Scholarly article on topic 'Influence of biomass burning on mixing state of sub-micron aerosol particles in the North China Plain'

Influence of biomass burning on mixing state of sub-micron aerosol particles in the North China Plain Academic research paper on "Earth and related environmental sciences"

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{"Biomass burning" / "Sub-micron aerosol particles" / "Mixing state"}

Abstract of research paper on Earth and related environmental sciences, author of scientific article — Simonas Kecorius, Nan Ma, Monique Teich, Dominik van Pinxteren, Shenglan Zhang, et al.

Abstract Particulate emissions from crop residue burning decrease the air quality as well as influence aerosol radiative properties on a regional scale. The North China Plain (NCP) is known for the large scale biomass burning (BB) of field residues, which often results in heavy haze pollution episodes across the region. We have been able to capture a unique BB episode during the international CAREBeijing-NCP intensive field campaign in Wangdu in the NCP (38.6°N, 115.2°E) from June to July 2014. It was found that aerosol particles originating from this BB event showed a significantly different mixing state compared with clean and non-BB pollution episodes. BB originated particles showed a narrower probability density function (PDF) of shrink factor (SF). And the maximum was found at shrink factor of 0.6, which is higher than in other episodes. The non-volatile particle number fraction during the BB episode decreased to 3% and was the lowest measured value compared to all other predefined episodes. To evaluate the influence of particle mixing state on aerosol single scattering albedo (SSA), SSA at different RHs was simulated using the measured aerosol physical-chemical properties. The differences between the calculated SSA for biomass burning, clean and pollution episodes are significant, meaning that the variation of SSA in different pollution conditions needs to be considered in the evaluation of aerosol direct radiative effects in the NCP. And the calculated SSA was found to be quite sensitive on the mixing state of BC, especially at low-RH condition. The simulated SSA was also compared with the measured values. For all the three predefined episodes, the measured SSA are very close to the calculated ones with assumed mixing states of homogeneously internal and core-shell internal mixing, indicating that both of the conception models are appropriate for the calculation of ambient SSA in the NCP.

Academic research paper on topic "Influence of biomass burning on mixing state of sub-micron aerosol particles in the North China Plain"

Accepted Manuscript

Influence of biomass burning on mixing state of sub-micron aerosol particles in the North China Plain

Simonas Kecorius, Nan Ma, Monique Teich, Dominik van Pinxteren, Shenglan Zhang, Johannes Größ, Gerald Spindler, Konrad Müller, Yoshiteru linuma, Min Hu, Hartmut Herrmann, Alfred Wiedensohler

Pll: S1352-2310(17)30324-2

DOI: 10.1016/j.atmosenv.2017.05.023

Reference: AEA 15332

To appear in: Atmospheric Environment

Received Date: 20 November 2016 Revised Date: 25 March 2017 Accepted Date: 16 May 2017

Please cite this article as: Kecorius, S., Ma, N., Teich, M., van Pinxteren, D., Zhang, S., Größ, J., Spindler, G., Müller, K., linuma, Y., Hu, M., Herrmann, H., Wiedensohler, A., Influence of biomass burning on mixing state of sub-micron aerosol particles in the North China Plain, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.05.023.

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1 Influence of biomass burning on mixing state of sub-micron aerosol

2 particles in the North China Plain

11* 1 1 3

4 Simonas Kecorius , Nan Ma , Monique Teich , Dominik van Pinxteren , Shenglan Zhang ,

5 Johannes Größ1, Gerald Spindler1, Konrad Müller1, Yoshiteru Iinuma1, Min Hu2, Hartmut

6 Herrmann1, Alfred Wiedensohler1.

8 1Leibniz-Institute for Tropospheric Research, Permoserstr. 15, Leipzig, Germany

9 College of Environmental Sciences and Engineering, Peking University, Beijing 100871,

10 China

11 Chengdu University of Information Technology, Chengdu 610225, China

14 Abstract

16 Particulate emissions from crop residue burning decrease the air quality as well as

17 influence aerosol radiative properties on a regional scale. The North China Plain (NCP) is

18 known for the large scale biomass burning (BB) of field residues, which often results in heavy

19 haze pollution episodes across the region. We have been able to capture a unique BB episode

20 during the international CAREBeijing-NCP intensive field campaign in Wangdu in the NCP

21 (38.6oN, 115.2oE) from June to July, 2014. It was found that aerosol particles originating from

22 this BB event showed a significantly different mixing state compared with clean and non-BB

23 pollution episodes. BB originated particles showed a narrower probability density function

24 (PDF) of shrink factor (SF). And the maximum was found at shrink factor of 0.6, which is

25 higher than in other episodes. The non-volatile particle number fraction during the BB

26 episode decreased to 3% and was the lowest measured value compared to all other predefined

27 episodes. To evaluate the impact of biomass burning on aerosol single scattering albedo

28 (SSA), SSA at different RHs was simulated using the measured aerosol physical-chemical

29 properties. The differences between the calculated SSA for biomass burning, clean and

30 pollution episodes are significant, meaning that the variation of SSA in different pollution

31 conditions needs to be considered in the evaluation of aerosol direct radiative effects in the

32 NCP. And the calculated SSA was found to be quite sensitive on the mixing state of BC,

33 especially at low-RH condition. The simulated SSA was also compared with the measured

34 values. For all the three predefined episodes, the measured SSA are very close to the

35 calculated ones with assumed mixing states of homogeneously internal and core-shell internal

36 mixing, indicating that both of the conception models are appropriate for the calculation of

37 ambient SSA in the NCP.

39 Keywords: biomass burning; sub-micron aerosol particles; mixing state

40 1. Introduction

42 Biomass burning (BB) is one of the major sources of atmospheric carbonaceous aerosol

43 particles. During BB processes, high quantities of organic compounds, elemental carbon,

44 carbon monoxide and carbon dioxide gas, mercury and other pollutants are emitted to the

45 atmosphere (e.g. Andreae and Merlet, 2001; Pirrone et al., 2010). BB is known to have effects

46 on regional air quality and climate. For example, a significant relation between childhood

47 asthma admissions and increase in particulate matter (PM) from BB was showed by Nastos et

48 al. (2010). Transported to remote areas, BB products can cause the exceedances of

49 predetermined particulate matter limits, decreased visibility and the degradation of air quality

50 (e.g. Jaffe et al., 2003; Jacob et al., 2003). Aerosol generated in BB may also have a large

51 radiative forcing on regional scales (Christopher et al., 1996; Hobb et al., 1997). Its radiative

52 forcing is found to be strongly influenced by its aging state (e.g. Zhuang et al., 2013; Chung

53 et al., 2012; Jacobson 2001).

54 Worldwide, chemical composition and mixing state of BB aerosol were the subjects in

55 numerous studies. Pratt et al. (2011) found that in fresh BB plume the average mass fraction

56 of particles was dominated by organics (80-90%), followed by soot (0.3-13%), nitrate (1-3%),

57 chloride (0.8-2%), sulfate (0-1%) and ammonium (0.2-0.4%). In another study (Reid et al.,

58 2005), inorganic salts were found to account for 12 to 15% of fresh smoke particles. Non-

59 volatile water-insoluble organic polymers, to some extent similar to high-molecular weight

60 humic-like substances (HULIS) were also reported to be products of BB (Posfai et al., 2004;

61 Tivanski et al., 2007). These substances are known as the tar balls (TB). Recent laboratory

62 studies also suggest that TBs are produced by the ejection of liquid tar droplets during the

63 biomass burning, which then dominate the number fraction of BB particles in a size range

64 from 100 to 600 nm (Toth et al. 2014).

65 A recent study in Germany using Weather Research and Forecast (WRF) model showed

66 that the particle light absorption coefficient can be biased by 20% (Nordmann et al., 2014).

67 The treatment of the state of mixing (external versus internal) of black carbon (BC) in the

68 WRF was underlined as a potential cause for miscalculation of optical properties. The

69 importance of the state of mixing of aerosol particles was also addressed in regional climate

70 and chemistry modeling system RegCCMS (Zhuang et al., 2013). It was shown that the

71 mixing state and hygroscopicity of carbonaceous aerosol particle determine aerosol direct

72 radiative forcing and corresponding climate responses.

73 In China, aerosol particle mixing state during biomass burning was also addressed by

74 several studies. Bi et al. (2011) found that in the Pearl River Delta region, as much as 90% of

75 the particles were mixed internally together with some secondary inorganic species. K-Na-

76 rich substances were observed in 12% of the particles. The fractions of nitrate and particulate

77 sulfate in particles in the accumulation size range were by 10% higher than in non-BB related

78 pollution episodes. A study in the Guanzhong Plain, Xi'an, showed that during polluted

79 episodes about 49% of the refractory BC was internally mixed with non-refractory materials,

80 which is 27% higher than in clean periods (Wang et al., 2014). Organics were suggested to be

81 a primary contributor to BC coating, which was found to enhance light absorption by a factor

82 of 1.8. Lan et al. (2013) investigated the influence of BC mixing state on its mass absorption

83 efficiency (MAE) in south China. The percentage of internally mixed BC was found to be

84 24%, amplifying MAE by 7%.

85 To our knowledge, studies on the mixing state of aerosol particle properties, during BB

86 events, specifically in the North China Plain (NCP) are still scarce. This limitation may

87 significantly contribute to the uncertainties in the evaluation of aerosol radiative forcing,

88 especially in the North China Plain (NCP), where most of China's agricultural fires are

89 registered. The NCP, covering an area of about 410000 square kilometers, is the area where

90 up to 50% of annual China demand of cotton and cereal is produced (Godfray et al., 2010). It

is also the major region for wheat, meaning that harvest seasons usually starts in June, after which BB activities start to spread from south to north (Ni et al., 2015). It leads to a regional degradation of air quality and increases the climatic change over East Asia (Zheng et al., 2005; Cheng et al., 2013; Jeong et al., 2014).

Recent assessments of aerosol radiative effects have highlighted the need for better spatial coverage of BC mass concentration, mixing state and optical properties, which are crucial for reducing the uncertainties in radiative forcing evaluation and improving pollution control strategies. In this study, we have applied the state of art instrumentation coupled with offline chemical analysis to investigate the mixing state of BB aerosol particles and the resulting aerosol optical properties in the NCP.

2. Experimental

2.1. Measurement Site

The measurements were conducted at a rural site in the NCP (38.6oN 115.2oE, shown as the black square in Fig. 3), around 5 km south-east of Wangdu town and 170 km south-west of Beijing. Such a site is the best choice for investigating the well-mixed regional air pollution of the NCP as there were no industrial or residential activities in the vicinity of the station. The measurement containers were located in an open field surrounded by farmland. The nearest living areas and the roads were about 2 and 1 km away from the measurement site, respectively. The aerosol instruments were placed in an air-conditioned (24 oC) TROPOS mobile laboratory. The sampling line consists of a PM10 inlet (16.67 l min-1, 6 m above ground level), followed by two 1.5 m Nafion dryers and an automatic drying chamber, in which the relative humidity (RH) of aerosol sample was regulated to be below 30% (Tuch et al., 2009). The aerosol sample was directed to the instruments via an isokinetic flow splitter and stainless steel/conductive rubber tubing.

2.2. Volatility Tandem Differential Mobility Analyzer (V-TDMA)

In this study, a TROPOS-type V-TDMA system (Philippin et al. 2004) was used to determine the mixing state of aerosol particles in terms of volatility. The refractory fractions of aerosol particles with certain sizes (20, 30, 50, 100, 150 and 250 nm) were retrieved by evaporating the volatile material in a thermal conditioning column at 300 °C. V-TDMA scans at 25 °C were used to calibrate the kernel function (Gysel et al., 2009). Particle transport efficiency was measured using generated NaCl particles under laboratory conditions. The time resolution of the measurement was adjusted to be less than one hour.

In this study, species which did not evaporate at 300 °C were referred as a refractory. TDMAinv routine (Gysel et al., 2009) was used to retrieve the Probability Density Function of shrink factor (SF-PDF). The shrink factor is defined as SF = where D25 and D300 are

respectively particle diameters at 25 °C and 300 °C. SF-PDFs were integrated within the ranges of SF >0.85, 0.5<SF<0.85 and SF<0.5 to retrieve number fractions of non-volatile (NV), semi volatile (SV) and highly volatile (HV) particles, respectively. We have also used a term "externally mixed" further in the paper, which refers to the number fraction of NV particles. Furthermore, an assumption that all particles contain a non-volatile core has been made. In general, this assumption leads to an overestimation of the refractory fraction because of two reasons. Firstly, heated aerosol particles may shrink to sizes below the detection limit (10 nm) of the condensation particle counter (CPC). Secondly, the number fraction of completely volatile particles usually does not exceed 10-20%, but might be as high as 60% during new particle formation events (Wehner et al., 2009). The authors are aware of these

possible sources of uncertainties and the needed precautions were made in data evaluation process and discussion of the results. The volume fraction remaining (VFR) of aerosol particles after heating was extracted from TDMAinv routine as explained by Gysel et al. (2009).

2.3. High Humidity Tandem Differential Mobility Analyzer (HH-TDMA)

Size-resolved hygroscopic growth factor (GF) of ambient aerosol particles at high RH was measured using a TROPOS HH-TDMA (Hennig et al., 2005; Liu et al., 2011). Particle

hygroscopic growth factor is defined as GF = -, where DRH and Ddry are respectively

particle diameters at dry condition and after being humidified. The main difference between the conventional H-TDMA (Rader and Mc-Murry, 1986) and the high humidity system lays in the temperature stability and humidification of the aerosol/sheath air. Temperature fluctuations in the second differential mobility analyzer (DMA) cause instabilities in RH, making measurements at RHs above 90% nearly impossible with conventional systems. In HH-TDMA, two separate water baths are used to control the temperature of both the humidity conditioning section and the second DMA which are immersed in water. Temperature is maintained within ±0.1 K with a stability of ±0.02K. This ensures that RH in the second DMA can reach 98% with ±1.2% accuracy. To calibrate the RH in HH-TDMA system, the GFs of ammonium sulfate particles of 100 nm dry diameter were measured every day at 17:00 LT. During the measurement campaign the dry initial diameters selected by the first DMA were set to 30, 50, 100, 150, 200, 250 nm and 98% RH was chosen in the second DMA. The time resolution of the system was adjusted to be approximately one hour.

TDMAinv routine by Gysel et al. (2009) was used to retrieve the Probability Density Functions of GF (GF-PDF). To get rid of the curvature effect and facilitate the comparison of hygroscopicity of particles with different diameters, hygroscopicity parameter k is calculated (Petters and Kreidenweis, 2007):

Where S is saturation ratio; pw is water density; Mw is the molecular weight of water; os/a is the surface tension of the solution / air interface which is assumed to be the same as the surface tension of the pure water / air interface; R is the universal gas constant; T is the temperature. The probability density functions of k (k-PDFs) were integrated between three ranges to get the number fractions of nearly hydrophobic (NH, k < 0.1), slightly hygroscopic (SH, 0.1 < k < 0.2) and more hygroscopic (MH, k > 0.2) particles.

2.4. Particle Number Size Distribution Measurements

Dry-state (RH below 30%) particle number size distributions (PNSD) in a mobility size range from 3 to 800 nm were recorded using a TROPOS-type dual mobility particle size spectrometer (MPSS). The detailed principles of the custom built closed loop TROPOS MPSS is described in Wiedensohler et al. (2012). Recorded particle mobility distributions were inverted to PNSD using the algorithm developed by Pfeifer et al. (2014). The time resolution of the measurement is 10 min. Corrections for transmission losses in the sampling lines and CPC counting efficiency were also applied to the data (Wiedensohler et al., 1997). Polystyrene latex spheres (PSL, Thermo Scientific, Duke Standards) of 203 nm were used to check the sizing accuracy of the DMAs. And the high voltage supply offset calibrations were performed regularly on all DMA based instruments. Instrument flow checks (using primary standard airflow calibrator) and zero checks for all instruments were performed on a weekly

basis. On average deviations in the sample flow did not exceed 1.5% for all instruments throughout the measurement campaign.

2.5. Particle Light Absorption Measurements

A multi-angle absorption photometer (MAAP Model 5012, Thermo, Inc., Waltham, MA USA, Petzold and Schonlinner, 2004) was used to quantify the particle light absorption coefficient at a wavelength of 637 nm. The instrument operates by converting the light attenuation into an absorption coefficient. Backscattering of the filter is included in radiative transfer calculation for a higher accuracy. The MAAP flow was adjusted to 3 L min-1 using a custom made nozzle to save filter material. The temporal resolution of the measurement was set to 1 min. The MAAP may suffer from an artifact after a filter spot change at high absorption coefficient (Hyvarinen et al., 2013). This artifact was visible in our measurement, and the influenced data was removed from the time series.

2.6. Particle Light Scattering Measurements

Aerosol light scattering and hemispheric backscattering coefficient were measured at wavelengths of 450, 550 and 700 nm with an integrating nephelometer (Model 3563, TSI, Inc., Shoreview, MN USA; Heintzenberg and Charlson, 1996; Anderson et al., 1996). The temporal resolution of the measurement was 1 min. The nephelometer was regularly calibrated using clean air and CO2. And the calibration constants stayed within 5% during the campaign period. Checks with particle-free zero air were performed four times per day.

2.7. Aerosol Chemical Measurements

For an offline filter analysis, PM10 aerosol particles (<10 |im) were collected on quartz fiber filters using a high volume sampler. Samples were taken with 12-hour time resolution (day: 06:00 - 18:00 LT, night: 18:00 - 06:00 LT) at a flow rate of 0.5 m3 min-1. The filters were pre-heated at 105°C for 24 h before usage. Aerosol loaded filters were stored at -20°C until analysis. A portion of the sampled filter (9.42 cm2) was extracted and dissolved into 20 mL of ultrapure water by shaking with a laboratory orbital shaker for 120 min. An aliquot of the solution was analyzed for levoglucosan, mannosan and galactosan as described by Iinuma et al. (2009). Another aliquot was analyzed for inorganic ions (K+, Na+, NH4+, Mg+, Ca2+, Cl-, NO3-, SO42-, NO2-) by ion chromatography (IC690 Metrohm, Switzerland; ICS3000, Dionex, USA). Organic and elemental carbon (OC and EC) were determined following the EUSAAR 2 protocol (Cavalli et al., 2010). The uncertainties for this method are estimated to be below 10 %.

2.8. Backward trajectories and FIRMS fire information

Information from the Fire Information for Resource Management System (FIRMS) operated by National Aeronautics and Space Administration (NASA) of the United States (available at, accessed 26 November 2015) was used to identify the possible biomass burning regions. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model was used to check particulate matter pathways to the measurement site (Draxler and Rolph, 2013). 48-hour backward trajectories starting at 500 m above ground level were calculated using Global Data Assimilation System meteorological data (Rolph, 2013).

Results and Discussion

3.1. Overview of the measurement

During the intensive measuring period from June 6 to July 6 2014, particle number size distribution, light absorption and scattering coefficients, chemical composition, and mixing state in terms of hygroscopicity and volatility were continuously measured at our station in Wangdu. The temporal variations of measured parameters are shown in Fig. 1 and a statistical summary of the data is presented in Table 1.


: 06/Jun 08/Jun 10/Jun 12/Jun 14/Jun 16/Jun 18/Jun 20/Jun 22/Jun 24/Jun 26/Jun 28/Jun 30/Jun 02/Jul 04/Jul 06/Jul

Fig. 1. Time series of (A) aerosol particle number size distribution, (B) particle number (Ntot) and volume concentration (Vtot), (C) aerosol scattering coefficients (sscat) at 3 wavelengths, (D) black carbon mass concentration and single scattering albedo, and (E) offline chemical analysis of K+, levoglucosan, OC and EC. Blue, red and gray color shades mark the clean episode (EP1), BB episode (EP2) and pollution episode (EP3), respectively.

The arithmetic mean and standard deviation of the particle light absorption coefficient (Sabs), as well as total aerosol particle volume (Vtot) and number concentrations (Ntot) during the measurement period were 31±19 Mm-1, 63±37 mm3 cm-3, and 20200+15100 cm-3, respectively. The scattering coefficient (sscat) at the wavelengths of 450, 550 and 700 nm were 442+288, 345+235 and 236+168 Mm-1, respectively. Offline analysis of PM10 high volume sampler filters showed that campaign average OC and EC mass concentrations were

17.8+8.9 and 3.6+1.7 mg m- , respectively. These results are comparable with previous studies conducted in the NCP (e.g. Ma et al., 2011; Zhang et al., 2016). A clean episode with pronounced new particle formation events (EP1) and two pollution episodes (EP2 and EP3)

264 are selected (marked as colored shades in Fig. 1) and will be discussed more in detail in Sect.

265 3.3.

267 Table 1. Summary of aerosol particle physical-chemical parameters (average ± standard

268 deviation)_

Date 2014- Sabs637, Sscat450, Mm-1 Mm-1 Sscat550, Mm-1 Sscat700, Mm-1 Ntot, cm-3 • 104 Vto, mm3 cm-3 SSA



June 06 -July 07 31±19 442±288 345±235 236±168 2.02±1.51 63±37 0.88±0.06

June 07 -12 (EP1) 25±18 232±166 175±127 118±85 2.53±1.45 41±24 0.83±0.06

June 14 -16 (EP2) 55±21 860±343 636±255 410±164 2.59±1.12 115±41 0.89±0.03

July 02 - 04 (EP3) 32±10 624±130 523±111 379±83 1.14±0.22 84±18 0.93±0.02

Offline analysis OC, -3 mg m3 m EC, Levoglucosan, mg m-3 ng m-3 K+, mg m 3

June 06 -July 07 17.8±8.9 3.6±1.7 264±433 1.5±1.4

June 07 -12 (EP1) 14.6±5.5 3.5±1.8 158±151 0.8±0.4

June 14 -16 (EP2) 43.8±4.4 6.1±1.6 1822±259 6.3±0.8

July 02 - 04 (EP3) 14.5±0.7 3.2±0.2 103±38 1.1±0.4

270 sf k

272 Fig. 2. Campaign-averaged probability density functions (PDF) of shrink factor (A) and kappa

273 (B), the dashed lines show the arithmetic mean values

275 Campaign-averaged probability density functions of shrink factor and hygroscopicity

276 parameter k for different particle sizes are presented in Fig. 2. The integrated number

277 fractions of particles with different volatility and hygroscopicity are list in Table 2. It can be

278 seen that both SF-PDFs and k-PDFs exhibit a clear bi-modal shape.

279 The number fraction of NV particles increases from 4 to 13% with the particle diameter

280 increasing from 50 to 250 nm. This phenomenon was also observed in previous studies and

281 was assigned to locally emitted BC (Wehner et al., 2009; Zhang et al., 2016). The highest

282 number fraction of 55% of HV particles was registered in the smallest measured particle size.

283 The number fraction of HV particles in the diameter range from 100 to 250 nm was around

284 49%.

285 The highest NH particle number fraction of 12% was observed in 50 nm size range. It can

286 also be seen in Fig. 2B that the NH mode of 50 nm particles is slightly shifted towards higher

287 k values. The number fraction of hydrophobic particles with k=0 gradually decreased with a

288 decreasing particle size. This observation was consistent with particle volatility, where NV

289 particle number fraction increased with increasing particle size. The highest number fraction

of MH particles was observed at 250 nm where to the MH mode is centered at k of 0.4. The arithmetic mean of k-PDF shifted towards higher k values with increasing particle size (Fig. 2B). In general, accumulation mode particles were found to be mixed internally more than sub 100 nm particles. This is a result of atmospheric aging, during which aerosol particles become more volatile and hygroscopic (Liu et al., 2011; Zhang et al., 2016).

Table 2. Summary of number fractions of particles with different volatility and hygroscopicity. Avg. in the table means the campaign average; EP1, EP2 and EP3 denote the averages in the three episodes marked in Fig. 1.

Number fraction NV 50 nm 100 nm

Avg. 0.041±0.066 EP1 0.034±0.068 EP2 0.010±0.020 EP3 0.075±0.076 Avg. 0.076±0.054 EP1 0.085±0.061 EP2 0.035±0.029 EP3 0.101±0.048

SV 0.409±0.231 0.295±0.207 0.755±0.238 0.487±0.117 0.431±0.176 0.400±0.168 0.752±0.187 0.372±0.100

HV 0.551±0.247 0.670±0.225 0.235±0.231 0.438±0.144 0.493±0.179 0.514±0.179 w0.213±0.173 0.526±0.127

NH 0.120±0.119 0.143±0.153 0. 190±0.121 0.160±0.081 0.095±0.092 0.117±0.115 0.095±0.069 0.125±0.084

SH 0.180±0.143 0.246±0.162 0.221±0.141 0.163±0.089 0.109±0.117 0.158±0.132 0.139±0.065 0.059±0.034

MH 0.700±0.224 0.611±0.264 0.589±0.197 0.677±0.149 0.796±0.173 0.725±0.201 0.766±0.098 0.816±0.104

Number fraction 150 nm 250 nm

NV Avg. 0.109±0.062 EP1 0.130±0.073 EP2 0.061±0.028 EP3 0.120±0.043 Avg. 0.129±0.088 EP1 0.187±0.101 EP2 0.100±0.140 EP3 0.104±0.040

SV 0.424±0.170 0.415±0.168 0.722±0.144 0.297±0.056 0.384±0.176 0.399±0.167 0.644±0.148 0.222±0.040

HV 0.468±0.176 0.455±0.183 0.218±0.134 0.584±0.084 0.487±0.202 0.415±0.195 0.256±0.117 0.675±0.031

NH 0.107±0.109 0.139±0.134 0.086±0.059 0.112±0.078 0.115±0.128 0.184±0.159 0.112±0.066 0.084±0.073

SH 0.092±0.096 0.103±0.080 0.104±0.057 0.043±0.031 0.065±0.113 0.060±0.050 0.096±0.037 0.012±0.020

MH 0.801±0.174 0.758±0.179 0.810±0.089 0.846±0.099 0.820±0.204 0.756±0.189 0.792±0.076 0.904±0.083

3.2. Biomass burning event

An intensive BB episode was observed on 14 to 16 of June, 2014 (Fig. 1A, EP2). Time variation of the anhydrosugar levoglucosan (1,6-anhydro-D-glucopyranose) and water soluble potassium (K+), commonly taken as tracers for BB (Zhang et al., 2013; Simoneit, et al., 1999), are shown in Fig. 1E. A noticeable increase in mass concentration of levoglucosan and K+ can

be seen on 14 to 16 of June, with maximum values of 2012 ng m- and 7.2 mg m- , respectively. These values were much higher than the mean concentrations during the whole

measurement period (290±420 ng m- and 1.5±1.6 mg m- ). Such increases in mass concentration of levoglucosan and K+ prove that during these days the dominant aerosol particles have originated from biomass burning. Other physical-chemical measures, such as sabs, Sscat, Vtot, OC and EC mass concentrations, were also almost 2 times higher than their campaign averaged values.

Comparing to other BB-related studies, our observed mass concentration of levoglucosan and K+ were considerably higher. Cheng et al. (2013) reported average ambient levoglucosan

and K mass concentrations in summer in Beijing to be 230±370 ng m- and 1.7±2.3 mg m- , respectively. In the same study, the BB episode was characterized by 750±680 ng m- of

levoglucosan and 5.8±2.7 mg m- of K . However, it is worth noting that in the study by

Cheng et al. (2013) the aerosol was sampled through a PM25 inlet, which might have caused underestimates due to different particle cut-off size.


Jun.13 Jun.14 Jun.15 Jun.16 Jun.17

Fig. 3. (A) Fire spots (yellow dots) during June 12-14, 2014 from FIRMS and 48-h backward trajectories (colored lines). The colors of the lines show their different arriving time. On each backward trajectory, the time interval between two small dots is 3 hours, and between two big dots it is 6 hours. (B) Time series of altitude of the backward trajectories.

Satellite images of fire spots from FIRMS were analyzed to determine the region where BB did occur. It can be seen from Fig. 3(A) that the highest density of fire spots were registered in the regions which are known to be a major producers of wheat. The fires of crop residues start to spread in this region at the end of harvest season - June. Air mass backward trajectories of 48 hour (Fig. 3) identified that BB products were transported from the biomass burning region to our measurement station and resulted in a high observed aerosol loading.

3.3. Mixing state of aerosol particles

As highlighted previously, only limited amount of studies of the mixing state of aerosol particles during BB are available for the NCP. In this section we will present particle mixing state in terms of hygroscopicity and volatility during a BB episode (EP2), based on in-situ HH-TDMA and V-TDMA measurements. As a reference, particle mixing state during a clean

342 episode (EP1) and a non-BB related pollution episode (EP3) will be firstly overviewed in

343 section 3.3.1. The mixing state of BB particles and its differences from non-BB episodes will

344 be discussed then in a section 3.3.2. All selected episodes are shown in Fig. 1.

348 Fig. 4. Average probability density functions of shrink factor (A-D) and hygroscopicity

349 parameter k (E-H) at measured particles sizes for the three selected episodes

352 3.3.1. Particle mixing state during clean and non-BB related pollution episodes: a

353 reference

355 An episode with low aerosol particle loading, EP1, during which (Jabs, Scat and Vtot were

356 lower than the overall campaign average, was chosen as a clean episode. During this episode,

357 the mixing state of ultrafine particles is mainly determined by new particle formation (NPF).

358 NPF with a subsequent growth was observed on 52% of the campaign days with air masses

359 prevailing from north, which are known to favor NPF (Wu et al., 2007; Wehner et al., 2008).

360 In previous studies, the growth of nucleation mode particles in China was assigned to

361 secondary produced ammonium sulfate (Wiedensohler et al., 2009; Yue et al., 2010). As a

362 result, the number fraction of HV particles increased to 67% in EP1. Newly formed and

363 slightly grown particles had the lowest VFR and were highly volatile. As shown in Fig. 5A, a

364 clear diurnal pattern in VFR shows that the minimum value, about 5%, of VFR was observed

365 during noon when the NPF was most prominent. Despite high volatility of newly formed

366 particles, non-refractory cores still existed. This might be the result of certain organic

367 components produced in NPF in the NCP (Kecorius et al., 2015). In previous studies it was

368 shown that the non-BC refractory fraction in newly formed particles is possibly formed of

369 non-volatile oligomers, organic salts and polymers (Birmili et al., 2010; Ehn et al., 2014). A

370 clear diurnal variation in VFR was also observed for 100-150 nm diameter particles in EP1.

371 The higher level of VFR during nighttime can be explained by a shallower planetary

372 boundary layer and higher local emission.

A EP1 Clean B EP2 BB C EP3 Polluted

Time of day (h) Time of day (h) Time of day (h)

Fig. 5. Diurnal variation of particle volume fraction remaining (VFR) at 300 oC during clean (A), BB (B) and pollution (C) episode

Another distinct property of aerosol particles in EP1 was their broad SF-PDF (Fig. 4 A-D). During the clean episode, the particles might consist of background aerosol, locally freshly emitted aerosol and secondary formed aerosol etc., and none of them dominates the aerosol mass, which is different from the case in BB and polluted episodes. Particles from different originations might have different volatility. Therefore, a broad distribution of shrink factors was observed in the clean episode.

We have also captured a non-BB related pollution episode (EP3 in Fig. 1), which is defined as a period with Vtot stays within the top 20% highest values for at least 48 h. During this episode, we observed explicitly different particle volatility and hygroscopicity behaviors compared to EP1. As can be seen from volatility distributions in Fig. 4, highly volatile particles with a narrower HV mode dominated the SF-PDF in EP3. Particle VFR varied between 10 to 30% with a clear diurnal pattern and was in the similar range as in EP1, as shown in Fig. 5. However, different from EP1 of which VFR showed a strong size-dependence, the VFRs of particles with different sizes in EP3 were very similar (Fig. 5C). In EP3, aerosol particles underwent intensive aging processes in highly polluted atmosphere and the secondary aerosol production somehow unified particle volatility and hygroscopicity properties. Wehner et al., (2009) showed that secondarily produced materials dominate the volume and mass of aerosol particles in polluted environment in Beijing.

As can be seen from Fig. 4E-H, the k-PDFs in EP3 are dominated by MH mode particles with average number fractions ranging from 68% to 90%. The hygroscopicity of smaller particles (diameter 50, 100 nm) was similar in all episodes with an average MH k = 0.27 and a number fraction of 67%. The biggest difference in hygroscopicity in the three episodes was observed between accumulation mode particles (150, 250 nm). In EP3, the number fraction of MH mode particles was approximately 13% higher than in EP1. The hygroscopicity of 250 nm MH mode particles in EP3 was close to pure ammonium sulfate (k about 0.5 to 0.6) and further supporting the evidence that in highly polluted sulfur-rich environments, aerosol particle volatility and hygroscopicity are significantly influenced by heterogeneous reactions and condensation of sulfate onto preexisting particle surfaces (Posfai et al., 2004).

Therefore, in our observed non-BB related pollution episode secondary aerosol formation was very likely to be driven by sulfate, while in the clean episode aerosol particles probably aged through the secondary organic aerosol production. These findings are consistent with previous works (Takegawa et al., 2009; Yue et al., 2009; Wehner et al., 2009; Liu et al., 2011).

3.3.2. Particle mixing state during the BB episode

Because the particle number concentration during the BB episode (EP2) is much higher than regular ambient level, we assumed that the observed particle physical-chemical properties during BB episode are dominated by BB aerosol. During the BB episode, the number fractions of NV and NH mode particles, which are related to fresh emissions, were much lower compared with other periods. In contrast to EP3, HV mode in EP2 tailed away with increasing particle diameter. The dominant volatile mode centered at SF = 0.6 with a number fraction of 80%, which is 43% higher than the campaign average. All these results suggested that in terms of mixing state, aerosol particles originating from BB were significantly different from those from other episodes. Aerosol particles from BB did not carry thick hygroscopic and volatile coatings as particles from the non-BB pollution episode. In fact, BB originated particles were less volatile than in other episodes. This can be seen in the VFR of particles larger than 100 nm, which ranges from 25% to 40% in BB episode; while during clean and non-BB related pollution episodes particle VFR were in a range from 20% to 30% (Fig. 5). Interestingly, 50 nm particles did not follow the trend of bigger particles in EP2. This suggests that particles produced in BB are mainly in accumulation mode. The dip in VFR in EP2 at 18:00 LT is the result of missing values.

In terms of hygroscopicity, the MH mode in the BB episode centered between k of 0.25 and 0.33. Lager particles showed higher values of k, which was comparable to the values observed in EP1. The MH mode in the BB episode was slightly narrower and its number fraction was only marginally higher than in other episodes. Narrower distribution of MH mode with higher peak value resulted in MH mode number fraction similar to EP1 ~ 77%. The same was also true for SH and NH mode particle number fractions. Therefore, we may conclude that our observed BB originated aerosol particles did not have higher hygroscopicity, at least not higher than in the other predefined periods.

To answer the question, which kind of aerosol particles and which processes resulted in a mixing state of BB aerosol, we have to clarify the constituents of BB emission and then discuss their volatility properties. The non-volatile (at 300 oC) species originating from BB may include: potassium salts that decompose between 400 oC and 1700 oC (Knudsen et al., 2004), EC that decompose at around 900 oC (Jennings et al. 1994), and organic matter of which the majority was found to be semi- or non-volatile (Poulain et al., 2014). As mentioned above, Pósfai et al. (2004) also found tar ball (TB) is rich in BB aerosol. All mentioned BB products may form an externally mixed NV or/and SV mode in a volatility distribution after being treated thermally. This external mixture of BB particles was already found at the emission source in previous studies (e.g. Li et al., 2003). However, in our study, we did not observe any increase in the NV mode particle number fraction during the BB episode. We explain this as follows. During the BB episode, high numbers non-volatile particles were emitted from BB. Since the number concentration of particles created in the combustion process was significantly higher than the background particle concentration, BB originated particles became the dominant in the environment. Before being observed, those particles aged in the air mass during the long range transport. On one hand, coated with secondary produced species with higher volatility, BB products may have turned to be more volatile. On the other hand, the transformation of volatile organic compounds to low-volatility organic matter may have changed BB particles to be less volatile. Such atmospheric processing of BB particles was already observed both in laboratory and field studies. For example, Capes et al. (2008) investigated aerosol in BB plume over West Africa based on aircraft measurements with an aerosol mass spectrometer onboard. The abundance of low-volatility organic matter was explained by atmospheric aging. In laboratory studies, Hennigan et al. (2011) found that photo-oxidation chemically and physically transformed (via condensation and aerosol processing) the organic aerosol including decrease in particle volatility with aging. Tritscher

467 et al. (2011) also showed that particle volatility can be reduced due to OH and O3 induced

468 condensation. Secondary organic aerosol mass production through functionalization or

469 fragmentation increased particle oxidative state, which made particles even less volatile. The

470 competition of above mentioned atmospheric processes may have determined our observed

471 broadened SF-PDFs of BB originated particles.

473 3.3.3. Implication on SSA

475 Previously, we have shown that aerosol particles, produced in biomass burning, are

476 explicitly different in terms of mixing state compared to particles in other episodes (Fig. 4).

477 Thus, it is important to evaluate what effect BB originated aerosol has on the aerosol optical

478 particle properties which determines the aerosol direct radiative forcing. SSA, defined as the

479 ratio of light scattering coefficient to total light extinction coefficient (a sum of scattering and

480 absorption), is a dominant parameter in the estimation of aerosol radiative effects and is also

481 important in satellite retrieval algorithms (McComiskey, 2008). Mishchenko et al., (2004)

482 reported that an accuracy of 0.03 and a precision of 0.02 in the determination of the SSA are

483 required for a reliable quantification of aerosol direct and semi-direct effects on climate. Even

484 small biases in SSA may result in large uncertainties (Takemura et al., 2002).

485 To evaluate the impact of BB on the SSA of ambient aerosol, SSA at different RHs was

486 simulated using the measured aerosol physical-chemical properties. The calculation is based

487 on the methodology presented by Ma et al., (2014) and will not be discussed in detail here.

488 Shortly, three conceptual models of BC mixing state - external mixture (EXT), homogeneous

489 internal mixture (INT), and core-shell internal mixture (CS), were employed to simulate SSA

490 using the BHMIE and BHCOAT models (Bohren and Huffman, 1983; Ma et al., 2014). These

491 three conceptual models have been widely used to assess aerosol optical properties and direct

492 radiative forcing (Jacobson, 2001; Ma et al., 2012). To implement the simulations of the state

493 of mixing to derive the SSA, several assumptions had to be made. Firstly, BC volume fraction

494 was assumed to be constant in sub-micron size range, and 0 in super-micron size range.

495 PNSD at a certain RH was calculated based on the measured size-resolved k (averaged from

496 k-PDF). The refractive indices of BC and other particle species are assumed to be 1.75-0.55i

497 and 1.53-10-6i, respectively. And the density of BC was assumed to be 1.5 gcm-3. The

498 calculation of SSA at RH from 50% to 95% was respectively done for the three selected

499 episodes, based on the average PNSD, BC mass concentration and aerosol hygroscopicity for

500 each episode.

501 Considering that assuming constant refractive indices of aerosol species and density of BC

502 is arbitrary, Monte Carlo approach was used to estimate the uncertainty of the calculated SSA

503 induced by this assumption. The uncertainty (assumed to be 3o hereinafter) of the density of

504 BC was set to 33% to cover the values reported in literature, 1.00 to 2.00 g cm-3 (Sloane et al.,

505 1983, 1984, 1991; Sloane and Wolff, 1985; Ouimette and Flagan, 1982; Seinfeld and Pandis,

506 1998). The uncertainties of the refractive indices were also set in the similar way according to

507 literature (Ouimette and Flagan, 1982; Hasan and Dzubay, 1983; Sloane, 1984; Seinfeld and

508 Pandis, 1998; Covert et al., 1990; Tang and Munkelwitz, 1994). For refractive index of BC, it

509 was assumed to be 12% and 20% for real part and imaginary part, respectively. And for the

510 refractive index of other species it was assumed to be 33% and 1.5%, respectively. The detail

511 of the Monte Carlo simulation can be found in Ma et al. (2014).

-o 0 _Q

TO 0.9 ? C3

0.95 -r

-bb-int -bb-cs bb-ext -pol-int -pol-cs ■■ pol-ext cle-int cle-cs cle-ext

% 0.85

50 55 60 65 70

75 80 85 90 95

rh (%)

Fig. 6. Simulated SSA at different relative humidities (RHs) for biomass burning episode (BB), pollution episode (POL) and clean episode (CLE). Here INT, CS and EXT denotes homogeneously internal, core-shell internal, and external mixing state models, respectively. The shaded areas show the standard deviations of the calculated SSA obtained from the Monte Carlo simulation. The average measured SSA at dry condition is marked as squares in the left side, with the errorbars denotes the standard deviation of the time series.

Figure 6 displays the results of the simulation. The shaded areas show the standard deviations of the calculated SSA obtained from the Monte Carlo approach. It can be seen that with the same aerosol mixing state, the differences between the SSA for the three episodes are significant. At RH of 50%, the difference between SSA in pollution episode and clean episode can be up to 0.8. Although the differences decrease with increasing RH, they are still higher than the standard deviation of SSA. It means that the variation of SSA in different pollution conditions needs to be considered in the evaluation of aerosol direct radiative effects in the NCP.

It also can be seen in Fig. 6 that SSA is quite sensitive on the mixing state of BC, especially at low-RH condition. The differences between SSA for mixing state of INT and EXT range from about 0.6 to 0.9 for the three episodes, at RH of 50%. This difference decreases with the increase of RH, and is around 0.3 at RH of 95%. However, the SSA calculated based on mixing state of INT and CS are very similar at whole RH range in Fig. 6.

The average measured SSA at dry state during the three episodes are also shown in the left side of Fig. 6. The error bar shows the temporal variability of the measurements. It can be found that for all the three episodes, the measured SSA are very close to the calculated ones with assumed mixing states of INT and CS, but much lower than the SSA with assumed mixing state of EXT. The conceptual model of CS mixing is usually suggested for calculation of aerosol radiative properties (Jacobson, 2001; Chandra et al., 2004; Katrinak et al., 1992, 1993; Ma et al., 2012). Our calculation indicates that both CS and INT model are appropriate for the calculation of ambient SSA in the NCP. And the SSA will be significantly over-predicted if EXT mixing is assumed.

4. Summary and Conclusions

546 Atmospheric aerosol particle physical and chemical properties were measured at a regional

547 site in the North China Plain in the frame of the CAREBeijing-NCP project in summer 2014.

548 The primary focus of this case study was to determine the mixing state of particles originating

549 from biomass burning and to find how the particle mixing state influences aerosol optical

550 properties.

551 During the biomass burning episode defined with high concentration of anhydrosugar

552 levoglucosan and water soluble potassium, the probability density function of shrink factor

553 showed a different shape compared with other selected episodes. The reduction in highly

554 volatile and non-volatile particle number fractions together with a simultaneous increase in

555 semi-volatile particle number fraction have shaped the shrink factor probability density

556 function so that the maximum of was found at shrink factor of 0.6 with a number fraction of

557 semi-volatile particles reaching 70%. The non-volatile particle number fraction during the

558 biomass burning episode decreased to 3% and was the lowest measured value compared to all

559 other predefined episodes. Particle volume fraction remaining increased from approximately

560 25% to 40%, while in the clean and non-biomass burning related episodes the volume fraction

561 remaining varied around 20%. In terms of hygroscopicity, particles originating from biomass

562 burning showed a similar PDF of hygroscopicity parameter k as that observed in the clean

563 episode, but largely differed from the non-biomass burning related pollution episode during

564 which accumulation mode particles showed a higher hygroscopicity.

565 To evaluate the impact of biomass burning on aerosol single scattering albedo, SSA at

566 different RHs was simulated using the measured aerosol physical-chemical properties. And

567 Monte Carlo approach was used to estimate the uncertainty of the calculation. It was found

568 that the differences between the calculated SSA for biomass burning, clean and pollution

569 episodes are significant, meaning that the variation of SSA in different pollution conditions

570 needs to be considered in the evaluation of aerosol direct radiative effects in the NCP. We

571 also found that SSA is quite sensitive on the mixing state of BC, especially at low-RH

572 condition. The differences between SSA for internal and external mixing range from about

573 0.6 to 0.9 at RH of 50%. The simulated SSA was also compared with the measured values.

574 For all the three predefined episodes, the measured SSA are very close to the calculated ones

575 with assumed mixing states of homogeneously internal and core-shell internal mixing,

576 indicating that both homogeneously internal and core-shell internal models are appropriate for

577 the calculation of ambient SSA in the NCP.

580 Acknowledgment

582 This research is supported by the projects Sino German Science center (No. GZ663) and

583 National Basic Research Program of China (Nos. 2013CB955801, 2013CB228503), and the

584 National Natural Science Foundation of China (Nos. 21190052, 41305030, 41121004,

585 41175030). Travelling costs for CAREBeijing-NCP 2014 were partly funded by the EU

586 project AMIS 295132.


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• Biomass burning aerosol particles are moderately volatile and mixed internally.

• Particle hygroscopicity did not change during biomass burning.

• Single scattering albedo is sensitive to particle mixing state especially during biomass burning.