Using Î´13C of levoglucosan as a chemical clock - Environmental

Subscriber access provided by UNIV OF DURHAM

Environmental Processes 13

Using # C of levoglucosan as a chemical clock Iulia Gensch, Xue Fang Sang-Arlt, Werner Laumer, Chuen Y. Chan, Guenter Engling, Jochen Rudolph, and Astrid Kiendler-Scharr Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b03054 • Publication Date (Web): 31 Aug 2018 Downloaded from http://pubs.acs.org on September 1, 2018

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 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 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.

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 25

Environmental Science & Technology

Using δ13C of levoglucosan as a chemical clock

1 2 3

I. Gensch1,*, X. F. Sang-Arlt1,**, W. Laumer1, C. Y. Chan2,

4

G. Engling3,*** , J. Rudolph1,4 and A. Kiendler-Scharr1

5

1

IEK-8: Troposphere, Forschungszentrum Jülich, Jülich, 52428 Germany

6

2

Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710043, China

7

3

Department of Biomedical Engineering and Environmental Sciences, National Tsing

8

Hua University, Hsinchu, 30013 Taiwan

9

4

10

Chemistry Department, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3

Canada

11 12 13

Key words: compound specific isotope ratio analysis, biomass burning, levoglucosan,

14

molecular tracer, chemical aging

15 16 17 18 19 20 21 22 23 24 25 *

Correspondence to I. Gensch, [email protected] now at Untersuchungsinstitut Heppeler GmbH, Lörrach, 79539 Germany *** now at California Air Resources Board, El Monte, CA, 91731 USA **

-1-

ACS Paragon Plus Environment


26 27 28

Abstract

29

Compound specific carbon isotopic measurements (δ13C) of levoglucosan were carried

30

out for ambient aerosol sampled during an intensive biomass burning period at different

31

sites in Guangdong province, China. The δ13C of ambient levoglucosan was found to be

32

noticeably heavier than the average δ13C of levoglucosan found in source C3-plant-

33

combustion samples. To estimate the photochemical age of sampled ambient

34

levoglucosan, back trajectory analyses were done. The origin and pathways of the probed

35

air masses were determined, using the Lagrangian-particle-dispersion-model FLEXPART

36

and ECMWF meteorological data. On the other hand, the isotopic hydrocarbon clock

37

concept was applied to relate the changes in the field-measured stable carbon isotopic

38

composition to the extent of chemical processing during transport. Comparison of the

39

photochemical age derived using these two independent approaches shows on average

40

good agreement, despite a substantial scatter of the individual data pairs. These analyses

41

demonstrate that the degree of oxidative aging of particulate levoglucosan can be

42

quantified by combining laboratory KIE studies, observed δ13C at the source and in the

43

field, as well as back trajectory analyses. In this study, the chemical loss of levoglucosan

44

was found to exceed 50% in one-fifth of the analyzed samples. Consequently, the use of

45

levoglucosan as a stable molecular tracer may underestimate the contribution of biomass

46

burning to air pollution.

47 48

1. Introduction

-2-


Page 2 of 25

Page 3 of 25


49

Biomass burning (BB) emissions have an adverse effect on the air quality in many urban

50

environments1-3, particularly in developing and newly industrialized countries4-7. Rural

51

communities are affected by air pollution from indoor combustion of wood and crop

52

residues for cooking or heating, as well as from open burning associated with agricultural

53

practices, i.e. forest and cropland clearing. Assessments of the source strength of

54

anthropogenic activities and their impact on air quality often rely on emission studies,

55

where process specific emission factors are determined at the source. Receptor-oriented

56

models, such as those using chemical mass balance (CMB) techniques, are widely

57

employed to estimate major source contributions to e.g. the observed particulate matter.

58

This relies on a comparison of the chemical composition at emission with that at the

59

monitoring site8-10. Particularly the use of non-reactive atmospheric chemical tracers,

60

unique for a selected source, allows accurate apportionment, given that applied emission

61

profiles are correct. Yet for species reactive in the atmosphere, measured ambient

62

concentrations cannot be directly linked to the source strength.

63

For BB emissions, levoglucosan (1,6-anhydro--D-glucopyranose) has long been

64

considered as an inert and source specific tracer11, 12 since it is formed by the thermal

65

breakdown of cellulose and hemicellulose alone. This is the reason why it was typically

66

used in molecular marker based CMB calculations (CMB-MM), to quantify BB primary

67

emission sources of the observed aerosol13-18. The advantage of such studies is that the

68

calculations do not require absolute emission factors, which are often associated with

69

high uncertainties. The ambient concentration measurements of the pollutants are

70

combined in CMB-MM models with emission profiles (i.e. relative contributions of the

71

most abundant species to the total emission), also known as ´fingerprints´. Based on the

-3-



72

observed profile at the sampling site, the model attributes the relative input of the sources

73

by balancing their fingerprints. The accuracy of source apportionment is largely

74

determined by the uncertainties in the selected source profiles19. Besides, none of CMB-

75

MM studies hitherto accounted for the atmospheric chemical degradation of levoglucosan.

76

Based on observations of long-range-transported ambient aerosol20 and laboratory

77

studies21, 22, it was recently shown that levoglucosan reacts with the OH radical, with an

78

atmospheric half-life of few days. Consequently, the ratio of levoglucosan to organic

79

aerosol (LG/OA, as used in CMB-MM) at the emission is most likely not representative

80

for the aged wood smoke at the sampling site18, 23. Based on the observed levoglucosan

81

reactivity, Hennigan et al. already pointed out the need of introducing other tracers as

82

additional constrains in the CMB-MM equation systems to improve biomass burning

83

source apportionment21.

84

In recent years, the use of compound specific isotopic measurements emerged as valuable

85

information to better apportion sources of atmospheric organic trace compounds and to

86

describe their chemical processing during atmospheric transport24-27. Sources have their

87

isotopic signature and chemical decay results in isotope fractionation specific for any

88

given reaction. In other words, as dilution and mixing processes in the atmosphere do not

89

fractionate isotopes, isotopic information can be used in theory to differentiate between

90

different types of sources and gain insight into chemical processing. According to the so

91

called ‘isotopic hydrocarbon clock’ equation (see Equation 3 below)28, the isotope

92

signature of a chemical species at a specific point in time and space can be used to trace

93

sources or calculate the extent of its chemical degradation, when both source specific

94

isotopic composition and the kinetic isotope effect for the relevant chemical reactions are

-4-


Page 4 of 25

Page 5 of 25


95

known. Based on this concept, the global isotope distribution of the long-lived

96

hydrocarbon ethane was studied using chemical transport modelling29, 30. Thus, source

97

apportionment of ambient aerosol using reactive tracers could be improved by combining

98

compound specific isotopic analyses with trajectory and wind-based models. Recently

99

Sang et al. suggested the use of levoglucosan stable carbon isotope ratios, δ13C, as tracers

100

which fulfill the general requirements for two co-emitted tracers from the source of

101

interest22.

102

The benefits of linking laboratory results with atmospheric ambient processes by using a

103

chemical time dimension are obvious. The main goal of this study is to estimate the

104

extent of the aerosol atmospheric processing, using isotopic ratios in their role as ‘internal

105

chemical clock’. To this end, compound specific isotopic measurements of levoglucosan

106

were carried out for ambient aerosol sampled during biomass burning episodes at three

107

different sites in Guangdong province, China. Further, the origin and transport pathways

108

of the probed air masses were determined, based on back trajectories calculated with the

109

Lagrangian particle dispersion model FLEXPART31, 32 using meteorological data from

110

the European Centre for Medium-Range Weather Forecasts (ECMWF). The gained

111

information was finally combined with source specific levoglucosan δ13C in BB aerosol33

112

and the KIE of levoglucosan chemical degradation by OH radicals22 to derive chemical

113

age information on the sampled aerosol.

114 115

2. Stable carbon isotope ratios in atmospheric studies

116

The stable carbon isotope ratio is usually reported as the difference between the ratio of

117

13

C and 12C atoms (13C/12C) in a sample and a reference standard relative to the standard

-5-



Page 6 of 25

13

C/12C = 0.01118(28)34, 35). Due to the very

118

(Vienna Peedee Belemnite, VPDB, with

119

small differences in natural stable carbon isotope ratios, these are expressed using the

120

delta notation (δ13C) in parts per thousand (‰):

121

=

− 1 ∙ 1000 ‰

13

(1)

122

Usually, molecules containing

123

only 12C. Given that the rate constants of these reactions are 13k and 12k, respectively, the

124

kinetic isotope effect (KIE) is defined as

125

given as relative difference in the form of the epsilon notation (ε) in parts per thousand

126

(‰):

127

# = $ − 1% ∙ 1000 ‰ "

C react slightly slower than their isotopologues having

" "

. Similarly to isotope ratios, KIE is typically

"

(2)

128

A ‘normal’ KIE ( >1, #>0) results in the continuing enrichment of 13C in the precursor "

129

during a chemical reaction. In other words, the progress of a reaction can be measured by

130

an increase of the δ13C in the reactant. The changes in the isotopic composition of a

131

chemical species from the emission to the sampling location can be linked to its

132

atmospheric photochemical processing by the isotopic hydrocarbon clock equation28.

133 134 135

"

C' = C( + *+, -./0+, 123 23 #

(3)

where C( and C' represent the isotopic composition at the source and observation

site, respectively, *+, -./0+, is the average photochemical age,-./0+, is the average OH

136

concentration during the photochemical processing, 123 is the rate coefficient of the

137

species of interest with OH, and

23 #

is the KIE of the latter oxidation reaction.

-6-


Page 7 of 25


138 139

3. Materials and experimental methods

140

Ambient aerosol was collected at three different sites (rural, N 24.90º, E 114.16º,

141

suburban, N 24.78º, E 113.27º and urban, N 23.06º, E 113.39º) located in Guangdong

142

province, China. Samples were taken daily over ~24 h time periods (from 8 AM to 8 AM

143

next day), from November 4th to December 22nd 2009. Details on the sampling locations

144

are given in the Supporting Information (Section S1).

145

The aerosol particles were collected by Mini-volume samplers (5±0.5 L min-1; Airmetrics,

146

Eugene, OR, USA) on quartz microfiber filters (47 mm diameter, Whatman, Piscataway,

147

NJ, USA). These were baked at 500 ℃ for 8 h before each sampling. All filters were

148

weighted before and after sampling on an electronic balance after conditioning them at

149

constant temperature (23±1 ℃) and humidity (40±1 %) for 24 h. Field blanks, obtained

150

by placing clean filters in the sampler for 5 - 10 min without any air flow, were collected

151

every week. After sampling, the filters were stored in a freezer at -20 °C. In total, 76

152

samples were analyzed for levoglucosan concentrations and carbon isotope ratios,

153

although δ13C could not be determined in 13 pieces (ca. 20% of total) due to the very

154

small collected amounts of anhydrosugars.

155

Since the mass loadings of the probed levoglucosan varied over two orders of magnitude

156

(from ~200 ng to 20000 ng), the method introduced by Sang et al.33 to determine δ13C in

157

source aerosol, using direct thermal desorption from the filter material had to be adapted,

158

to substantially lower the limit of detection for the compound specific isotopic

159

measurements. In this regard, the aerosol was gained from the filter material by liquid

160

extraction and the prepared extracts were directly injected in the cooled injection system

-7-



161

(CIS at -30 °C) of a gas chromatography-isotope ratio mass spectrometry system (GC-

162

IRMS) prior to the chromatographic separation. A significant improvement in the

163

separation of the complex organic matrices was reached by increasing the polarity of the

164

used GC columns, as well as by refining their dimensions. Details are presented in the

165

Supporting Information, Section S2. Briefly, the extracted organic mixtures were thermo-

166

desorbed in the CIS at 220 °C and transferred to be separated by ‘heart-cut’ two-

167

dimensional-gas-chromatography. Thereto, an Agilent 6890 was equiped with two GC

168

columns with different selectivity, a low-polar (Rtx 1301, 30 m length, 0.25 mm ID, 0.5

169

µm film thickness) and a mid-polar one (FS-OV 225, 30 m length, 0.25 mm ID, 0.2 µm

170

film thickness). The fractions of the first-column elute, which contain all compounds of

171

interest, i.e. levoglucosan, mannosan, galactosan and the internal standard (Methyl 3,6-

172

anhydro-α-d- galactopyranoside, employed to survey the reproducibility of the

173

measurements) were once more cryogenically trapped, prior to injection into the second

174

column. The base-line-separated individual compounds were sent to the combustion

175

interface, where they were quantitatively oxidized to CO2 and H2O. After removing the

176

water, CO2 was transferred to the IRMS, where m/z 44, 45 and 46 were detected.

177

Performance aspects of the method are given in the Supporting Information, Section S2

178

and Figures S1 and S2.

179

containing more than 100 ng levoglucosan. Considering the sampling conditions and the

180

liquid extraction recovery, this mass corresponds to 50 ng m-3, which is at the lower limit

181

of the levoglucosan atmospheric concentration during biomass burning events.

182

Additionally, δ13C of the total carbon (TC) on the filters was measured by Elemental

183

Analysis - Isotope Ratio Mass Spectrometry (EA-IRMS).

The δ13C measurement accuracy was 0.5 ‰ for samples

-8-


Page 8 of 25

Page 9 of 25


184 185

4. Application of back trajectory analyses in the evaluation of the isotopic measurements

186

of levoglucosan in ambient BB aerosol

187

The application of isotope ratios to atmospheric research makes use of their quality to

188

fingerprint sources and chemical degradation of the species of interest. In this study,

189

information on origin and pathways of the investigated levoglucosan from the fire to the

190

sampling sites was gained by tracing backwards the probed air parcels. The Lagrangian

191

Particle Dispersion Model (LPDM) FLEXPART was employed31,

192

classical trajectory model, a LPDM stochastically accounts for turbulence and convection.

193

For this purpose, statistically simulated turbulent fluctuations are superimposed to the

194

mean winds for a large number of particles (the retroplumes) transported along different

195

trajectories. Here, sufficiently large chosen particle ensembles ensure correct

196

representations of the finite sampled volume of air. For this study, the retroplume

197

centroids (RPC) of 200000 particles were calculated for each run. These were released

198

hourly for the whole measuring period at each sampling site and followed 3 days

199

backward in time. Here the total of 3 days was chosen as it represents an average value

200

for the levoglucosan half-life time21, 22, considering a typical [OH]av of 1 to 3·106 molec

201

cm-3 for low and mid-latitudes in fall36-38. The simulations were initialized with the three-

202

hourly meteorology from the European Centre for Medium-Range Weather Forecasts

203

(ECMWF), having a horizontal resolution of 1° × 1° at all the 91 vertical model levels39.

204

In order to allow for comparison between the results of FLEXPART simulations and the

205

extent of the chemical processing derived from the measured levoglucosan carbon isotope

206

ratios, the following approach was conceived. The FLEXPART simulations provided

-9-


32

. Compared to a


207

sequences of hourly RPC positions, considered to be best related to conventional

208

trajectories, together with the corresponding mixed layer (atmospheric boundary layer -

209

ABL40) heights. From here on, the RPC position sequences will be named ‘trajectories’ in

210

the present manuscript. The likely duration for the chemical degradation of levoglucosan

211

along each trajectory relevant for the measurements (in total 24 for each filter sample)

212

was determined, by adding up all hours during day-light. A differentiation was made

213

between episodes above and within the ABL. For the time in which the air parcel was

214

within the ABL, the possibility of adding fresh emission was accounted for. Details on

215

the weighing of this contribution as well as the implementation of the isotopic

216

hydrocarbon clock equation are given in the Supporting Information, section S4. The

217

resulting average time corresponds to *+, in Eq. 3. Considering the duration of the filter

218

sampling, all values were averaged for the 24 trajectories calculated each day. Finally, the

219

derived times of levoglucosan exposure to OH radicals determined form the FLEXPART

220

model were compared with the observed isotopic ratios in ambient aerosol based on the

221

isotopic hydrocarbon clock equation (Eq. 3).

222 223

5. Results and discussion

224

Ambient carbon isotope ratios

225

The isotopic composition of levoglucosan and TC in ambient aerosol from urban,

226

suburban, and rural sites containing more than 100 ng levoglucosan and 10 µg TC are

227

presented in Figure 1. Overall, the data show that there is no systematic dependence of

228

the carbon isotope ratios on the environment type where the particles were collected.

- 10 -


Page 10 of 25

Page 11 of 25


229 230 231 232 233 234 235 236 237 238 239

Figure 1: Compound specific and total carbon isotopic ratios of levoglucosan in source and ambient aerosol. The shaded areas show the range of TC (lower panel) and levoglucosan (upper panel) δ13C measured in source aerosol originating from the combustion of selected plants. The latter includes two ranges based on the levoglucosan carbon isotope ratios reported by Sang et al.33 for different types of burned biomass: dark green for C3 hard woods and crop residues and light green for C3 soft woods. The source sample depicted by orange was taken during a fire that burned through Texas switchgrass (Panicum virgatum), a C4 grass, in February 2012 in Texas, USA. The full symbols show the TC (black) and levoglucosan (green) δ13C in ambient aerosol sampled at the three sites (rural circles, suburban triangles, urban squares) during November-December 2009 (Supporting Information, Section S1).

240

Additionally, source specific δ13C ranges are included in the plot, based on source

241

isotopic studies of levoglucosan and TC in aerosol formed during the combustion of

242

selected C3 plants, relevant for East Asia33. For C4 plant combustion, only one

243

measurement was available, for which the aerosol used to determine the isotope ratio of

244

levoglucosan was collected during a fire that burned through Texas switchgrass (Panicum

245

virgatum). This measurement is depicted here to show the significant

246

levoglucosan formed during the C4 compared to C3 plant combustion, in agreement with

- 11 -


13

C enrichment in


247

the δ13C differences found in the corresponding parent material. *

248

As can be seen, δ13C shows a lower variation for TC than for levoglucosan (standard

249

deviation of 0.67 ‰ vs. 1.02 ‰, with an F-test of 0.0024). Compared to the TC ambient

250

data, levoglucosan is enriched in 13C by 3.6 ± 0.2 ‰, on average.

251

These observations may be caused by differences in the source specific δ13C, or aerosol

252

aging leading to a δ13C increase due to the chemical degradation of levoglucosan. While

253

levoglucosan is most likely associated with BB emissions, it can also originate from the

254

combustion of low quality coals (e.g. lignite and brown coal containing small amounts of

255

partially decayed plant material) during the heating season41, 42. The emission factors in

256

the latter case are at least one order of magnitude lower than for BB. Other than that, the

257

sources of TC are more various, including various types of fossil fuel combustion.

258

However, for this study there is a high degree of linear correlation for all three sampling

259

locations between levoglucosan and TC concentrations, showing Pearson coefficients

260

higher than 0.92 (for details see Supporting Information, section S6, Table S1). This

261

together with the measured LG/OA ratios showing a mean of 0.031 ± 0.014 demonstrate

262

that BB is the dominant source for the measured TC42.

263

Furthermore, a potentially large variability in the carbon isotope ratio of BB emissions

264

can result from contributions from BB of C4 plants, which are substantially enriched in

265

13

266

average of -27.0 ‰ rules out a substantial impact of C4 plant BB. However, Sang et al.33

267

reported sizeable differences in the range of δ13C values for BB of different types of C3-

268

plants (Figure 1), showing also that levoglucosan is consistently enriched in 13C relatively

269

to the parent material.

C compared to C3 plants. The narrow range of observed TC δ13C values with an

- 12 -


Page 12 of 25

Page 13 of 25


270 271

Three-day back trajectories of the sampled aerosol

272

To identify the main source regions as well as the transport pathways of the collected

273

aerosol, the FLEXPART model was used (described in Section 4). The three-day-back-

274

trajectory analyses allowed a qualitative identification of the typical vegetation in the

275

areas of interest (i.e. mixed forests, steppes and crops areas43, see Supporting Information,

276

Section5). Neither tropical grass steppes, nor pure C4 plant croplands are characteristic

277

for the regions northerly (the prevalent wind direction) of the sampling sites44. Therefore,

278

the existence of a homogeneous source, predominantly consisting of C3 plant burning is

279

very likely for this study, being consistent with the measured TC δ13C (see above).

280

More quantitative knowledge for potential atmospheric reactions and processes was

281

gained from the RPC and ABL height data. Figure 2 visualizes two exemplarily selected

282

back trajectory analyses. These were carried out for the aerosol particles collected from

283

November 22nd, 8 AM to November 23rd, 8 AM and from November 12th, 8 AM to

284

November 13th, 8 AM, respectively, at the suburban site south of the city of Shaoguan,

285

China. As can be seen, both source regions are situated northerly from the sampling

286

locations, where the typical vegetation is characterized by steppes43. For the sample

287

collected on November 23rd, there are almost no periods with trajectories passing through

288

the ABL. Addition of fresh BB emissions only occurs for a short period of time, mostly

289

just before sampling. Thus, it seems likely that the δ13C of levoglucosan is strongly

290

affected by chemical degradation. Indeed, a relatively high δ13C value, -22.54 ‰, was

291

observed in this sample. Corresponding to the described processing along the back

292

trajectory, *+, was determined to be 22.26 daylight hours (for details, see Supporting

- 13 -



293

Information, section S4). In contrast, for the sample taken on Nov 13th, all 24 trajectories

294

pass many times through the ABL allowing a strong contribution of fresh BB emissions.

295

Here a small *+, of 2.78 h is derived from the simulations. The corresponding observed

296

δ13C is -24,11 ‰, which is consistent with the isotopic ratios for levoglucosan emissions

297

from burning of hardwoods and crop residues (e.g. peanut stalks, rice straw33).

Figure 2: Selected three-days back trajectories of the aerosol sampled on November 23th (upper panel, measured δ13C value -22.54 ‰) and 13th (lower panel, measured δ13C value -24.11 ‰) at the suburban site south of the city Shaoguan, China. On the left side, 24 hourly RPC positions and the corresponding heights in the given color code are shown for the last three days before sampling. On the right side, 24 RPC (full lines) and ABL (dashed lines) heights, each are depicted for the last three days before sampling.

298

Aerosol chemical aging and isotope ratios

299

Similarly, FLEXPART back trajectory analyses were carried out for each isotopically

300

analyzed aerosol sample. The resulting *+, value, depending on both chemical

301

degradation and addition of fresh emissions within the ABL, was assigned to each

- 14 -


Page 14 of 25

Page 15 of 25


Figure 3: Scatter plot of the levoglucosan measured isotopic ratios in ambient aerosol and the corresponding tav (for details, see text). The latter is related to the levoglucosan photo-oxidation extent in the free troposphere. The full symbols represent the data for three different measurement sites (circles for the rural, triangles for the suburban and squares for the urban location). The lines show fits to the processed data. The colors are attributed to two major data features (light green for CAT1 and dark green for CAT2. The separation into two categories was done based on the main vegetation types at the origin of the investigated trajectories, for details see text).

302

measured levoglucosan isotope ratio. Scatter plots of δ13C vs. *+, were created, by

303

additionally splitting the data in two categories, based on the typical vegetation prevailing

304

at the trajectory origin: (CAT1) mixed forest regions (soft- and hardwoods), and (CAT2)

305

steppes, crop and grass lands. For twelve samples the air masses came from over the

306

ocean. The corresponding data were apportioned into the two sets described above by

307

making a simplifying assumption. The vegetation type at the water nearest land was

308

assigned to each of these samples. Finally, linear regression analyses were separately

309

done for the two data sets.

- 15 -



Page 16 of 25

310

The overall results are depicted in Figure 3, combining the measured isotopic ratios of

311

levoglucosan in ambient aerosol with its estimated time of reaction derived from

312

FLEXPART simulations. According to the Equation 3, a straight line can physically

313

describe the trend of the data points. Pearson product moment correlation coefficients

314

were calculated, resulting in 0.518 for the mixed forest and 0.522 for the steppes, crop

315

and grass land data sets (for details see Supporting Information, Table S2). Both values

316

indicate a moderate correlation for the linear dependence of the independently obtained

317

variables.

318

Using Eq. 3 as regression equation, the calculated slope and y-intercept of the two lines

319 320 321 322

fitted to the data can be interpreted as the product 3600 ∙ -./0+, 123 23 # and the source specific C( , respectively.

Table 1: Overall results for the regression analysis of the scatter plot δ13C vs. 789 presented in Figure 3. -:;089 and :; < are derived from the slopes using data from literature.

Slope

23 b #

δ13 C0

3600 ∙ -./0+, 123 23 #

-./0+, a

‰

‰ h-1

molec cm-3

‰

3.3· 106

2.53

Intercept Data category

-23.1± CAT1

0.072±0.024 0.2

-24.9± CAT2

0.076±0.021 0.2 a b

323

given 23 # = 2.29‰ and 123 = 2.67 ∙ 10AB cm3 molec-1 s-1(22) given -./0+, = 3 ∙ 10C molec cm-3 (e.g.38)

- 16 -


Page 17 of 25


324

The outcome of the regression analysis given in Table 1 shows that the C( values, -

325

23.1 and -24.9 ‰, calculated for CAT1 and CAT2, respectively, fall well into the ranges

326

of levoglucosan source specific isotopic ratios observed in aerosol originating from the

327

combustion of softwoods along with crop residues and hardwoods, as reported by Sang et

328

al.33, respectively. It should be noted that this does not necessarily mean that the CAT1

329

samples are solely impacted by levoglucosan emissions with -23.7‰ to -21.9‰, or in

330

case of the CAT2 samples, by emissions with -26.5‰ to -23.6‰ (see below). The slopes

331

from the linear least square fit are very similar to each other (0.072±0.024 ‰ h-1 and

332

0.076±0.021 ‰ h-1), indicating similar oxidation conditions in the investigated regions.

333

Given the kinetic information determined in laboratory studies on the oxidation of

334

levoglucosan aerosol particles by OH (i.e.

23 #

= 2.29 ‰ and 123 = 2.67 ∙ 10AB cm3

335

molec-1 s-1, from22), daytime -./0+, values of (3.3±1.1)·106 molec cm-3 and (3.4±1.0)·106

336

molec cm-3 during the atmospheric transport are calculated for the two different source

337

types, respectively. This is consistent with the climatological average of daytime OH-

338

radical concentrations for the studied latitude range and season of approximately 3·106

339

molec cm-3 reported by Spivakovsky et al.38. The positive slope values show that the

340

‘older’ the sampled air masses, the higher are the delta values, confirming the normal

341

KIE of the levoglucosan atmospheric degradation. The statistical significance of the

342

observation-modelling comparison was verified by a bootstrapping analysis. The variance

343

of the slopes (0.072±0.023 and 0.076±0.022, respectively) resulting from 100000

344

independent resamplings is 0.00053 and 0.00048 for the two data sets, respectively.

345

In theory, Equation 3 allows for deriving the average age of levoglucosan from measured

346

carbon isotope ratios, hereafter referred to as *+,,E . However, the resulting individual

- 17 -



347 348

values will have substantial uncertainties due to the parameter uncertainties (e.g. 23 # or 123 ). Besides, the isotopic measurement uncertainty of 0.5 ‰ corresponds to an

349

uncertainty of nearly 8 h in the calculated *+,,E . Furthermore, *+,,E calculation based on

350

Eq. 3 requires knowledge of [OH]av. Here, a climatological average value for the

351

investigated season and region was implemented, as reported by Spivakovsky et al.38.

352

However, for individual trajectories, [OH] may differ from this climatological average,

353

thus contributing to the *+,,E uncertainty. On the other side, *+, determined from the

354

FLEXPART modelling, further on referred to as *+,,'F+G , has significant uncertainties due

355

to the sensitivity of trajectory analysis on meteorological data. Another source of

356

uncertainty for the FLEXPART model derived age is the potential spatial and temporal

357

variability of the BB emission intensities. Although the observed cumulated fire counts

358

for November and December 2009 (Figure S5, Supporting Information) support the

359

assumption of a widespread BB burning activity in the studied region on average, a

360

possible day-to-day variability is not addressed as such in the proposed approach.

361

Consequently, it is not surprising that the comparison of *+,,'F+G with *+,,E calculated

362

using Eq. 3 shows a substantial apparently random variability (for a detailed statistical

363

analysis, see Supporting Information, section S7). However, on average the age of

364

levoglucosan derived from the two methods is very similar, 11.1 ± 0.6 h and 12.2 ± 1.5 h,

365

respectively. The difference of 1.1 ± 1.6 h, corresponding to a relative difference of 9 ±

366

16 %, is statistically not significant.

367

Finally, potential sources of bias in deriving the photochemical age from the different

368

approaches are discussed. In the case of *+,,E there is the assumption of a uniform carbon

369

isotope ratio for all BB emissions along a given trajectory of either category. The

- 18 -


Page 18 of 25

Page 19 of 25


370

assignment of separate C ranges to the two designated categories implies a dominant

371

type of BB emissions along the trajectories, but does not tackle the possibility of

372

contributions from other BB source types, with heavier or lighter levoglucosan isotope

373

ratios. For the CAT1 samples, the average C reported for China fir and Chinese red

374

pine33 was used. This value is at the high end of the isotope ratios for levoglucosan

375

emitted during BB. It is 2.3 ‰ heavier than the mean C of levoglucosan emitted by

376

hardwoods or crop residue combustion33. Consequently, for CAT1, the contribution of

377

isotopically lighter BB sources (e.g. hardwood or crop residue combustion) will introduce

378

a bias towards *+,,E shorter than *+,,'F+G . In contrast, for CAT2, contributions from

379

heavier sources (e.g. China fir or Chinese red pine burning) would bias the calculated

380

*+,,E towards higher values, compared to the simulation results. Indeed, the calculated

381

*+,,E for the CAT1 samples is on average 3 ± 1.6 h shorter than *+,,'F+G , whereas for the

382

CAT2 samples, the isotope ratio derived age is 4.5 ± 1.5 h longer than that from

383

FLEXPART simulations.

384

Possible bias for *+,,'F+G might also arise from the limitation of trajectory calculations to

385

three days, which corresponds to 30 h of daylight. However, on average, the differences

386

between *+,,E and *+,,'F+G are statistically not significant (see above and section S7 in

387

Supporting Information). Moreover, based on the standard deviation and number of data

388

pairs (Table S5) the probability of a bias exceeding 3 h is less than 10%.

389

Perhaps the most important finding to come out of this study is that for approximately 50%

390

of the samples analyzed, the *+,,E exceeds 11 h, which corresponds to loss of nearly 30%

391

of levoglucosan due to chemical reaction. For about 20% of the samples the chemical loss

392

of levoglucosan exceeds 50%. In conclusion, the use of levoglucosan as inert BB

- 19 -



393

molecular marker might significantly underestimate the impact of BB on the sample

394

composition. Carbon isotope ratio measurements of levoglucosan can be used to correct

395

for this bias. A reduction of the levoglucosan concentration by 30% corresponds to a

396

change in the levoglucosan carbon isotope ratio by approximately 0.8 ‰, which is larger

397

than the uncertainty of the carbon isotope ratio measurements.

398

Figure 4: Plot of 789,7H8I calculated by Flexpart versus 789,J determined from levoglucosan carbon isotope ratios binned for 4 h intervals of 789,7H8I . The error bars represent the standard error of the mean. The dashed line shows the dependence 789,7H8I = 789,J .

399 400 401 402 403

Caution is necessary due to the uncertainties in the carbon isotope ratio of levoglucosan

404

emitted by BB of different parent material. Other critical issues here are the identification

405

and estimation of the contributions of the different source types to the measured samples.

406

The approach described in this study introduces simplifying assumptions to fill the gaps

407

in this knowledge, such as the homogeneity of levoglucosan δ13C0 values, which are

- 20 -


Page 20 of 25

Page 21 of 25


408

employed to determine the photochemical age of levoglucosan, or the premise that the

409

origin of the trajectories coincides with the injection height of the BB aerosol. The

410

relative standard deviation of the linear regression analysis introduced due the considered

411

δ13C variation for source aerosol33 is approximately 32%. In addition, there is possibly

412

systematic bias for the slope and intercept of the regression shown in Figure 3, caused by

413

errors in the KIE and the rate constant for the reaction of levoglucosan (see above), as

414

well as in the employed average concentration of OH-radicals. Yet, the very good

415

agreement between the average of photochemical ages derived from isotope ratio

416

measurements with those resulting from FLEXPART trajectory calculations (Figure 4)

417

rules out a major bias in either of the two independent methods used to determine the

418

extent of photochemical processing of levoglucosan in the atmosphere.

419 420

Supplementary Information: Texts S1-S7, Figures S1-S5, Tables S1-S5

421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439

1. Lei, W.; Li, G.; Molina, L. T., Modeling the impacts of biomass burning on air quality in and around Mexico City. Atmos. Chem. Phys. 2013, 13, 2299-2319, 21 pp. 2. Paraskevopoulou, D.; Liakakou, E.; Gerasopoulos, E.; Mihalopoulos, N., Sources of atmospheric aerosol from long-term measurements (5 years) of chemical composition in Athens, Greece. Sci. Total Environ. 2015, 527-528, 165-178. 3. van Drooge, B. L.; Fontal, M.; Bravo, N.; Fernandez, P.; Fernandez, M. A.; Munoz-Arnanz, J.; Jimenez, B.; Grimalt, J. O., Seasonal and spatial variation of organic tracers for biomass burning in PM1 aerosols from highly insolated urban areas. Environ. Sci. Pollut. Res. 2014, 21, 11661-11670. 4. Cheng, Y.; Engling, G.; He, K. B.; Duan, F. K.; Ma, Y. L.; Du, Z. Y.; Liu, J. M.; Zheng, M.; Weber, R. J., Biomass burning contribution to Beijing aerosol. Atmos. Chem. Phys. 2013, 13, 7765-7781, 17 pp. 5. Chowdhury, Z.; Zheng, M.; Schauer, J. J.; Sheesley, R. J.; Salmon, L. G.; Cass, G. R.; Russell, A. G., Speciation of ambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities. J. Geophys. Res., [Atmos.] 2007, 112, D15303/1-D15303/14. 6. Reisen, F.; Meyer, C. P.; Keywood, M. D., Impact of biomass burning sources on seasonal aerosol air quality. Atmos. Environ. 2013, 67, 437-447. - 21 -



440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483

7. Zhang, Z.; Gao, J.; Engling, G.; Tao, J.; Chai, F.; Zhang, L.; Zhang, R.; Sang, X.; Chan, C.-y.; Lin, Z.; Cao, J., Characteristics and applications of size-segregated biomass burning tracers in China's Pearl River Delta region. Atmos. Environ. 2015, 102, 290-301. 8. Calhoun, D. D.; Salmon, L. G.; Schauer, J. J.; Christoforou, C. S., PM2.5 characterization and source-receptor relations in South Carolina. J. Environ. Eng. Sci. 2003, 2, 441-451. 9. Fine, P. M.; Chakrabarti, B.; Krudysz, M.; Schauer, J. J.; Sioutas, C., Diurnal variations of individual organic compound constituents of ultrafine and accumulation mode particulate matter in the Los Angeles Basin. Environ Sci Technol 2004, 38, 12961304. 10. Yin, J.; Harrison, R. M.; Chen, Q.; Rutter, A.; Schauer, J. J., Source apportionment of fine particles at urban background and rural sites in the UK atmosphere. Atmos. Environ. 2010, 44, 841-851. 11. Simoneit, B. R. T.; Rogge, W. F.; Lang, Q.; Jaffe, R., Molecular characterization of smoke from campfire burning of pine wood (Pinus elliottii). Chemosphere: Global Change Sci. 2000, 2, 107-122. 12. Simoneit, B. R. T.; Schauer, J. J.; Nolte, C. G.; Oros, D. R.; Elias, V. O.; Fraser, M. P.; Rogge, W. F.; Cass, G. R., Levoglucosan, a tracer for cellulose in biomass burning and atmospheric particles. Atmos. Environ. 1998, 33, 173-182. 13. Busby, B. D. W., T. J.; Turner, J. R.; Palmer, C. P., Comparison and Evaluation of Methods to Apportion Ambient PM2.5 to Residential Wood Heating in Fairbanks, AK. In Aerosol and Air Quality Res., 2016; Vol. 16, 492–503. 14. Engling, G.; He, J.; Betha, R.; Balasubramanian, R., Assessing the regional impact of Indonesian biomass burning emissions based on organic molecular tracers and chemical mass balance modeling. Atmos. Chem. Phys. 2014, 14, 8043-8054, 12 pp. 15. Robinson, A. L.; Subramanian, R.; Donahue, N. M.; Bernardo-Bricker, A.; Rogge, W. F., Source Apportionment of Molecular Markers and Organic Aerosol. 2. Biomass Smoke. Environ. Sci. Technol. 2006, 40, 7811-7819. 16. Schauer, J. J.; Rogge, W. F.; Hildemann, L. M.; Mazurek, M. A.; Cass, G. R.; Simoneit, B. R. T., Source apportionment of airborne particulate matter using organic compounds as tracers. Atmos. Environ. 2008, 41, 241-259. 17. Sheesley, R. J.; Schauer, J. J.; Zheng, M.; Wang, B., Sensitivity of molecular marker-based CMB models to biomass burning source profiles. Atmos. Environ. 2007, 41, 9050-9063. 18. Zheng, M.; Ke, L.; Edgerton, E. S.; Schauer, J. J.; Dong, M.; Russell, A. G., Spatial distribution of carbonaceous aerosol in the southeastern United States using molecular markers and carbon isotope data. J. Geophys. Res., [Atmos.] 2006, 111, D10S06/1-D10S06/14. 19. Fine, P. M.; Cass, G. R.; Simoneit, B. R. T., Organic compounds in biomass smoke from residential wood combustion: Emissions characterization at a continental scale. J. Geophys. Res., [Atmos.] 2002, 107, ICC11/1-ICC11/9. 20. Mochida, M.; Kawamura, K.; Fu, P.-Q.; Takemura, T., Seasonal variation of levoglucosan in aerosols over the western North Pacific and its assessment as a biomassburning tracer. Atmos. Environ. 2010, 44, 3511-3518.

- 22 -


Page 22 of 25

Page 23 of 25

484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528


21. Hennigan, C. J.; Sullivan, A. P.; Collett, J. L., Jr.; Robinson, A. L., Levoglucosan stability in biomass burning particles exposed to hydroxyl radicals. Geophys. Res. Lett. 2010, 37, L09806/1-L09806/4. 22. Sang, X. F.; Gensch, I.; Kammer, B.; Khan, A.; Kleist, E.; Laumer, W.; Schlag, P.; Schmitt, S. H.; Wildt, J.; Zhao, R.; Mungall, E. L.; Abbatt, J. P. D.; Kiendler-Scharr, A., Chemical stability of levoglucosan: An isotopic perspective. Geophys. Res. Lett. 2016, 43, 5419-5424. 23. Sang, X.; Zhang, Z.; Chan, C.; Engling, G., Source categories and contribution of biomass smoke to organic aerosol over the southeastern Tibetan Plateau. Atmos. Environ. 2013, 78, 113-123. 24. Gensch, I.; Kiendler-Scharr, A.; Rudolph, J., Isotope ratio studies of atmospheric organic compounds: Principles, methods, applications and potential. Int. J. Mass Spectrom. 2014, 365-366, 206-221. 25. Goldstein, A. H.; Shaw, S. L., Isotopes of Volatile Organic Compounds: An Emerging Approach for Studying Atmospheric Budgets and Chemistry. Chemical Reviews 2003, 103, (12), 5025-5048. 26. Saccon, M.; Kornilova, A.; Huang, L.; Moukhtar, S.; Rudolph, J., Stable carbon isotope ratios of ambient secondary organic aerosols in Toronto. Atmos. Chem. Phys. 2015, 15, 10825-10838. 27. Wintel, J.; Hoesen, E.; Koppmann, R.; Krebsbach, M.; Hofzumahaus, A.; Rohrer, F., Stable carbon isotope ratios of toluene in the boundary layer and the lower free troposphere. Atmos. Chem. Phys. 2013, 13, 11059-11071. 28. Rudolph, J.; Czuba, E., On the use of isotopic composition measurements of volatile organic compounds to determine the “photochemical age” of an air mass. Geophysical Research Letters 2000, 27, (23), 3865-3868. 29. Saito, T.; Stein, O.; Tsunogai, U.; Kawamura, K.; Nakatsuka, T.; Gamo, T.; Yoshida, N., Stable carbon isotope ratios of ethane over the North Pacific: atmospheric measurements and global chemical transport modeling. J. Geophys. Res., [Atmos.] 2011, 116, D02308/1-D02308/8. 30. Stein, O.; Rudolph, J., Modeling and interpretation of stable carbon isotope ratios of ethane in global chemical transport models. J. Geophys. Res., [Atmos.] 2007, 112, D14308/1-D14308/18, 10 plates. 31. Stohl, A.; Forster, C.; Frank, A.; Seibert, P.; Wotawa, G., Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys. 2005, 5, 2461-2474. 32. Stohl, A.; Sodemann, H.; Eckhardt, S.; Frank, A.; Seibert, P.; Wotawa, G., The Lagrangian particle dispersion model FLEXPART version 8.2. In https://www.flexpart.eu/, 2010. 33. Sang, X. F.; Gensch, I.; Laumer, W.; Kammer, B.; Chan, C. Y.; Engling, G.; Wahner, A.; Wissel, H.; Kiendler-Scharr, A., Stable Carbon Isotope Ratio Analysis of Anhydrosugars in Biomass Burning Aerosol Particles from Source Samples. Environ. Sci. Technol. 2012, 46, 3312-3318. 34. Craig, H., Isotopic standards for carbon and oxygen and correction factors for mass-spectrometric analysis of carbon dioxide. Geochimica et Cosmochimica Acta 1957, 12, (1–2), 133-149.

- 23 -



529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560

35. Brand, W. A.; Assonov, S. S.; Coplen, T. B., Correction for the 17O interference in δ(13C) measurements when analyzing CO2 with stable isotope mass spectrometry (IUPAC Technical Report). Pure Appl. Chem. 2010, 82, 1719-1733. 36. Lawrence, M. G.; Joeckel, P.; von Kuhlmann, R., What does the global mean OH concentration tell us? Atmos. Chem. Phys. 2001, 1, 37-49. 37. Lu, K. D.; Hofzumahaus, A.; Holland, F.; Bohn, B.; Brauers, T.; Fuchs, H.; Hu, M.; Haeseler, R.; Kita, K.; Kondo, Y.; Li, X.; Lou, S. R.; Oebel, A.; Shao, M.; Zeng, L. M.; Wahner, A.; Zhu, T.; Zhang, Y. H.; Rohrer, F., Missing OH source in a suburban environment near Beijing: observed and modelled OH and HO2 concentrations in summer 2006. Atmos. Chem. Phys. 2013, 13, 1057-1080, 24 pp. 38. Spivakovsky, C. M.; Logan, J. A.; Montzka, S. A.; Balkanski, Y. J.; ForemanFowler, M.; Jones, D. B. A.; Horowitz, L. W.; Fusco, A. C.; Brenninkmeijer, C. A. M.; Prather, M. J.; Wofsy, S. C.; McElroy, M. B., Three-dimensional climatological distribution of tropospheric OH: update and evaluation. J. Geophys. Res., [Atmos.] 2000, 105, (D7), 8931-8980. 39. Persson, A., User Guide to ECMWF Forecast Products 3.1. Meteorological Bulletin 2001, M3.2. 40. Stohl, A.; Eckhardt, S.; Forster, C.; James, P.; Spichtinger, N.; Seibert, P., A replacement for simple back trajectory calculations in the interpretation of atmospheric trace substance measurements. Atmos. Environ. 2002, 36, 4635-4648. 41. Fabbri, D.; Marynowski, L.; Fabiańska, M. J.; Zatoń, M.; Simoneit, B. R. T., Levoglucosan and Other Cellulose Markers in Pyrolysates of Miocene Lignites: Geochemical and Environmental Implications. Environmental Science & Technology 2008, 42, (8), 2957-2963. 42. Yan, C.; Zheng, M.; Shen, G.; Li, X.; Zhou, T.; Sullivan, A. P.; Desyaterik, Y.; Collett, J. L., Jr.; Chen, Y.; Wang, S.; Zhao, B.; Cai, S.; Gustafsson, O., Residential Coal Combustion as a Source of Levoglucosan in China. Environ Sci Technol 2018, 52, (3), 1665-1674. 43. Dornbusch, J., Diercke Weltatlas. Westermann: Braunschweig, 1996; Vol. 4., aktual. Aufl. , p 275. 44. Scholze, M.; Ciais, P.; Heimann, M., Modeling terrestrial 13C cycling: climate, land use, and fire. Global Biogeochem. Cycles 2008, 22, GB1009/1-GB1009/13, 8 plates.

- 24 -


Page 24 of 25

Page 25 of 25



Using Î´13C of levoglucosan as a chemical clock - Environmental

Recommend Documents