Natural Silicon Isotopic Signatures Reveal the Sources of Airborne

Subscriber access provided by Grand Valley State | University

Article

Natural Silicon Isotopic Signatures Reveal the Sources of Airborne Fine Particulate Matter Dawei Lu, Qian Liu, Miao Yu, Xuezhi Yang, Qiang Fu, Xiaoshan Zhang, Yujing Mu, and Guibin Jiang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06317 • Publication Date (Web): 28 Dec 2017 Downloaded from http://pubs.acs.org on December 30, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 26

Environmental Science & Technology

1

ARTICLE

2

Natural Silicon Isotopic Signatures Reveal the Sources of

3

Airborne Fine Particulate Matter

4

Dawei Lua,b, Qian Liua,b,d,*, Miao Yua, Xuezhi Yanga,b, Qiang Fuc, Xiaoshan Zhanga, Yujing

5

Mua, Guibin Jianga,b,*

6 7

a

State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China b

8 c

9 10

d

University of Chinese Academy of Sciences, Beijing 100190, China

China National Environmental Monitoring Center, Beijing 100029, China

Institute of Environment and Health, Jianghan University, Wuhan 430056, China

11 12

Corresponding author: Prof. Qian Liu or Prof. Guibin Jiang. 18 Shuangqing Road, Haidian

13

District, Beijing 100085, China. Tel: +86-10-62849124. Email: [email protected] (Q. Liu);

14

[email protected] (G. Jiang).

15

Keywords: particulate matter; silicon; stable isotope; aerosol; source tracing

16

1

ACS Paragon Plus Environment


17

Abstract

18

Airborne particulate pollution is a critical environmental problem affecting human health

19

and sustainable development. Understanding of the sources of aerosol particles is of extreme

20

importance for regional air pollution control. Here we show that natural Si isotopic signature

21

can be used as a new tool to elucidate the sources of fine particulate matter (PM2.5). Through

22

the analysis of Si isotopic composition (δ30Si) of PM2.5 and its primary sources collected in a

23

typical pollution region–Beijing, we recognized the direct source tracing ability of Si isotopes

24

for PM2.5. The different primary sources of PM2.5 had different Si isotopic signatures. The

25

δ30Si value of PM2.5 ranged from -1.99‰ to -0.01‰ and showed a distinct seasonal trend

26

(isotopically lighter in spring/winter and heavier in summer/autumn). The variations in δ30Si

27

of PM2.5 revealed that Si-isotopically light sources were important sources for Beijing’s

28

severe haze pollution and that coal burning was a major cause for the aggregating haze

29

weather in spring/winter in Beijing. We also analyzed several typical haze events by using Si

30

isotopic signatures. As the first study on the natural Si isotopes in the atmospheric

31

environment, this study may reveal an important tool to advance the particulate pollution

32

research and control.

33

2


Page 2 of 26

Page 3 of 26


34

1. Introduction

35

In developing countries hundreds of millions of people are suffering from severe haze

36

pollution caused by airborne fine particulate matter (PM2.5). PM2.5, with aerodynamic

37

diameter smaller than 2.5 µm, can affect the Earth’s radiation budget, impact regional to

38

global weather, and enter the human lungs and the bloodstream, causing a wide range of

39

diseases and a significant reduction of life expectancy.1, 2 PM2.5 can be directly emitted from

40

natural or anthropogenic sources (primary particles) or formed secondarily in the atmosphere

41

from gaseous precursors (secondary particles).3 Understanding of the sources and dynamics

42

of PM2.5 is of extreme importance for haze pollution control. However, till now, the factors

43

controlling the high levels of PM2.5 present during severe haze events in typical pollution

44

regions such as China remain poorly understood.4 The contributions of different sources to

45

the severe haze pollution are still highly controversial, making it difficult to develop efficient

46

pollution control policies.

47

Silicon (Si) is the second most abundant element in the Earth’s crust with three stable

48

isotopes (28Si,

29

49

biogeochemical cycle of Si that is linked to the global carbon cycle via the chemical

50

weathering of silicate rocks,5-7 whereas other applications are rare. Furthermore, although the

51

Si cycle in continental and marine environments has been well elucidated, the Si isotopic

52

compositions in atmospheric environment remain unreported. We notice that Si is a major

53

element in PM2.5.8 More importantly, Si has some exceptional properties that make it different

54

from other elements. In contrast to other major elements in PM2.5 (i.e., C, O, N, and S) that

55

are more reactive and may be prone to isotope fractionation during complex atmospheric

Si, and

30

Si). Natural Si stable isotopes have been used to quantify the

3



56

processes,9-11 Si has only one valence state (Si4+) and does not form volatile compounds

57

readily in terrestrial systems, so the potential of isotope fractionation of Si in the atmosphere

58

is limited by its low volatility, chemical inertness, and invariant bonding environment.12 This

59

suggests that the Si isotopic signatures may be maintained during the emission and growth

60

process of PM2.5. Therefore, we hypothesize that Si isotopic composition can be used as a

61

unique tool to unravel the information on the sources of PM2.5.

62

Here we investigated for the first time the natural abundance and stable isotopic

63

composition of Si in PM2.5 and its primary sources. The samples were collected during 2003

64

and 2013 around Beijing, China (see Fig. S1). This region is important in that it has

65

experienced extremely severe haze pollution in the past decades.13 The 2013 is a typical year

66

when the annual mean PM2.5 concentration in Beijing reached an unprecedentedly high

67

level14 and the 2003 was selected for a comparison. We show that natural Si signatures can be

68

used as a new tool to elucidate the sources of PM2.5. The seasonal variations in Si isotopic

69

composition of PM2.5 reveal the major causes for the severe haze events. Our results provide

70

new insights into the Beijing’s severe haze pollution; more importantly, the methodology

71

developed herein is also applicable to other developing countries that are suffering from air

72

pollution to help them develop effective pollution control strategies.

73 74

2. Materials and Methods

75

2.1. Chemicals and reagents

76

The Si isotope standard NIST SRM-8546 and the standard material of urban atmospheric

77

particulate matter (NIST-1648a) were purchased from the National Institute of Standards and 4


Page 4 of 26

Page 5 of 26


78

Technology (Gaithersburg, MD). The secondary Si isotope standard IRMM-017 was

79

purchased from the Institute for Reference Materials and Measurements (GEEL, Belgium).

80

The element calibration standard solution was purchased from Agilent (Santa Clara, CA).

81

The rare earth element calibration standard GSB 04-1789-2004 was purchased from the

82

National Testing Center of Nonferrous Metals and Electronic Materials Analysis (Beijing,

83

China). Sodium hydroxide was from Beijing Ruikai Co. (Beijing, China). Nitric acid was

84

purchased from Merck (Darmstadt, Germany). Hydrochloric acid was from Beijing

85

Chemicals Works (Beijing, China). Hydrogen peroxide was from Sinopharm Chemical

86

Reagent Co. (Shanghai, China). Ultrapure water (18.3 MΩ·cm) produced from a Milli-Q

87

Gradient system (Millipore, Bedford) was used throughout the experiments.

88

2.2. Sampling and characterization of PM2.5

89

PM2.5 samples were collected around the Beijing region, China (Fig. S1) in random days

90

(n = 100) of haze weather in 2003 and 2013. For PM2.5 samples of 2003, ca. 110 m3/day of air

91

was collected on polypropylene membrane filters (Ø = 90 mm) using a medium-volume air

92

sampler (Beijing Geological Instrument Co., China) at a flow rate of 77.6 L/min. For PM2.5

93

samples of 2013, ca. 80-100 m3/day of air was collected onto Whatman 3-5 Teflon membrane

94

filters (Ø = 47 mm, Maidstone, UK) by using a low-volume air sampler (Partisol 2025i,

95

Thermo Fisher, USA) at a flow rate of 16.7 L/min. The mass of the collected PM2.5 was

96

measured by the giant gravimetric balance method.15 The characterization of PM2.5 samples

97

was performed on a Hitachi S-3000N scanning electron microscope (Tokyo, Japan) equipped

98

with an energy dispersive X-ray spectroscope (EDX) operating at an accelerating voltage of

99

15 kV. 5



100

2.3. Sampling of primary sources of PM2.5

101

We selected seven sources as the major primary sources of PM2.5 according to the

102

literature reported,14, 16-19 including coal burning, industrial emission, biomass burning, urban

103

fugitive dust, soil dust, construction dust, and vehicle emission. The samples of these sources

104

were collected around the Beijing region (Fig. S1) following the previously reported

105

methods.20-26 Briefly, the coal burning samples were collected from burning of honeycomb

106

briquette and lump coal, which are the main fuels used around the Beijing region. The coal

107

burning fly ashes were first drawn through a dilution system,20 and then the formed

108

particulate matter was collected onto polypropylene membrane filters by an impactor at a

109

flow rate of 36 L/min. The industrial emission samples were collected from a steel plant and a

110

power plant in Tangshan, Hebei, China. The emitted dusts were collected by air sleeves or

111

electrostatic filters equipped in the chimneys. The biomass burning samples were the burnt

112

straws collected in Daxing and Tongzhou District, Beijing. The soil dust samples were

113

collected at different depths of soil (5 and 30 cm) and at different directions in the Beijing

114

region. The urban fugitive dust samples were collected at a height of 20 m by using an SYC-3

115

auto-sampler (Laoshan Instruments Co., Qingdao, China). The construction dust samples

116

were gathered at several construction sites in the Beijing city and mainly consisted of fine

117

sands, cement dusts and brick dusts, and were then sieved through a Tyler 200 mesh sieve.

118

The vehicle emission samples were collected by sampling the air onto Whatman Teflon

119

membrane filters (Ø = 102 mm, Maidstone, UK) in the middle section of the Tanyugou

120

Tunnel (3455 m in length) in Changping District of Beijing using a high-volume air sampler

121

(Echo Hi-Vol, Tecora Co., Milan, Italy) at a flow rate of 280 L/min. Since only motor 6


Page 6 of 26

Page 7 of 26


122

vehicles are permitted to pass through the tunnel and that the concentration of PM2.5 outside

123

the tunnel was very low (< 15 µg/m3), the collected airborne solids inside the tunnel could

124

represent the vehicle emission. This approach has also been used in previous studies.25

125

2.4. Sample preparation

126

For the measurements of Si concentration and isotopic composition, the samples were

127

first placed in a silver crucible and dried in a muffle furnace at 1000 K for 10 min. For the

128

samples collected on polypropylene membrane filters, the filters were sheared into strips and

129

heated together with the samples. Afterward, the samples were digested using the alkali

130

fusion method with NaOH.27, 28 Briefly, the samples were mixed with solid NaOH at a ratio of

131

1:20 in a silver crucible, and then the mixture was heated in a muffle furnace at 1000 K for 10

132

min followed by cooling down to room temperature. The obtained “fusion cake” was

133

dissolved with 2 mL of water and stored in the dark for 24 h. Then, an HCl solution was

134

added to the crucible to make the final pH of the solution to be ~2. The solution was stored in

135

acid-free centrifuge tubes for the isotope ratio measurement. The blank polypropylene and

136

Teflon membrane filters and solid NaOH have also been analyzed using the same procedures

137

to ensure that they caused no interference to the Si isotope ratio measurement.

138

To eliminate the interference from sample matrix, the samples were purified by using the

139

cation exchange chromatographic method as reported previously.27, 28 Briefly, the cation resin

140

(Dowex 50WX8, 200-400 mesh) in H+ form were activated for 12 h and filled to a 1.8 mL

141

resin bed in a BioRad column. Then the resin was rinsed with HCl, HNO3 solution, and water

142

until the pH of the eluate was close to neutral. Afterwards, 1 mL of the sample solution was

143

loaded to the 1.8 mL of acid-cleaned resin and then eluted with 2 mL of water. The cation 7



Page 8 of 26

144

exchange resin could remove cationic species from the Si species, because the Si in solution

145

are mostly in the form of non-ionic Si(OH)4 and anionic H3SiO4-.27 The recovery of the

146

column purification step obtained with the standard reference material IRMM-017 was >95%.

147

The whole sample pretreatment procedures were also tested using the standard reference

148

material NIST-1648 (urban atmospheric particulate matter), and the recovery of Si was in the

149

range of 89.9-96.2%.

150

2.5. Measurement of Si concentration

151

The concentration of Si in PM2.5 and its primary source samples was measured on an

152

Agilent 8800 inductively coupled plasma mass spectrometer (Santa Clara, CA, USA). The

153

clean blank membrane filters have also been analyzed using the same procedures to subtract

154

the background signals from the samples.

155

2.6. Measurement of Si isotope ratios

156

The stable Si isotopic composition was measured by multi-collector inductively coupled

157

plasma mass spectrometry (MC-ICP-MS)27 on a Nu Plasma II MC-ICP-MS (Wrexham, UK)

158

equipped with 16 Faraday cups coupled to a DeSolvation Nebulizer System (DSN-100)

159

working in medium-resolution mode. All samples were diluted with HCl solution (pH ~2) to

160

a Si concentration of ~300 ng/mL to obtain the intensity of 28Si of ~2 V (the intensities of 29Si

161

and 30Si were ~0.11 and ~0.08 V). The samples were injected by using a PFA nebulizer at a

162

flow rate of 70 µL/min in dry mode. The optimized parameters of the instrument and the

163

Faraday cup configuration are shown in Table S1. The intensity of

164

blank HCl solution (pH 2), the polypropylene and Teflon membrane filters, and solid NaOH

165

was all less than 0.04 V. This intensity was negligible compared to the intensities of the 8


28

Si obtained with the

Page 9 of 26


166

samples (~2 V) and caused no interference to the Si isotope ratio measurement. The

167

procedure blank intensities of the Faraday detector noise were subtracted in the electrostatic

168

analyzer for 30 s prior to each measurement. Three parallel measurements were made for all

169

samples (n = 3). A rinse with HCl solution (pH 2) for 120 s after each measurement was used

170

to reduce the background intensity to < 0.03 V. The Si isotopic composition in a sample was

171

expressed as a δ value (δ30Si and δ29Si) relative to a standard material (NIST SRM-8546) as

172

follows: Si/

Sisample

173

Si = Si/

Si

− 1 × 1000

(1)

174

! Si = "#

− 1 × 1000

(2)

175

The mass bias was corrected based on a standard-sample-standard bracketing method as

176

described previously.29 The δ value of a sample was calibrated based on the mean values of

177

the isotope ratios in two adjacent standard measurements. The method has been validated

178

using two standard reference materials, NIST SRM-8546 and IRMM-017. A δ30Si value of

179

-0.08 ± 0.11‰ (mean ± 2SD, n = 40) was obtained in a NIST SRM-8546 solution with a Si

180

concentration of 300 ng/mL. The δ30Si value of IRMM-017 was measured to be -1.38 ± 0.18‰

181

(mean ± 2SD, n = 36). This value was very close to the previously reported results,28 proving

182

that our method was highly accurate and precise.

standard "# Si/

Sisample Si/

Si standard

183 184

3. Results and Discussion

185

3.1. Abundance and isotopic composition of Si in PM2.5

186

As shown in Fig. 1a, the daily PM2.5 concentration in 2013 ranged from 1.6 to 530 µg/m3

187

with an annual mean value of up to 106.4 µg/m3 (n = 360). Weather with heavy haze (PM2.5 > 9



188

200 µg/m3) was much more frequent in spring/winter than in summer/autumn. Scanning

189

electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) measurements

190

show that PM2.5 is highly diverse in morphology and elemental composition, in which Si is a

191

ubiquitous element (Fig. S2). No seasonal trend was observed in total Si in PM2.5 (Fig. 1b) or

192

in abundance of Si in PM2.5 (Fig. 1c). Note that the total Si in PM2.5 is expressed as SiPM2.5

193

relative to air volume (in µg/m3) and the Si abundance is given as mass fraction (in %). For

194

comparison, we also measured the SiPM2.5 for 2003, which was overall higher than that for

195

2013 (P < 0.001; Fig. S3) and showed a peak value in April (Fig. 1b). This result was

196

consistent with the decreasing frequency of sand-dust weather in past decades in Beijing,30, 31

197

because the most common constituent of sand is silica (usually in the form of quartz).

198

We then measured the Si isotopic composition of PM2.5 in 2003 and 2013. The data for

199

all samples produced a mass-dependent line (Fig. 1d). The annual mean δ30Si value showed

200

no significant difference between 2003 and 2013 (P = 0.52; see Fig. S3). Notably, as shown

201

in Fig. 1e-f, the δ30Si value changed dramatically over different months. A similar seasonal

202

trend was observed for 2003 and 2013, i.e., the Si in PM2.5 was isotopically lighter in

203

spring/winter and heavier in summer/autumn (see Fig. S4 for P values). It was interesting to

204

note that in both 2003 and 2013 the ranges of δ30Si value in spring/winter were much wider

205

than those in summer/autumn, indicating that the source contributions to PM2.5 in

206

summer/autumn were more stable than those in spring/winter. Specifically, the δ30Si ranges in

207

spring/winter in 2013 were wider than those in 2003 (Fig. 1e-f), suggesting that the primary

208

sources of PM2.5 in Beijing were becoming more complex from 2003 to 2013.

209

3.2. Si isotopic signatures of primary sources of PM2.5 10


Page 10 of 26

Page 11 of 26


210

To link PM2.5 to its sources via Si, we investigated both Si abundance and isotopic

211

signatures of seven major primary sources of PM2.5 collected around the Beijing region.14, 16,

212

19, 32

213

Si (Fig. 2a). Soil, construction, and urban fugitive dusts contained high abundance of Si (>

214

10%), coal burning was mid-Si-abundant (8.1%), and biomass burning, industrial emission,

215

and vehicle emission were low-Si-abundance sources (< 1%).

Elemental analysis demonstrated that all primary sources contained a certain content of

216

Notably, these sources showed different Si isotopic signatures (Fig. 2b). The δ30Si values

217

of soil, construction, and urban fugitive dusts were in the range of -1.0‰ to 0.5‰ (n = 64).

218

This range was narrower than the δ30Si range in soils at a global scale6 but close to the natural

219

Si isotopic signatures of soils and natural endogenous rocks and sands (-1.1‰ to 0.7‰).7 The

220

soil dusts collected at different depths and directions had an identical Si isotopic composition

221

(P > 0.05; Fig. S5). The δ30Si in particles from biomass burning was also in this range (-0.9‰

222

to 0.1‰, n = 10) due probably to the plant uptake of Si from soil.33, 34 However, the δ30Si in

223

particles from coal burning was remarkably negative (-1.2‰ to -3.4‰, n = 11) probably

224

resulting from the long-term evolution from the phytoliths.35 The δ30Si of industrial emission

225

was also negative (-0.9‰ to -1.8‰, n = 8). Lastly, the δ30Si of vehicle emission ranged from

226

0.8‰ to 1.2‰ (n = 3), which was consistent with that of petrol and diesel used in vehicles

227

(2.8‰ to 3.9‰, n = 3). All Si isotope data for the sources could be described by a

228

mass-dependent line (Fig. S6). The different Si isotopic signatures of primary sources of

229

PM2.5 met the prerequisite for using Si isotopic signature to trace the source of PM2.5.

230

Considering the different Si isotopic signatures of primary sources and limited potential

231

of Si isotope fractionation, the variations in Si isotopic composition of PM2.5 were able to 11



232

directly reflect the changes in primary sources. In both 2003 and 2013, the Si in PM2.5 in

233

spring/winter was isotopically lighter (all monthly mean δ30Si values < -1.0‰) than in

234

summer/autumn (Fig. 1e-f). This result clearly indicated that the contributions of

235

Si-isotopically light sources (i.e., coal burning and industrial emission with δ30Si < -1.0‰;

236

Fig. 2b) in spring/winter were higher than those in summer/autumn. Note that severe haze

237

weather in spring/winter was much more frequent than in summer/autumn (Fig. 1a) and that

238

the contribution of industrial emission should be relatively steady throughout the year.

239

Therefore, the aggravation of haze pollution in spring/winter should mainly result from coal

240

burning. This conclusion was evidenced by the fact that coal burning is a major manner of

241

heating in spring and winter around the Beijing region36 and was also consistent with the

242

result obtained in a recent report.37

243

3.3. Correlation of Si isotopic composition of PM2.5 with the pollution level

244

The correlation analysis of Si isotopic composition with the PM2.5 level also unraveled

245

the important sources of pollution. We investigated the correlation of both Si abundance in

246

PM2.5 and isotopic composition of PM2.5 with the PM2.5 concentration in 2013. From Fig. 3a,

247

no significant correlation of Si abundance in PM2.5 with the PM2.5 concentration could be

248

found. However, if dividing the Si abundance data into three groups in terms of PM2.5

249

pollution level (PM2.5 < 100, 100–200, and > 200 µg/m3; Fig. 3b), it was clear that heavy

250

haze weather (PM2.5 > 200 µg/m3) tended to have a low content of Si in PM2.5 (< 1.1%).

251

Regarding to Si isotopic composition (Fig. 3c), we found that the δ30Si value of PM2.5 was

252

strongly correlated with the PM2.5 concentration. The δ30Si value decreased linearly with the

253

PM2.5 concentration increasing (r = 0.66, P < 0.001). If the Si isotope data were also divided 12


Page 12 of 26

Page 13 of 26


254

into three groups in terms of PM2.5 pollution level (Fig. 3d), it could be seen that the δ30Si

255

value was significantly different at different pollution levels (P < 0.001), indicating that

256

different primary sources were responsible for different levels of pollution. All tested PM2.5

257

samples in heavy haze weather (> 200 µg/m3) yielded a δ30Si value of < -1.0‰; while with

258

the improvement of air quality (PM2.5 < 100 µg/m3) the Si was enriched in the heavier isotope

259

(30Si). This suggested that the Si-isotopically light sources, i.e., coal burning and industrial

260

emission, played a critical role in the heavy haze weather in the Beijing region.

261

In addition, we found that the δ30Si of PM2.5 appeared to be heavy isotope-deficit with

262

the increase of atmospheric concentration of SO2 and NOx (P < 0.001; Fig. S7), because

263

Si-isotopically light sources (i.e., coal burning and industrial emission) were usually

264

accompanied by the emission of high concentration of SO2 and NOx. We also found that the

265

δ30Si of PM2.5 did not show any dependence on the relative humidity (P = 0.487) but showed

266

a tendency to be enriched in PM2.5 at high temperature (Fig. S8), which well matched the

267

seasonal trend in δ30Si of PM2.5 (see Fig. S4).

268

3.4. Analysis of typical haze events

269

To further demonstrate the potential of Si isotopes in source tracing, we analyzed two

270

typical haze events in Beijing during 2013 (Fig. 4). In the haze event during Feb 23 to 25 (Fig.

271

4a), the burst of PM2.5 from 42.7 to 401.4 µg/m3 within 34 h was accompanied by only a

272

slight decrease in both abundance and isotopic composition of Si (P > 0.2; Fig. 4b-c),

273

suggesting that there was no significant change in the primary source input and thereby the

274

rapid growth of PM2.5 might mainly result from a secondary formation process in the

275

atmosphere.3, 4 13



276

In the second haze event during Mar 5 to 8 (Fig. 4d), two haze episodes were observed

277

with quite different variations in abundance and isotopic composition of Si. Similar to the

278

haze event described in Fig. 4a-c, the first haze episode (Mar 5-6) might also result from a

279

secondary formation process. The slightly positive shift in δ30Si during this episode (P < 0.1;

280

Fig. 4f) could be explained by the effect of low-Si-abundance and isotopically heavy sources

281

(e.g., vehicle emission; see Fig. 2). However, in the second haze episode (Mar 7), the PM2.5

282

steeply decreased to 127.6 µg/m3 due to a strong wind,38 and then rose again to 255.9 µg/m3

283

(Fig. 4d). The dramatic increase in abundance of Si and δ30Si (Fig. 4e-f) suggested that it was

284

mainly caused by high-Si-abundance and Si-isotopically heavy sources. According to Fig. 2,

285

these sources could be soil, construction, or urban fugitive dust. Thus, the second haze

286

episode should mainly result from a direct emission of PM2.5 from primary sources. These

287

results indicated that the Si isotopic signature not only is useful to analyze PM2.5 pollution

288

over a period of time, but also can be used to monitor individual haze events.

289

3.5. Environmental implications

290

Source tracing and apportionment of PM2.5 is critical for the development of pollution

291

control policies. Until now, the sources of PM2.5 in typical pollution regions are still poorly

292

understood due partly to the lack of proper tracers. This study demonstrates that the natural Si

293

isotopes provide a unique tool to trace the sources of PM2.5. Unlike other isotopes of major

294

elements in PM2.5, Si isotopic composition can directly reflect the information on the primary

295

sources of PM2.5 due to the chemical inertness of Si and different Si isotopic signatures of the

296

primary sources. Furthermore, from the analysis of typical haze events, Si isotopic

297

composition can provide extra information on the sources to the Si concentration and offers 14


Page 14 of 26

Page 15 of 26


298

evidence to distinguish the primarily emitted from the secondarily formed PM2.5, thus making

299

it a versatile tool for PM2.5 research.

300

Specifically, this study reveals that the Si-isotopically light sources were important

301

sources for Beijing’s severe haze pollution and demonstrates that coal burning was a major

302

cause for the aggregating haze weather in spring/winter in the studied years in Beijing.

303

Therefore, more stringent control policies on emission from coal burning should be effective

304

to mitigate the Beijing’s severe haze pollution in spring/winter. The methodology developed

305

herein may also be applicable to other developing countries that are suffering from air

306

pollution to help them develop effective pollution control strategies. Furthermore, this study

307

also suggests that stable isotope compositions may be better tracers in source apportionment

308

of PM2.5 than normally used concentrations of elements. Several measures may further

309

improve the reliability of the result, such as increasing the size and representativeness of

310

primary source samples. Considering the inter-regional transport of PM2.5, inclusion of

311

primary sources of adjacent cities/regions in the investigation may also help obtain more

312

accurate results, which will be considered in our future studies.

313

15



314

Acknowledgements

315

This work was financially supported by the Chinese Academy of Sciences (No.

316

XDB14010400, QYZDB-SSW-DQC018), the National Basic Research Program of China

317

(2015CB931903, 2015CB932003), and the National Natural Science Foundation of China

318

(No. 91543104, 21377141, 21422509). We thank Dr. Man Teng, Dr. Chenglong Zhang,

319

Pengfei Liu, and Chaoyang Xue for their help with collecting PM2.5 and vehicle emission

320

samples, and thank Prof. Yong Cai for helpful comments on the manuscript. We also thank

321

Qifeng Li for processing the GIS data.

322

Supporting information

323

Supporting experimental details, additional discussion, supporting tables, supporting

324

figures, and references for SI. The Supporting information is available free of charge on the

325

ACS Publications website.

326

Competing financial interests

327

The authors declare no competing financial interests.

328

16


Page 16 of 26

Page 17 of 26


329

References

330

1.

331

expectancy in the United States. N. Engl. J. Med. 2009, 360 (4), 376-386.

332

2.

333

Long-Range Transboundary Air Pollution. World Health Organization: 2006.

334

3.

335

nanoparticles in the atmosphere. Chem. Rev. 2012, 112 (3), 1957-2011.

336

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

337

Zeng, L.; Molina, M. J.; Zhang, R. Elucidating severe urban haze formation in China. P. Natl.

338

Acad. Sci. U.S.A 2014, 111 (49), 17373-17378.

339

5.

340

Environmental Isotope Geochemistry, Baskaran, M., Ed. Springer: Heidelberg, 2011; pp

341

87-104.

342

6.

343

2006, 88 (1-3), 252-256.

344

7.

345

silicon cycle. Nature 2005, 433 (7024), 399-402.

346

8.

347

Johnston, M. V. Silicon is a frequent component of atmospheric nanoparticles. Environ. Sci.

348

Technol. 2014, 48 (19), 11137-11145.

349

9.

350

Wang, Y. Formation of urban fine particulate matter. Chem. Rev. 2015, 115 (10), 3803-3855.

Pope, C. A., 3rd; Ezzati, M.; Dockery, D. W. Fine-particulate air pollution and life

The WHO Regional Office for Europe, Health Risks of Particulate Matter from

Zhang, R.; Khalizov, A.; Wang, L.; Hu, M.; Xu, W. Nucleation and growth of

Reynolds, B., Silicon Isotopes as Tracers of Terrestrial Processes. In Handbook of

Basile-Doelsch, I. Si stable isotopes in the Earth's surface: A review. J. Geochem. Explor.

Basile-Doelsch, I.; Meunier, J. D.; Parron, C. Another continental pool in the terrestrial

Bzdek, B. R.; Horan, A. J.; Pennington, M. R.; Janechek, N. J.; Baek, J.; Stanier, C. O.;

Zhang, R.; Wang, G.; Guo, S.; Zamora, M. L.; Ying, Q.; Lin, Y.; Wang, W.; Hu, M.;

17



351

10. Dawson, T. E.; Siegwolf, R. T. W., Using Stable Isotopes as Indicators, Tracers, and

352

Recorders of Ecological Change: Some Context and Background. In Terrestrial Ecology,

353

Elsevier: 2007; Vol. 1, pp 1-18.

354

11. Saltzman, E. S.; Savoie, D. L.; Zika, R. G.; Prospero, J. M. Methane sulfonic acid in the

355

marine atmosphere. J. Geophys. Res. Oceans 1983, 88 (C15), 10897-10902.

356

12. Savage, P. S.; Armytage, R. M. G.; Georg, R. B.; Halliday, A. N. High temperature

357

silicon isotope geochemistry. Lithos 2014, 190-191, 500-519.

358

13. van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.;

359

Villeneuve, P. J. Global estimates of ambient fine particulate matter concentrations from

360

satellite-based aerosol optical depth: development and application. Environ. Health Perspect.

361

2010, 118 (6), 847-855.

362

14. Huang, R. J.; Zhang, Y.; Bozzetti, C.; Ho, K. F.; Cao, J. J.; Han, Y.; Daellenbach, K. R.;

363

Slowik, J. G.; Platt, S. M.; Canonaco, F.; Zotter, P.; Wolf, R.; Pieber, S. M.; Bruns, E. A.;

364

Crippa, M.; Ciarelli, G.; Piazzalunga, A.; Schwikowski, M.; Abbaszade, G.; Schnelle-Kreis,

365

J.; Zimmermann, R.; An, Z.; Szidat, S.; Baltensperger, U.; El Haddad, I.; Prevot, A. S. High

366

secondary aerosol contribution to particulate pollution during haze events in China. Nature

367

2014, 514 (7521), 218-222.

368

15. Yang, J.; Fu, Q.; Guo, X.; Chu, B.; Yao, Y.; Teng, Y.; Wang, Y. Concentrations and

369

seasonal variation of ambient PM2.5 and associated metals at a typical residential area in

370

Beijing, China. Bull. Environ. Contaminat. Toxicol. 2015, 94 (2), 232-239.

371

16. Zhang, R.; Jing, J.; Tao, J.; Hsu, S.-C.; Wang, G.; Cao, J.; Lee, C. S. L.; Zhu, L.; Chen, Z.;

372

Zhao, Y. Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal 18


Page 18 of 26

Page 19 of 26


373

perspective. Atmos. Chem. Phys. 2013, 13 (14), 7053-7074.

374

17. Reff, A.; Bhave, P. V.; Simon, H.; Pace, T. G.; Pouliot, G. A.; Mobley, J. D.; Houyoux, M.

375

Emissions inventory of PM2.5 trace elements across the United States. Environ. Sci. Technol.

376

2009, 43 (15), 5790-5796.

377

18. Song, Y.; Tang, X.; Xie, S.; Zhang, Y.; Wei, Y.; Zhang, M.; Zeng, L.; Lu, S. Source

378

apportionment of PM2.5 in Beijing in 2004. J. Hazard. Mater. 2007, 146 (1), 124-130.

379

19. He, K.; Yang, F.; Ma, Y.; Zhang, Q.; Yao, X.; Chan, C. K.; Cadle, S.; Chan, T.; Mulawa, P.

380

The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 2001, 35 (29), 4959-4970.

381

20. Ge, S.; Xu, X.; Chow, J. C.; Watson, J.; Sheng, Q.; Liu, W.; Bai, Z.; Zhu, T.; Zhang, J.

382

Emissions of air pollutants from household stoves: honeycomb coal versus coal cake. Environ.

383

Sci. Technol. 2004, 38 (17), 4612-4618.

384

21. Li, H.; Zhang, J.; Zhao, Y.; Wu, C.-Y.; Zheng, C. Wettability of fly ashes from four

385

coal-fired power plants in China. Ind. Eng. Chem. Res. 2011, 50 (13), 7763-7771.

386

22. Das, O.; Wang, Y.; Hsieh, Y.-P. Chemical and carbon isotopic characteristics of ash and

387

smoke derived from burning of C3 and C4 grasses. Org. Geochem. 2010, 41 (3), 263-269.

388

23. Zhao, P.; Feng, Y.; Zhu, T.; Wu, J. Characterizations of resuspended dust in six cities of

389

North China. Atmos. Environ. 2006, 40 (30), 5807-5814.

390

24. Chow, J. C.; Watson, J. G.; Ashbaugh, L. L.; Magliano, K. L. Similarities and differences

391

in PM10 chemical source profiles for geological dust from the San Joaquin Valley, California.

392

Atmos. Environ. 2003, 37 (9), 1317-1340.

393

25. Fraser, M. P.; Cass, G. R.; Simoneit, B. R. Gas-phase and particle-phase organic

394

compounds emitted from motor vehicle traffic in a Los Angeles roadway tunnel. Environ. Sci. 19



Page 20 of 26

395

Technol. 1998, 32 (14), 2051-2060.

396

26. Hu, J.; Zheng, M.; Liu, W.; Li, C.; Nie, Z.; Liu, G.; Xiao, K.; Dong, S. Occupational

397

exposure

398

polychlorinated biphenyls, and polychlorinated naphthalenes in workplaces of secondary

399

nonferrous metallurgical facilities in China. Environ. Sci. Technol. 2013, 47 (14), 7773-7779.

400

27. Georg, R. B.; Reynolds, B. C.; Frank, M.; Halliday, A. N. New sample preparation

401

techniques for the determination of Si isotopic compositions using MC-ICPMS. Chem. Geol.

402

2006, 235 (1-2), 95-104.

403

28. Zambardi, T.; Poitrasson, F. Precise determination of silicon isotopes in silicate rock

404

reference materials by MC-ICP-MS. Geostand. Geoanal. Res. 2011, 35 (1), 89-99.

405

29. Lu, D.; Liu, Q.; Zhang, T.; Cai, Y.; Yin, Y.; Jiang, G. Stable silver isotope fractionation in

406

the natural transformation process of silver nanoparticles. Nat. Nanotechnol. 2016, 11,

407

682-686.

408

30. Song, H.; Zhang, K.; Piao, S.; Wan, S. Spatial and temporal variations of spring dust

409

emissions in northern China over the last 30 years. Atmos. Environ. 2016, 126, 117-127.

410

31. Liu, L.; Lyu, Y.; Xu, W.; Shi, P., Blown sand disasters in China. In Natural Disasters in

411

China, Springer: 2016; pp 239-271.

412

32. Song, Y.; Xie, S.; Zhang, Y.; Zeng, L.; Salmon, L. G.; Zheng, M. Source apportionment

413

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

414

and UNMIX. Sci. Total. Environ. 2006, 372 (1), 278-286.

415

33. Ding, T. P.; Tian, S. H.; Sun, L.; Wu, L. H.; Zhou, J. X.; Chen, Z. Y. Silicon isotope

416

fractionation between rice plants and nutrient solution and its significance to the study of the

to

polychlorinated

dibenzo-p-dioxins

and

20


dibenzofurans,

dioxin-like

Page 21 of 26


417

silicon cycle. Geochim. Cosmochim. Acta 2008, 72 (23), 5600-5615.

418

34. Ding, T. P.; Ma, G. R.; Shui, M. X.; Wan, D. F.; Li, R. H. Silicon isotope study on rice

419

plants from the Zhejiang province, China. Chem. Geol. 2005, 218 (1–2), 41-50.

420

35. Teichmüller, M.; Teichmüller, R., The geological basis of coal formation. In Stach's

421

Textbook of Coal Petrology, Stach, E., Ed. Gebruder Borntraeger: Stuttgart, Germany, 1982;

422

pp 5-86.

423

36. Liu, P.; Zhang, C.; Mu, Y.; Liu, C.; Xue, C.; Ye, C.; Liu, J.; Zhang, Y.; Zhang, H. The

424

possible contribution of the periodic emissions from farmers' activities in the North China

425

Plain to atmospheric water-soluble ions in Beijing. Atmos. Chem. Phys. 2016, 16,

426

10097-10109.

427

37. Liu, J.; Mauzerall, D. L.; Chen, Q.; Zhang, Q.; Song, Y.; Peng, W.; Klimont, Z.; Qiu, X.;

428

Zhang, S.; Hu, M.; Lin, W.; Smith, K. R.; Zhu, T. Air pollutant emissions from Chinese

429

households: A major and underappreciated ambient pollution source. P. Natl. Acad. Sci. U.S.A

430

2016, 113 (28), 7756-7761.

431

38. Gao, M.; Carmichael, G.; Wang, Y.; Saide, P.; Yu, M.; Xin, J.; Liu, Z.; Wang, Z.

432

Modeling study of the 2010 regional haze event in the North China Plain. Atmos. Chem. Phys.

433

2016, 16 (3), 1673-1691.

434

21



435

Figures

436

437

Fig. 1. Abundance and stable isotopic composition of Si in PM2.5. a, Daily mean PM2.5

438

concentration in 2013. The error bars represent standard deviations (1SD) from 24

439

measurements in a day (n = 24). b, Monthly mean total Si in PM2.5 in 2003 and 2013. The

440

error bars represent 1SD from samples in a month (n = 2–9). c, Si abundance in PM2.5 at

441

different days in 2013. The error bars represent standard deviations (1SD) from 24

442

measurements in a day (n = 24). d, Three-isotope plot showing δ29Si versus δ30Si for all PM2.5

443

samples collected in 2003 and 2013. The black line represents the mass-dependent

444

fractionation line. The error bars represent 1SD from three parallel measurements (n = 3). e,f,

445

ranges of δ30Si value of PM2.5 in different months of 2003 (e) and 2013 (f). In e and f, the

446

horizontal lines in the bars represent the mean values for each month.

22


Page 22 of 26

Page 23 of 26


447 448

Fig. 2. Si abundances (a) and stable isotopic signatures (b) of primary sources of PM2.5.

449

a, The error bars represent 1SD of measurements from the group. The mean abundances of Si

450

in the sources are labeled in the parentheses. b, Si isotopic signatures of primary sources of

451

PM2.5 collected around the Beijing region.

23



452 453

Fig. 3. Correlation of Si in PM2.5 with the PM2.5 pollution level in 2013. a, Plot of Si

454

abundance in PM2.5 versus PM2.5 concentration. The error bars represent 1SD from 24

455

measurements in a day (n = 24). b, Grouping of Si abundance in PM2.5 according to PM2.5

456

concentration. *P = 0.522; **P < 0.05. c, Plot of δ30Si versus PM2.5 concentration. The error

457

bars on x-axis represent 1SD from the PM2.5 measurements in a day (n = 24), and those on

458

y-axis represent 1SD from three parallel measurements of Si isotope ratios (n = 3). d,

459

Grouping of δ30Si values according to PM2.5 concentration. *P < 0.001; **P < 0.001.

460

24


Page 24 of 26

Page 25 of 26


461 462

Fig. 4. Analysis of typical haze events by using Si isotopic signatures. a,d, Monitoring of

463

PM2.5 concentration in two haze events during Feb 23, 2013 to Feb 25, 2013 (a) and Mar 5,

464

2013 to Mar 8, 2013 (d). b,c, Daily mean abundance of Si in PM2.5 (b) and δ30Si values of

465

PM2.5 (c) in the first haze event (a). The yellow and orange bars in b and c correspond to the

466

episodes marked in the same colors in a. *P = 0.553, and **P = 0.203. The error bars

467

represent 1SD from the PM2.5 measurements (b) or three parallel measurements of Si isotope

468

ratios (c). e,f, Daily mean abundance of Si in PM2.5 (e) and δ30Si values of PM2.5 (f) in the

469

second haze event (d). The yellow, orange and red bars in e and f correspond to the episodes

470

marked in the same colors in d. In e, *P = 0.666, **P < 0.01. In f, *P = 0.093, **P < 10-4.

471

The meanings of error bars are the same as in b and c.

472 25



473

For TOC only

474

26


Page 26 of 26

Natural Silicon Isotopic Signatures Reveal the Sources of Airborne

Recommend Documents