 2015.

707

Comparison and overview of PM2.5 source apportionment methods (in Chinese). Chin. Sci.

708

Bull., 60, 109–121

709

Zhao, X., Zhang, X., Xu, X., Xu, J., Meng, W., Pu, W., 2009. Seasonal and diurnal variations of

710

ambient PM2.5 concentration in urban and rural environments in Beijing. Atmos. Environ. 43,

711

2893-2900. 31 / 32

ACCEPTED MANUSCRIPT Zhao, B., Wang, P., Ma, J. Z., Zhu, S., Pozzer, A., Li, W., 2012. A high-resolution emission

713

inventory of primary pollutants for the Huabei region, China. Atmos. Chem. Phys. 12, 481-501.

714

Zhao, P. S., Dong, F., He, D., Zhao, X. J., Zhang, X. L., Zhang, W. Z., Yao, Q., Liu, H. Y., 2013.

715

Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing,

716

Tianjin, and Hebei, China. Atmos. Chem. Phys. 13, 4631-4644.

RI PT

712

717

Zheng, M., Salmon, L. G., Schauer, J. J., Zeng, L., Kiang, C. S., Zhang, Y., Cass, G. R., 2005.

718

Seasonal trends in PM2.5 source contributions in Beijing, China. Atmos. Environ. 39, 3967-3976. Zheng, Y., Liu, F., Hsieh, H., 2013. U-Air: when urban air quality inference meets big data,

720

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and

721

data mining. ACM, pp. 1436-1444.

M AN U

SC

719

722

Zhou, Y., Wu, Y., Yang, L., Fu, L., He, K., Wang, S., Hao, J., Chen, J., Li, C., 2010. The impact of

723

transportation control measures on emission reductions during the 2008 Olympic Games in

724

Beijing, China. Atmos. Environ. 44, 285-293.

Zhou, J., Zhang, R., Cao, J., Chow, J. C., Watson, J. G., 2012. Carbonaceous and Ionic Components

726

of Atmospheric Fine Particles in Beijing and Their Impact on Atmospheric Visibility. Aerosol

727

Air Qual. Res. 12, 492-502.

EP

729

Zhu, X. L., Zhang, Y. H., Zeng, L. M., Wang, W., 2005. Source identification of ambient PM2.5 in Beijing. R. Environ. Sci. 5, 000. (in Chinese)

AC C

728

TE D

725

32 / 32

AC C

EP

TE D

M AN U

Fig. 1 The terrains of Beijing and the surrounding big cities.

SC

RI PT

ACCEPTED MANUSCRIPT

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

Fig. 2 Annual and seasonal trend of average PM2.5 mass concentrations from 2000 to 2012 in Beijing

40

70

35

60

30

50

25

40

20

30

15

10 0 1999

10

SO2 annual average daily concentration NO2 annual average daily concentration SO2 annual emission

2001

2003

2005

2007

5 2009

2011

0 Year

RI PT

20

SO2 emissions (Kton)

Concentrations (µg/m3)

ACCEPTED MANUSCRIPT 80

Fig. 3 Annual SO2 emission in Beijing from 2004 to 2011 and annual average daily concentration of SO2 and NO2 from

M AN U

SC

2000 to 2011 (Beijing Statistical Yearbook 2012).

4000 3500

Coal consumption

Civil car ownership

Crude oil consumption

Floor area under construction

TE D

3000 2500 2000

EP

1500 1000

0 1999

AC C

500

2001

2003

2005

2007

2009

2011 Year

Fig. 4 Coal consumptions (10k tons), Crude oil consumption (10k tons), Civil car ownership (10k) and Floor area under construction (40k M2) in Beijing, 2000-2011 (Beijing Statistical Yearbook 2012)

2000-2011

300

2006-2011

2000-2005

300

300

250

250

200

200

200

150

150

150

100

100

100

50

50 Spring Summer Autumn Winter

RI PT

250

50

Spring Summer Autumn Winter

Spring Summer Autumn Winter

Fig. 5 Seasonal variations of PM2.5 in Beijing. (The dots represent average PM2.5 concentrations; the upper bars represent

EP

TE D

M AN U

SC

maximum values and the lower bars represent the minimum values of observed PM2.5 concentrations.)

PM2.5 concentratioin (µg/m3)

100

95

AC C

Concentratioins (µg/m3)

ACCEPTED MANUSCRIPT

90

85

80 0

4

8

12

16

20

Hour of the day Fig. 6 Diurnal variations of PM2.5 concentration in a typical urban site in Beijing.

AC C

EP

TE D

M AN U

Fig. 7 Reported Compositions of PM2.5 in Beijing from 2000 to 2010.

SC

RI PT

ACCEPTED MANUSCRIPT

Fig. 8 Reported OC and EC concentrations in summer and in winter in Beijing from 2000 to 2009.

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

Fig. 9 Reported concentrations of SO42- , NO3- and NH4+ in PM2.5 in summer and in winter in Beijing from 2000 to 2009.

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

Fig.10 Reported percentage contributions of PM2.5 from all sources in Beijing from 2000 to 2010.

ACCEPTED MANUSCRIPT

Highlights Data from over sixty studies on PM2.5 in Beijing from 2000 to 2012 are reviewed. Annual average PM2.5 concentrations decrease from 2000 to 2012.





Concentrations of OC and EC remain unchanged from 2000 to 2010. Concentrations of SNA decrease in winter and increase in summer from 2000 to




2010. Sources of PM2.5 correlate closely with social and economic development.

AC C

EP

TE D

M AN U

SC

RI PT





ACCEPTED MANUSCRIPT

ST.1 Summary of PM2.5 sampling activities from 2000 to 2010 in Beijing SAMPLING TIME

REFERENCE

R1

1999.06-2000.09.26

He et al., 2001

SAMPLING SITES

SAMPLING METHODS

RI PT

ID

Tsinghua University(THU);

Low flow rate sampler; Mass concentration: Teflon + gravimetry ; Water; soluble

Chegongzhuang(CGZ)

ions: Teflon + ion chromatography (IC); Elements: X-ray fluorescence (XRF);

OC/EC: thermal/optical reflectance (TOR)

R2

1999.05.28-07.01

Song et al., 2002

Guoan Hotel (GAH)

R3

1999.06-2000.09.28

Yao et al., 2002

THU and CGZ

Tapered Element Oscillating Microbalance (TEOM)

SC

Low flow rate sampler; Mass concentration: gravimetry; Water soluble ions:

Teflon + IC; Elements: XRF; OC/EC: TOR

Monthly periods over 2001, with 7 days

Shi et al., 2003

collection per month.

China University of Ming and

M AN U

R4

Mass: glass fiber and polycarbonate filter + microbalance

Technology(CUMT), Nankou and Ming Tomes Reservoir (MT)

R5

Summer of 2001 and the spring of 2002

Yao et al., 2003

R6

2001.03-2001.04

Zhang et al., 2003

IAP

R7

(1) 2001.07-08 and 2002.06-07; (2)

Dan et al., 2004

Beijing normal university (BNU) ; Capital steel

OC/TC: quartz filter + C/H/N elemental analyzer; Elements: Inductively Coupled

company (CS) ; Yihai garden

Plasma-Atomic Emission Spectrometry (ICP-AES); Ions: IC

2001.12-2002.01, 2002.12; and (3) 2003.03

PKU

Mini volume sampler; Mass: gravimeter ; Ions: IC; Elements: XRF; OC/EC: TOR Elements: Teflon + proton-induced X-ray emission (PIXE) method

R9

1997.11.07-1998.10.31

summer and winter from 2002 to 2003

Duan et al., 2004

Sun et al., 2004

March, June, September, December 2001

Wang et al., 2004

R11

1990.09-2000.06

Yang et al., 2004

R12

2003.01.16-05.05

Yu et al., 2004

R13

2001.08.23-2002.08.15

R14

2003.08.10-25

BNU, CS, YH

Sampling: glass fiber filter; OC/EC: TOR; Water soluble ions: flame atomic absorption (FAAS) OC/TC: quartz filter + C/H/N elemental analyzer; 23 elements: Teflon filter + ICP-AES; Ions: IC

CMA, PKU, EPB

OC/EC: TOR; Water soluble ions: IC; Elements: XRF and ICP

THU and CGZ

Water soluble ions: IC; Elements: XRF; OC/EC: TOR

BMEMC

Mass concentration: TEOM

Yu et al., 2004

THU and CGZ

OC/EC: C/H/N elemental analyzer; Ions: IC

Chan et al., 2005

Institute of Atmospheric Physics(IAP), Chinese

MINIVOL Samplers; Mass concentration: DustTrak portable monitor; OC/EC:

Academy of Sciences(CAS), Chinese Academy

TOR

AC C

R10

MT and the Temple of Heaven (TOH)

EP

R8

TE D

(YH) ;Miyun(MY) ; Pinggu(PG)

of Meteorological Sciences(CAMS) and Southern Observational Base(SOB) of CAMS

ACCEPTED MANUSCRIPT

R15

2002.07, 2002.11.11-18 and 2002.12.02-09

Feng et al., 2005

PKU and Chinese Research Academy of

Quartz filter + GC/MS

Environmental Science(CRAES)

R17

2001-2003

Han et al., 2005

BNU, CS and YH

2002.07.25-08.05, 2002.10.27-11.03, and

He et al., 2005

PKU

Huang et al., 2005

PKU

Medium volume sampler; Elements: ICP-AES

RI PT

R16

Large volume sampler; OC/EC: carbon analyzer; Organic species: GC/MS

2003.01.02-10. R18

2002.07.25-08.05, 2002.10.27-11.03, and

Large volume sampler; OC/EC: TOR; Organic species: GC/MS

SC

2003.01.03-10. From 2001 to 2003.

Wang et al., 2005

BNU, CS, YH, Miyun(MY) and Pinggu(PG)

Medium volume sampler; Ions: IC; Elements: ICP-AES

R20

1999.07-2000.06

Yang et al., 2005

THU and CGZ

Low volume sampler; Mass: analytical balance; Ions: IC; Elements: XRF; OC/EC:

R21

2000.01, 04, 07, 10

Zheng et al., 2005

DongSi EPB(EPB), PKU, The site close to the

Medium volume sampler; Ions: IC; Elements: ICP-MS; OC/EC: TOR; Organic

airport (NB)， Yong Le Dia(YLD) and MT

speciation: GC/MS

Beijing Union University (BUU), Chinese

Ions: IC; OC/EC: TOR; Organic speciation: GC/MS

R22

24-30 April, 18-25 August, 30 October to 4

Zhu et al., 2005

November 2000

M AN U

R19

TOR

Center For Disease Control And Prevention(CDC), CRAES

2001.08-2002.09, weekly

Duan et al., 2006

CGZ and THU

R24

2002.07.25-08.05, 2002.08.27-11.03, and

He et al., 2006

PKU

Large volume sampler; OC/EC: carbon analyzer; Organic species: GC/MS

PKU

Large volume sampler; Mass: gravimeter; OC/EC: carbon analyzer; Organic

2003.01.03-10

TE D

R23

Low flow rate sampler; Mass: gravimetric filter measurements; Water soluble ions: Teflon + IC; Elements: XRF; OC/EC: TOR

2001.08-2002.07

Huang et al., 2006

R26

2004.11.30-12.09

Sun et al., 2006

R27

2000.01, 04, 07, 10

Song et al., 2006a

MT, NB, PKU, EPB and YLD.

Medium volume sampler; Ions: IC; Elements: ICP-MS; OC/EC: TOR; Organic

R28

2000.01, 04, 07, 10

Song et al., 2006b

MT, NB, PKU, EPB and YLD.

Medium volume sampler; Ions: IC; Elements: ICP-MS; OC/EC: TOR; Organic

R29

2004.01.11-19 and 2004.08.11-19

Song et al., 2006c

PKU, Olympic Center (OLC), CS, Tongzhou

Medium volume sampler; Ions: IC; Elements: ICP-MS; OC/EC: TOR; Organic

(TZ), Liangxiang (LX), MT

speciation: GC/MS

EP

R25

AC C

BNU

species: GC/MS Medium volume sampler; Elements: ICP-AES; Ions: IC

speciation: GC/MS

speciation: GC/MS

ACCEPTED MANUSCRIPT

R30

2001.3, 6, 9, 12

Wang et al., 2006

Atmosphere

Exploration Base of China

Elements: ICP; Ions: XRF; OC/EC: TOR

PKU, DongSi (DS), Capital Airport (CA), Yongledian (YLD), MT R31

2003.1.10-28; 2003.8.27-9.12

Yu et al., 2006

Beijing Municipal Environmental Protection Monitoring Center (BMEMC)

July 2001 to April 2003

Zhang et al., 2006

BNU

R33

2002.03.12-04.26; 06.18-07.21;

Wang et al., 2007

BNU, CS and YH

Medium-volume sampler; Elements: ICP-AES; Ions: IC;

Xu et al., 2007

Beijing dance Institute (BDI) and CMA

Wang et al., 2008

BNU

Zhang et al., 2008

Sino-Japan Friendship Centre for

2003.1.29-2.28; 3.1-5.21; 8.4-31 ;

R35

summertime and wintertime from 2001 to

9.1-11.30; 12.1-2004.2.5

2006 R36

2002.7 – 2003.7

M AN U

09.05-10.06; 12.01-2003.01.05 R34

Mass: TEOM; OC/EC: real time Ambient Carbon Particulate Monitor

SC

R32

RI PT

Meteorological Administration (AEBCMA),

Ions: IC; Elements: ICP-AES;

Mass: TEOM;

Elements: NAA + PIXE; Ions: IC

Medium volume sampler; Elements: ICP-AES; Ions: IC

OC/EC: two-stage thermal method; Elements: GC-FID and GC/MS; Ions: IC

Environmental Protection (SJFCEP) R37

2005

Dong et al., 2009

CUGB, Chengfu Road(CFR),

Medium volume sampler

R38

R39

June 2006

winter, 2005.11.19-2006.01.16; spring,

Jung et al., 2009

2007.01.16-02.02 (Winter)

AC C

2006.09.28-10.31 2005.08.02-31 (Summer I) ,

PKU

Mass: aerosol spectrometer; OC/EC: semi-continuous OC and EC analyzer; Ions: IC. OC/EC: semi-continuous OC and EC analyzer

EP

2006.06.27-09.16; and autumn,

2006.08.16-09.10 (Summer II);

PKU

Lin et al., 2009

2006.03.24-05.16; summer,

R40

TE D

Qianmen(QM),CS, Zhongguancun(ZGC)

Wang et al., 2009a

PKU

EC/OC: TOR Primary organic matter (POM): GC/MS

R41

7.28 – 9.3, 2008; 9.13 – 10.7, 2008

Wang et al., 2009b

PKU

High volume sampler; Mass: gravimeter

R42

2005.1-2007.12

Zhao et al., 2009

Baolian(BL) and Shangdianzi (SDZ).

Mass: TEOM

R43

2006

Schleicher et al., 2010

MY, OLC, TH, TZ

Low volume sampler; Mass: mircobalance; water soluble ions: IC

ACCEPTED MANUSCRIPT

2007.01.23-02.14 and 2007.08.2-31

Ianniello et al.,2011

PKU

Low volume sampler; Ions: IC

R45

2008.07.28-09.03 and 2008.09.13-10.07

Wang et al., 2011

Peking University(PKU)

EC/OC analyzer; PAHs: gas chromatographic mass spectrometry (GC/MS)

R46

2005-2008

Yang et al., 2011a

THU and MY

mini volume sampler; OC/EC: Three-stage thermal method.

R47

April 18th 2005 to May 26th 2008

Yu et al., 2011

China University of Geosciences, Beijing (CUGB)

March 2005 to February 2006

Yang et al., 2011b

THU

R49

January 6 – 20, June 3 –July 30 2003

Cao et al., 2012

BNU

2008.07.28 – 2008.08.19; 2008.12.05 –

Song et al., 2012

North 4th Ring

R50

mass spectrometer (HR- ICP- MS); BC: ProTaqs Paramount TM Mini volume sampler; Mass: gravimeter; Ions: IC; Elements: XRF; OC/EC: TOR Mini volume sampler; Mass: gravimeter; Elements: XRF; Ions: IC; OC/EC: TOR Medium volume sampler; Mass: gravimeter; Ions: IC; Elements: XRF; OC/EC:

TOR

2009.11.30 – 2009.12.11

M AN U

2008.12.14; 2009.08.03 – 2009.08.14;

R51

December 2010 - March 2011

Wang et al., 2012

BNU

R52

3.16 – 4.6; 7.19 – 8.31, 10.23 – 11.13, and

Zhou et al., 2012

IAP

Gao et al., 2013

CRAES and Heishanzai(HSZ)

12.6 – 12.29, 2006. R53

7.11 – 8.25, 2008 at CRAES and 7.31 –

Mini volume sampler; Elements: high-resolution inductively coupled plasma;

SC

R48

RI PT

R44

TE D

8.25, 2008 at HSZ

Airborne particulate sampler; analytical balance; Elements: ICP/MS; Ions: IC; OC/EC: TOR Mini volume sampler; quartz-fiber filters; Mass: gravimeter; OC/EC: TOR; Ions: IC; Ions: ambient ion monitors (AIM)

R54

2010

Yu et al., 2013

BNU

Low volume sampler; Mass concentration: TEOM; Elements: PIXE.

R55

2009. 6, 7, 10 and 2010.1

Zhang et al., 2013

PKU

Mini volume sampler; Ions: IC; Elements: ICP-MS; OC/EC: thermal/optical

2008.07.01-09.20

Li et al., 2013a

Sep. 2006 – Aug. 2007

Li et al., 2013b

R58

2009.6, 7, 10 and 2010.1

Zhao et al., 2013

th

th

Medium volume sampler; Mass concentration: analytical balance; OC/EC: TOR; Elements: ACP-AES; Ions: IC.

IAP

High volume sampler; Mass: gravimeter; Organic species: GC-MS; Ions:IC.

Meteorological Stations

Medium-vol sampler; Elements: ICP-AES; Ions: IC; OC/EC: thermal optical

AC C

R57

THU

EP

R56

carbon analyzer

carbon analyzer.

R59

Sep. 30 , 2007 - Jan.. 4 , 2011

Chen et al., 2014

CARES

Mini volume sampler; Mass: gravimeter; Elements: HR-ICP-MS.

R60

Jan. 2004 – Dec. 2012

Liu et al., 2014

IAP

Mass: TEOM;

ST. 2 Compositions of PM2.5 (%) from 2000-2010

Article

Sites

Time

Organic

Elemental

Sulfate

Nitrate

Ammonium ion

Crustal

Trace

Unidentified

ACCEPTED MANUSCRIPT

Carbon

ion

ion

species

CGZ

1999-2000

30

7

15

10

He et al., 2001

THU

1999-2000

34

8

12

8

Duan et al., 2006

CGZ

2001-2002

45

11.6

10.4

6.5

Duan et al., 2006

THU

2001-2002

48

9.8

10.2

7

Huang et al., 2005

PKU

11-2002

49.5

4.2

6.7

10.7

Sun et al., 2004

BNU

12-2002

29.4

8.1

22.4

Sun et al., 2004

CS

12-2002

30.9

7.0

16.4

Sun et al., 2004

YH

12-2002

24.7

12.0

16.4

Sun et al., 2004

BNU

7-2002

17.9

6.7

Sun et al., 2004

CS

7-2002

13.6

8.0

Sun et al., 2004

YH

7-2002

17.8

7.8

Huang et al., 2005

PKU

8-2002

24.2

4.7

Huang et al., 2005

PKU

1-2003

51.0

5.8

Song et al., 2006c

PKU

1-2004

44.2

Song et al., 2006c

OLC

1-2004

41.6

Song et al., 2006c

PKU

8-2004

29.4

Song et al., 2006c

OLC

8-2004

29.8

Yang et al., 2011

THU

2005

28.95

Jung et al., 2009

PKU

8-2006

22.00

Zhang et al., 2013

PKU

2009

Zhao et a., 2013

MS

4-2009

Zhang et al., 2013

PKU

4-2009

Zhao et a., 2013

Meteo

7-2009

Zhang et al., 2013

PKU

7-2009

5

species

12

1

20

5

11

1

21

5.5

14.5

6.5

5.1

13.5

6.4

9.2

2.5

2.4

14.8

12.5

9.5

8.7

5.9

3.5

9.6

9.4

7.6

5.7

13.3

10.6

11.1

8.0

5.0

12.1

20.7

15.8

13.5

7.6

3.3

14.6

23.4

16.2

13.4

9.0

3.6

12.8

26.1

17.5

12.9

9.5

3.0

5.2

24.9

9.7

14.3

1.7

0.3

20.2

10.1

6.2

9.6

1.9

2.2

13.2

TE D

M AN U

SC

He et al., 2001

RI PT

materials

12.5

9.2

5.4

10.3

11.4

7.3

11.3

6.2

5.6

9.9

18.2

7.4

7.8

18.1

6.2

11.5

19.6

6.8

3.9

11.9

5.3

8.5

33.8

6.91

13.33

8.52

6.16

14.74

1.03

7.00

11.00

9.50

31.50

16.00

3.00

21.10

3.50

9.00

7.40

4.40

25.50

0.50

28.60

18.42

4.31

13.68

16.96

5.64

13.05

0.62

27.32

18.90

2.30

10.80

11.10

5.20

35.20

0.50

16.00

12.89

5.36

30.69

20.69

7.66

4.88

0.62

17.20

14.70

3.20

15.40

7.60

7.10

9.10

0.50

42.40

AC C

EP

7.1

20.36

ACCEPTED MANUSCRIPT

Meteo

10-2009

24.54

6.14

10.03

18.73

4.36

14.59

0.63

20.98

Zhang et al., 2013

PKU

10-2009

22.10

3.80

5.10

6.50

2.80

25.50

0.50

33.70

Zhao et a., 2013

Meteo

1-2010

30.02

5.71

11.38

13.67

4.17

9.46

0.57

25.02

Zhang et al., 2013

PKU

1-2010

28.60

4.50

5.20

4.40

2.40

32.40

0.40

22.10

AC C

EP

TE D

M AN U

SC

RI PT

Zhao et a., 2013

ST. 3 OC and EC concentrations (µg/m3) of PM2.5 during last decade in Beijing.

Site

Study

Urban

Song et al., 2006a

EC-Summer

Year

2000

EC-Winter 3.06

OC-Summer 2.03

OC-Winter 16.37

23.4

ACCEPTED MANUSCRIPT

NB

Zheng et al., 2005

2000

Urban

Yang et al., 2005

2001

5.2

PKU

Wang et al., 2009

2001

5.6

BNU

Dan et al., 2004

2002

5.3

CS

Dan et al., 2004

2002

7

YH

Dan et al., 2004

2002

8

CGZ

Duan et al., 2006

2002

5.5

CAS

Chan et al., 2005

2003

8.4

BMEMC

Yu et al., 2006

2003

CREAS

Cao et al., 2012

2003

Urban

Song et al., 2006c

2004

PKU

Lin et al., 2009

2005

THU

Yang et al., 2011

2005

THU

Yang et al., 2011

2006

PKU

Lin et al., 2009

IAP

Zhou et al., 2012

THU

Yang et al., 2011

THU

Yang et al., 2011

N4R

Song et al., 2012

N4R

Song et al., 2012

PKU Meteo

13

31

17

40.1

9.8

22.1

36.2

21.9

11.5

36.6

11.3

9.3

36

9.9

11.2

37.5

7.4

14.9

32.2

11.3

29.2

6.1

9.4

11.2

5.7

6.2

19.7

23.9

4

8.3

15.3

47.1

TE D

M AN U

SC

RI PT

12.9

6

20

5

14.5

15

36

7

14

15

45

4.5

2006

4.2

7

14.9

37.9

2007

9.5

14

19.5

25

2008

6

11

13

28

2008

3.34

11.7

11.7

39.3

2009

8.76

10.4

29.8

34.6

Zhang et al., 2013

2009

2.8

7.5

11.1

24.9

Zhou et al., 2013

2009

5.9

7.14

10.13

26.8

Site

Reference

AC C

EP

2006

CGZ

He et al., 2001

1999-2000

3

10

ST. 4 Concentrations of inorganic ions (µg/m ) of PM2.5 during last decade in Beijing. Time

SO4-Summer 17.14

SO4-Winter 24.87

NO3-Summer 4.59

NO3-Winter 15.35

NH4-Summer 5.7

NH4-Winter 7.8

ACCEPTED MANUSCRIPT

Duan et al., 2006

2001-2002

13.43

9.88

5.36

10.72

5.9

7.13

Urban

Wang et al., 2005

2001-2003

18.42

20.96

11.18

12.29

10.1

10.64

BNU

Sun et al., 2004

2002-2003

16

30.4

12.2

17

10.4

12.9

CS

Sun et al., 2004

2002-2003

19.2

23.1

13.3

13.5

11

13.3

YH

Sun et al., 2004

2002-2003

19.7

29.9

13.2

19.3

9.75

20.3

CREAS

Cao et al., 2012

2003

22.6

20

13.7

13.1

9.8

9.4

Urban

Song et al., 2006c

2004

8.7

12.7

3.7

8.3

3.3

6

IAP

Zhou et al., 2012

2006

29.9

20.3

15.2

13.3

9.3

7.3

PKU

Ianniello et al., 2011

2007

18.24

7.5

9.62

8.38

12.3

6.51

IAP

Li et al., 2013b

2007

12.6

43.7

5.3

5.3

21.4

7.4

N4R

Song et al., 2012

2008

14

12.7

4.15

7.51

7.72

8.55

N4R

Song et al., 2012

2009

24.6

12.1

12.8

8.54

13.5

8

PKU

Zhang et al., 2013

2009

23.5

8.5

11.8

7.3

11

4.5

Meteo

Zhao et al., 2013

2009

33.76

14.23

22.76

17.09

8.43

5.21

AC C

EP

TE D

M AN U

SC

RI PT

CGZ

ACCEPTED MANUSCRIPT

ST. 5 Indicators and tracers that are often used in PM2.5 source identifications.

Ca

Description

Reference

RI PT

Indicators

An indicator of construction activities.

He et al., 2001; Wang et al., 2005; Schleicher et al., 2010; Yu et al., 2011;

A tracer of coal burning and waste incineration. Concentration of Cl- in PM2.5 is higher in

Yao et al., 2003; Sun et al., 2004; Wang et al., 2005; Duan et al., 2006;

winter in Beijing.

Song et al., 2006a; Song et al., 2006b;

SC

Cl-

An indicator of coal burning. Its concentration was also higher in winter.

Al

An element tracer for long range transportation of crustal aerosols. Its concentration was higher in Spring and during haze fog from western direction.

+

K

Pb/Ti

M AN U

F-

Sun et al., 2004; Wang et al., 2005;

He et al., 2001; Wang et al., 2005; Sun et al., 2006; Song et al., 2006b;

An indicator of biomass burning, whose concentration was higher in Spring and Autumn

Watson and Chow, 2001; He et al., 2001; Duan et al., 2004; Song et al.,

when straw and fallen leaves were burning.

2006a; Cheng et al., 2013; Yu et al., 2013;

An indicator of relative importance of geogenic or anthropogenic sources. It was larger in

Schleicher et al., 2010; Yu et al., 2011;

Ca/Al

TE D

autumn and winter revealing a stronger anthropogenic contributions.

An indicator of contributions from dust storms and suspended construction materials. It was usually larger in summer and autumn.

An indicator of relative importance of crustal dusts and suspended construction materials. It

EP

Ca/Si

He et al., 2001; Wang et al., 2005; Song et al., 2006a; Song et al., 2006b;

He et al., 2001; Duan et al., 2006; Song et al., 2006b;

was smaller in winter and during dust storms.

NO3-/SO42-

A tracer of sources of mineral aerosols. A larger ratio in spring in Beijing revealed higher

AC C

Mg/Al

Sun et al., 2004; Han et al., 2005; Zhang et al., 2006; Yu et al., 2011;

contributions of long range transportation mineral aerosols.

Zhang et al., 2010a

An indicator of relative importance of mobile and stationary sources. The ratio was lower in

Arimoto et al., 1996; Yao et al., 2003; Wang et al., 2005;

winter, revealing a higher proportion of emissions coal burning. O3+NO2 and O3+NO SOR =

Indicators of oxidation ability.

Polidori et al, 2006; Lin et al., 2009;

An indicator of secondary transformation of sulfur. In Beijing, the ratio was larger than 0.1

Sun et al., 2004; Wang et al., 2005; Duan et al., 2006; Yao et al., 2003;

ACCEPTED MANUSCRIPT

n-SO42-/(n-SO42- +

except in winter and the largest value appears in summer.

Zhou et al., 2012; Ohta and Okita, 2006;

NOR = n-NO3-/(n-NO3-+

RI PT

n-SO2)

An indicator of secondary transformation of nitrate. The ratio was higher in summer but lower in winter and dust storm days.

n-NO2) SO42-)/NH4+

The ratio >1 indicates complete neutralizations of acid species (HNO3 and H2SO4) by ammonia.

R

EC and OC

An indicator of predominant single source of EC and OC. In Beijing, the larger correlation coefficient in winter indicated proximate sources of EC and OC.

EC

Dan et al., 2004; Chan et al., 2005; Yang et al., 2005; Yu et al., 2006;

An indicator of sources of carbonaceous aerosols. The ratio larger than 2.9 indicates strong

Dan et al., 2004; Chan et al., 2005; Yang et al., 2005; Duan et al., 2006;

secondary organics formations and it was higher in winter in Beijing.

Zhang et al., 2006; Lin et al., 2009; Chow et al., 1996; Strader et al.,

An indicator of primary emissions of OC. It is usually higher in winter because of lower

TE D

OC/EC

M AN U

2

Yao et al., 2002; Sun et al., 2004; Duan et al., 2006;

SC

(NO3-+

Wang et al., 2005; Sun et al., 2006;, Yao et al., 2003; Zhou et al., 2012;

1999 Yang et al., 2005; Yu et al., 2006; Lin et al., 2009; Han et al., 2009

BLH and during the nighttime heavy-duty diesel trucks. K(excess)/OC

An indicator to help identify biomass burnings from other OC emission sources. The ratio

Index (CPI) Levoglucosan

14

C

To discriminate fossil fuel and biogenic sources of the alkanes. CPI was lower and closer to

Feng et al., 2005; He et al., 2006; Huang et al., 2006; Wang et al., 2009a;

1 in winter indicated larger contribution of fossil fuel combustions.

Simoneit, 1986

AC C

Carbon Preference

EP

was higher in Beijing than in other cities.

Andreae, 1983; Duan et al., 2004;

A unique indicator for biomass burning. Its concentration was higher in winter and autumn

Duan et al., 2004; He et al., 2005; Zheng et al., 2005; He et al., 2006;

and lower in summer, mainly because of straw and wood burning.

Zhang et al., 2008; Wang et al., 2009a;

A tracer of modern carbon emissions like biomass burning. It reveals that the fraction of

Wang et al., 2005;

modern carbon is about 40%. Cholesterol

A tracer of cooking emissions especially during high-temperature processing of meat. The

He et al., 2006; Wang et al., 2009a; Zhao et al., 2007

ACCEPTED MANUSCRIPT

tracer shows a larger contribution of Chinese cooking in summer. Oros and Simoneit, 2000; Feng et al., 2005; He et al., 2006; Huang et al.,

H30 and H29)

significant in summer and coal burning significant in winter.

2006; Zhang et al., 2008; Wang et al., 2009a;

Picene (PIC)

A PAH tracer of coal combustion. Its concentration was much higher in summer than in

Levo/EC &

Indicators of biomass burning (Levo/EC) and coal combustions (IncdP/EC).

IncdP/EC

(BghiP or BgP) /benzo(e) pyrene (BeP) An indicator of vehicular exhaust. Higher ratio in summer reveals larger contributions of traffic to PAH.

M AN U

benzo(ghi)perylene

Oros and Simoneit, 2000; Huang et al., 2006;

Wang et al., 2009a;

SC

winter, indicating a larger contribution of coal burning.

RI PT

Organic tracers of fossil fuel combustions. In Beijing, they revealed vehicular emission was

Hopanes(mainly

Nielson, 1996; Feng et al., 2005; Huang et al., 2005; He et al., 2006;

Enrichment factor

An indicator to determine if the element (X) are from crustal sources. Ti, Fe, Si and Al were

Dan et al., 2004; Sun et al., 2004; Duan et al., 2006; Yu et al., 2011;

(EF)=(X/R)aerosol/(

adopted in Beijing as reference elements (R). The results showed that Na, Mg, Si, K, Ca, Ti

Wang et al., 2012; Zhang et al., 2010a

EP

TE D

and Fe were mainly from crustal sources.

AC C

X/R)crust

ACCEPTED MANUSCRIPT

ST. 6 Source apportionment results (%) during last decade in Beijing Site

Time

Method

Diesel

Vegetati

Metallurgi

Biomass

Coal

Cigarett

Other

Secondary

Chinese

Unidentifi

Total

Secondary

Second

Secondary

Soil

Road

and

ve

Dust

cal

aerosol

combust

e smoke

organic

Products

Cooking

ed

identified

sulfate

ary

ammonium

dust

Dust

gasoline

detritus

emission /

exhaust

ion

matter

Industry

RI PT

Article

nitrate

Urban

Jan-00

CMB

6.3

0.5

14.6

9.5

15.6

1.3

24.8

27.3

0.0

100.0

13.6

8.2

5.6

Zheng et al., 2005

Urban

Apr-00

CMB

4.7

0.5

36.5

2.0

3.1

0.6

7.0

28.1

17.5

82.5

11.7

10.7

5.7

Zheng et al., 2005

Urban

Jul-00

CMB

7.9

0.8

16.9

2.4

0.8

Zheng et al., 2005

Urban

Oct-00

CMB

7.9

1.8

10.9

12.6

8.8

2.0

Song et al., 2006b

Urban

2000

CMB

6.5

1.0

12.3

0.0

8.3

6.3

1.3

Song et al., 2006a

Urban

2000

PCA/APCS

6.3

0.0

7.5

6.9

0.0

27.8

Song et al., 2006a

Urban

2000

UNMIX

11.3

0.0

8.8

11.5

0.0

Song et al., 2006b

Urban

2000

PMF

6.0

0.0

7.6

5.1

11.0

Sun et al., 2004

Urban

2002

FA

5.7

13.3

6.1

Wang et al., 2005

Urban

2001-2003

FA

6.44

22

12.2

Song et al., 2006c

Urban

Jan-04

PMF

8.0

7.3

Song et al., 2006c

Urban

Aug-04

PMF

14.9

8.4

Wang et al., 2008

BNU

2001-2006

PMF

5.9

15.68

Wang et al., 2009a

PKU

Aug-06

CMB

7.5

0.2

Wang et al., 2009a

PKU

Jan-07

CMB

11.7

0.5

Zhang et al., 2013

PKU

2009

PMF

4

Zhang et al., 2013

PKU

Apr-09

PMF

5

Zhang et al., 2013

PKU

Jul-09

PMF

4

Zhang et al., 2013

PKU

Oct-09

PMF

4

Zhang et al., 2013

PKU

Jan-10

PMF

2

Yu et al., 2013

BNU

2010-Spring

PMF

16.9

41.4

16.3

83.7

28.5

5.5

7.4

4.4

37.7

13.8

86.2

14.1

16.9

6.8

11.2

33.9

19.2

80.8

16.9

10.8

6.5

0.0

0.0

24.3

27.3

72.7

24.5

0.0

0.0

29.5

14.6

85.4

17.2

0.0

0.0

33.8

19.4

80.6

16.2

15.2

3.6

53.7

17.6

82.4

7.29

26.24

25.8

74.2

15.4

38.1

18.5

12.8

87.2

8.6

9.8

12.8

11.3

32.2

20.3

79.7

24.1

8.0

11.8

16.7

27.4

13.7

86.3

12.7

14.7

8.4

1.2

7.2

75.5

24.5

24.9

11.5

6.4

45.0

55.0

25

12

18

26

AC C

M AN U

13.6

TE D

SC

Zheng et al., 2005

0.0

100.0

0.0

EP

15

8.82

23

14

19

5

34

3

32

6

1

54

18

42

17

7

13

16

12

7

57

6

37.6

6.2

7.4

10.0

0.0

0.0

22.0

10.6

10.1

ACCEPTED MANUSCRIPT

BNU

2010-Summer

PMF

17.8

0.0

18.8

7.3

11.8

4.5

0.0

0.0

39.8

0.0

100.0

3

6.2

Yu et al., 2013

BNU

2010-Autumn

PMF

16.6

0.0

20.8

5.9

12.3

16.4

0.0

0.0

28.1

0.0

100.0

4.9

5.4

Yu et al., 2013

BNU

2010-Winter

PMF

17.5

0.0

14.5

4.6

13.1

32.0

0.0

0.0

18.2

0.0

100.0

3.2

4.7

Yu et al., 2013

BNU

2010

PMF

17.2

0.0

22.9

6.0

11.2

15.7

0.0

0.0

27.0

0.0

100.0

Wang et al., 2012

BNU

2010-Winter

FA

13.2

35.5

14.3

85.7

10.7

AC C

EP

TE D

M AN U

SC

26.3

RI PT

Yu et al., 2013

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

SF. 1 PM2.5 concentrations from different sources using receptor models in Beijing.