Kangning Li1,2, XiaoFei Wang1, Xiaohui Lu1, Hong Chen1, Xin Yang This email address is being protected from spambots. You need JavaScript enabled to view it.1,3 

1 Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
2 Ningxia Key Laboratory of Intelligent Sensing for the Desert Information, School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
3 School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China


Received: December 21, 2020
Revised: April 7, 2021
Accepted: May 20, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.200655  


Cite this article:

Li, K., Wang, X., Lu, X., Chen, H., Yang, X. (2021). Effect of Pollution Level on Size Distributions and Mixing State of Ambient Black Carbon Particles in an Urban Area during Wintertime. Aerosol Air Qual. Res. 21, 200655. https://doi.org/10.4209/aaqr.200655


HIGHLIGHTS

  • BC particles in polluted and clean environments of Shanghai were characterized.
  • The size distribution of internally mixed BC particles was bimodal.
  • BC particles from East China Sea have large core and thin coating thickness.
 

ABSTRACT


We deployed a Single Particle Soot Photometer (SP2) and Single Particle Aerosol Mass Spectrometer (SPAMS) to investigate the size distribution and mixing state of ambient black carbon (BC) particles in Shanghai during the winter of 2017. The mixing state of the particles changed drastically under different meteorological conditions, and higher concentrations often occurred during haze episodes. The BC particles existed in two size modes: the condensation mode, which mainly consisted of fresh traffic-emitted particles, and the droplet mode, which encompassed heavily aged traffic-emitted particles, biomass burning particles and heavy-duty diesel engine emission particles. Whereas the heavily aged traffic-emitted particles exhibited relatively small BC cores (60–80 nm in diameter) surrounded by a thick coating (100–160 nm), the biomass burning particles displayed slightly larger cores (80–130 nm) covered by an even thicker layer (160–300 nm). However, we observed a large number of particles with large cores (150–200 nm) but a relatively thin coating (40–80 nm) on clean days. Trajectory analyses of the air masses showed that they usually originated in local areas or on the North China Plain during haze episodes but mostly migrated from the East China Sea during clean episodes. Interestingly, the BC particles in the clean air masses contained V more frequently and generated intense mass spectrum signals for Ca+, suggesting that these particles probably arose from heavy machinery emissions near ports. Our real-time single particle data indicates that the sources of BC aerosol can be easily identified by measuring the size distribution and mixing state of the particles.


Keywords: Black carbon, Size distributions, Mixing state, Source identification


1 INTRODUCTION


BC aerosols, one of the strongest light-absorbing components in the atmosphere and a major contributor to positive radiative forcing, are produced by incomplete combustion of fossil fuels or biomass (Jayne et al., 2000; Bond et al., 2013). BC is chemically inert in the atmosphere and is mainly in the form of accumulation mode ambient aerosol particle with a diameter range between 0.01 and 1.0 µm (Bond et al., 2013). BC aerosols act as a carrier for many condensable species through physical absorption and heterogeneous chemical reactions on its surface, which can finally modify its morphology, chemical composition and hygroscopicity (Zhang and Zhang, 2005). The presence of non-BC material, such as sulfate, nitrate or organic materials, is thought to increase the light absorption properties of BC particles through “lensing effect” (Wang et al., 2014; Liu et al., 2015; Xie et al., 2019a, b). Moreover, increases in the size and hydrophilicity of BC particles can enhance their removal rate by precipitation scavenging (Liu et al., 2013). These effects have important implications for understanding of radiative forcing and global climate. BC aerosols usually have an atmospheric lifetime of approximately one week, which is sufficient to allow them to be long-range-transported to other regions (Liu et al., 2011; Wang et al., 2015; Targino and Krecl, 2016). Moreover, direct inhalation of BC particles causes vascular, cardiopulmonary, and respiratory diseases for human (Janssen et al., 2011; Heal et al., 2012). The presence of BC particle in the air also reduce visibility (Zhou et al., 2012; Wang et al., 2013a). Thus, BC is considered as a major air pollutant.

The mixing state of atmospheric BC aerosol is tightly linked to its sources, i.e., they can be emitted as pure BC or along with other inorganic species depending on their sources (Chirico et al., 2010; Heringa et al., 2011). Also, the size distribution and mixing state of ambient BC are very complex and constantly changing in the atmosphere. Atmospheric BC particles often consist of aggregates of small primary spherules 15–60 nm in diameter (Alexander et al., 2008; Zhang et al., 2008). They appear to be chain agglomerates after being freshly emitted from the combustion sources, and they are quickly coated by other aerosol components in the atmosphere, leading to the collapse of the chain agglomerates to more compact core-shell structures (Zhang et al., 2008). Change in the morphology of BC due to a thin coating has a competition effect between light absorption enhancement and reduction, resulting in less overall variation in the absorption efficiency (Wang et al., 2013c; Peng et al., 2016). In contrast, a thicker coating of the scattering shell enclosing the compact BC cores enhances the light absorption of BC by the lensing effect (Schnaiter et al., 2005; Khalizov et al., 2009; Shiraiwa et al., 2010; Peng et al., 2016).

Recently, the Single Particle Soot Photometer (SP2) can measure the mass and size of the refractory BC in high time resolution (Stephens et al., 2003; Schwarz et al., 2006). The mixing state of BC particles can also be derived from the SP2 data (Gao et al., 2007; Moteki and Kondo, 2007; Laborde et al., 2012). The sizes and mixing states of BC particles have been studied only in a few regions in China using SP2 (Huang et al., 2012; Wang et al., 2014; Wang et al., 2015; Wu et al., 2016). Liu et al. (2019) studied the size-resolved mixing state of BC-containing particles by an SP2 and their mass spectra was measured by a Soot Particle Aerosol Mass Spectrometer (SP-AMS). They compared two online source apportionment methods using simultaneous measurements made by the SP2, and the chemical approach using the positive matrix factorization (PMF) of mass spectra from the SP-AMS for the first time (Liu et al., 2019). Ding et al. (2019) reported comprehensive vertical profiles of BC size-related properties over the Beijing area and continental Europe. The results show that BC coatings were positively correlated with the pollution level). Some investigations on BC particles during different periods show the important role of chemical ageing to the pollution of BC particles in urban Beijing during wintertime (Wang et al., 2019). At the Tibetan Plateau, the BC was overall thickly coated and the coating species were predominantly secondarily formed by photochemical reactions (Wang et al., 2017). The size-resolved chemical components of the BC core and associated coatings were measured by a SP-AMS in suburban Nanjing, and the result shown that increased inorganic fraction and more oxidized organic coatings with thicker coatings, which modified the growth factor besides the determinant of particle size (Wu et al., 2019). However, SP-AMS is a mass-based instrument but is short in quantifying the composition of single particle therefore mixing state.

As a highly complementary instrument to SP2, SPAMS also can detect the chemical properties of BC particles. Combining these two instruments would provide more comprehensive information about chemical and physical properties of BC particles, which can provide us an improved understanding of their sources and evolution processes. Influenced by the local pollutant emissions and long-range transport from other heavily polluted areas, the physicochemical properties of ambient BC aerosols in urban Shanghai are highly varied. In this study, the size distribution and mixing state of BC were analyzed where the observation site is near the port with increased emissions by various heavy machinery in urban Shanghai based on the measurements combining SP2 and SPAMS during a wintertime in 2017 when haze occurred frequently. The time variations in the BC core size and coating thickness were also investigated, accompanied by an analysis of its relation with its potential source contributions. In the present study, source identification of BC aerosols has been studied based on the measured BC size distribution and their mixing state. Noticeably, our previous study by Gong et al. (2016) investigated BC particles in a sampling site far from the port and its surrounding industrial area using SP2 and SPAMS during heavily polluted days. We compared the results from this study to Gong et al. (2016) and showed how fresh industrial emissions affected BC particle mixing states.


2 METHODS


In situ measurements of ambient BC aerosol were conducted from October 25 to November 3, 2017, using an SP2 (Droplet Measurement Technology, Inc., Boulder, CO, USA) (Stephens et al., 2003; Baumgardner et al., 2004) and a SPAMS (Hexin Analytical Instrument Co., Ltd., Guangdong, China). The sampling site was located on the sixth floor of Environmental Science building on the Jiangwan campus of Fudan University (121.5°E, 31.3°N, approximately 18 m above ground level). The site is surrounded by schools and residential areas. To the east is the ferry terminal station (approximately 500 m) while to the south is a moderately traveled four-lane road. There are no other nearby major industrial sources except the port area.

The 60-min-averaged BC number concentration was measured with an SP2. Ambient air was drawn into the instrument at a low flow rate of 30 cm3 min−1 with a 2.5 µm cut-point inlet cyclone. We only saved data for every hundredth particle in order to extend the sampling time without generating excessively large data sets. A SPAMS instrument was deployed simultaneously with SP2 to detect chemical composition of BC-containing particles. Descriptions of the instruments and operational principles have been described in our previous work (Gong et al., 2016).

 
3 RESULT AND DISCUSSION


 
3.1 Overview of the Sampling Conditions

Temporal profiles of SO2, CO, K+, PM2.5 and PM10 mass concentrations obtained at an air quality monitoring site, Shanghai Environmental Monitoring Center (Yangpu Site), which is close to our sampling site (http://www.semc.com.cn/aqi/home/Index.aspx), BC number concentration and BC number fraction (NF) measured by SP2 during the study are shown in Fig. 1. The CO concentration showed two major peaks, whose values reached 2.66 mg m−3 at 09:00 local time (LT) on November 3, and 1.16 mg m−3 at 08:00 LT on October 29. SO2 has only one significant peak on November 3. The CO concentration has the similar trend with BC number concentration on October 29 and November 3. The CO concentration does not peak on October 27 and 31, while the BC concentration peaks. The inconsistence of the temporal trends of BC and CO could be due to the 4 km distance between our sampling location and the CO monitoring site.

Fig. 1. Temporal profiles of gaseous pollutants (SO2 and CO), PM2.5 and PM10 mass concentrations, BC number concentration and BC number fraction (NF) with 60 min resolution. Cases 1–5 represent the five high BC concentration events during this study.Fig. 1. Temporal profiles of gaseous pollutants (SO2 and CO), PM2.5 and PM10 mass concentrations, BC number concentration and BC number fraction (NF) with 60 min resolution. Cases 1–5 represent the five high BC concentration events during this study.

The PM2.5 concentration also showed two peaks on October 29 and November 3, which maximum value reached 170 µg m−3 (at 11:00 LT on October 29) and 173 µg m−3 (at 09:00 LT on November 3). The PM10 concentration had the similar trend as PM2.5. It varied from 21 to 218 µg m−3, with an average of 51.94 µg m−3.

 
3.2 BC Size Distribution and Number Concentration Measured by SP2

Fig. 2 shows the number and mass size distribution of BC core particles during the entire sampling period. The number size distribution ranges from 60 to 300 nm and the peak was around 60 nm. The measured number concentrations drastically dropped below 60 nm due to the low BC detection efficiency below this particle size (Gong et al., 2016). The BC core mass size distribution had a peak around 180 nm, and the majority of the BC core mass was distributed between 70 and 500 nm.

Fig. 2. BC core number size distribution and mass size distribution from SP2 measurement.Fig. 2. BC core number size distribution and mass size distribution from SP2 measurement.

As shown in Fig. 1, the BC number concentration varied from 53 particles cm−3 at 00:00 LT on October 25 to 1147 particles cm−3 at 16:00 LT on November 3, with an average of 267 particles cm3. All of the values above were based on SP2 measurement. It is found that both the BC number concentration and the PM2.5 reached their minimum as well as their maximum at the same time. To a large extent, BC number concentration is positively correlated with PM2.5 mass concentration. There are several exceptions. For example, PM2.5 concentration reached 170 µg m−3 at 11:00 LT on October 29, while the BC number concentration was low (298 particles cm−3). On October 27, high BC number concentration was detected while PM2.5 was low. The details are explained in the following sections.

 
3.3. BC Particle Classification by SPAMS

Elemental carbon (EC) peaks has been considered a distinct marker for BC aerosols. Hence, using Cn+/− (n = 1, 2, 3…) as the marker, a total of 230,105 BC-containing particles were identified, accounting for about 53.91% of detected particles with mass spectra. Then, using ART-2a algorithm (Song et al., 1999), BC-containing particles were classified into five groups based on their mass spectral characteristics, namely Pure EC, NaKEC, ECOC, KEC, and Other. Their number fractions for each group are shown in Table 1. The average mass spectral patterns of each group are shown in Fig. S1.

Table 1. Names, numbers and fractions of the five types of BC-containing particles detected by the SPAMS instrument.

Pure EC particles only presented BC fragment ions (Cn+ and Cn) in both positive and negative ion mass spectra. There was no or very small signals of secondary species, such as sulfate or nitrate, indicating that pure EC type were freshly emitted.

NaKEC particles exhibited strong signals for BC fraction in both positive and negative ion mass spectra; in addition, potassium (+39K+) and sodium (+23Na+) in positive ion mass spectra and nitrate (−46NO2 and −62NO3) and sulfate (−97HSO4) in the negative ion mass spectra were also exhibited. This group was similar to diesel vehicle emissions (Li et al., 2013; Li et al., 2017).

Mass spectra of ECOC (EC mixed with Organic Carbon) particles showed that BC internally mixed with organic carbon (OC) signals, such as +37C3H+, +43CH3CO+, +50C4H2+, +51C4H3+, +61CH3C(OH) = OH+, +62(CH3)2NHOH+, and a small signal +23Na+ in the positive ion mass spectra, along with BC fragment ions (Cn+). There was a very high signals for sulfate (−97HSO4) and nitrate (−46NO2 and −62NO3) in the negative ion spectra, suggesting they were aged BC particles that had been aged in the atmosphere. BC particles with various intensities of OC, nitrate and sulfate signals were detected in ambient aerosols by many other ATOFMS (Aerosol Time-of-Flight Mass Spectrometer) studies (Dall’Osto and Harrison, 2006; Moffet et al., 2008; Ault et al., 2009), and this particle type is usually assigned to aged traffic emissions (Healy et al., 2012).

KEC particles were characterized by an intense +39K+ signal in the positive ion mass spectra and relatively strong signals for −26CN and −42CNO in the negative ion mass spectra. Significant fragments of levoglucosan, −71C3H3O2 and −73C3H5O2 were also observed. Typical BC fragments Cn appeared in the negative ion mass spectra. High signals at −97HSO4, −46NO2 and −62NO3 were also observed, suggesting a significant accumulation of secondary inorganic materials can coat on BC particles in a short time (Reid et al., 2005; Leskinen et al., 2007). This type of mass spectra was previously observed in several field studies and assigned to either biomass burning or coal combustion or both (Moffet et al., 2008; Bi et al., 2011; Healy et al., 2012; Wang et al., 2013b; Gong et al., 2016).

Other particles, which were not grouped into any of the previous four types, accounted for 5.08% in number, including ash, dust, heavy metal and sea salt particles, and they were not the focus in this article. The average mass spectra of this type are displayed in Fig. S1(e).

Noticeably, the particles analyzed by SPAMS are in the size range of 200–2000 nm, and the detection efficiency decreases sharply below 400 nm and above 1200 nm (Li et al., 2011). The majority of the ambient BC particles are smaller than 200 nm (Kondo et al., 2006); thus, SPAMS has low detection efficiency for pure BC particles.


3.4 Mixing State and Size Distribution of Internally Mixed BC Particles


3.4.1 Temporal variations of internally mixed BC particles

A comparison of the internally mixed BC particles number concentration between SP2 and SPAMS is provided in Fig. 3. Considering the different cut-off diameters of SP2 (120 nm < particle diameter [Dp] < 600 nm) and SPAMS (200 nm < vacuum aerodynamic diameter [Dva] < 1200 nm), we also plotted the temporal variations of BC-containing particles with vacuum aerodynamic particle size in the size range from 200 to 600 nm.

Fig. 3. Temporal profiles of different particles with 60 min resolution. (a) BC number concentration measured by SP2; (b) 200–600 nm BC-containing particles measured by SPAMS; (c) total particle number measured by SPAMS; (d) the number fraction of V-containing BC particles with size in the range of 200–600 nm (ƒV); (e) the number fraction of Pb-containing BC particles with size in the range of 200–600 nm (ƒPb); (f) Pb-containing particles number chosen from BC-containing particles with size in the range of 200–600 nm.Fig. 3. Temporal profiles of different particles with 60 min resolution. (a) BC number concentration measured by SP2; (b) 200–600 nm BC-containing particles measured by SPAMS; (c) total particle number measured by SPAMS; (d) the number fraction of V-containing BC particles with size in the range of 200–600 nm (ƒV); (e) the number fraction of Pb-containing BC particles with size in the range of 200–600 nm (ƒPb); (f) Pb-containing particles number chosen from BC-containing particles with size in the range of 200–600 nm.

Vanadium (V) signal, a reliable marker for heavy fuel oil combustion (Ault et al., 2009; Furutani et al., 2011), was present in a significant portion of BC particles mass spectra. One previous study suggests that in Shanghai heavy fuel oil is mainly used by marine vessels passing by, entering or leaving the ports (Liu et al., 2017). V-containing particles are typically emitted by ships or oil 

refineries (Ault et al., 2009; Healy et al., 2009). The number fraction of V-containing BC particles with size in the range of 200–600 nm (ƒV) was calculated (Fig. 3(d)). Elements that engine wear usually contains (e.g., Cr, Fe, Cu and Pb) are frequently found in the emissions from the tailpipes of motor vehicles (Lim et al., 2007). Temporal variation of the number fraction of Pb-containing BC particles with size in the range of 200–600 nm (ƒPb) was also plotted in Fig. 3(e).

Temporal variations of BC number concentration detected by these two instruments changed rapidly with a complex pattern, as shown in Fig. 3. For the sake of illustration, we defined the periods from 00:00 to 12:00 on October 25, 00:00 to 12:00 on October 27, 10:00 to 20:00 on October 29, 00:00 to 12:00 on October 31, 00:00 to 12:00 on November 3, as Case 1, Case 2, Case 3, Case 4, and Case 5, respectively. High-resolution temporal variations of BC number concentration shows several peaks of which formation might be due to the typical fresh plume or municipal air pollution. We will discuss this hypothesis in the following paragraphs. To better describe this phenomenon, we colored the three cases (Case 1, 2 and 4) with blue background and the other two cases (Case 3 and 5) with yellow.

For Case 1, 2 and 4, as shown in Fig. 1, both the BC number concentration and BC number fraction detected by SP2 increased sharply, while the PM2.5 in Shanghai was maintained at lower values (PM2.5 < 35 µg m−3). The ƒV as well as ƒPb was relatively high during this period compared with Case 3 and 5, indicating that the enhanced BC pollution level was likely from ship or heavy-duty machinery emissions near the ports. ƒV displayed strong East China Sea air mass dependences (Fig. 9), consistent with the distribution of ƒPb, as well as the number fraction of pure EC shown in Fig. 5(e). The port area is the only industrial area close to the sampling location. All of the above suggests that the ports’ emissions contributed a significant fraction of BC aerosol measured at the sampling site.

Haze is defined as a weather phenomenon leading to atmospheric visibility less than 10 km due to the suspended solid or liquid particles, smoke and vapor in the atmosphere (Tan et al., 2009; Lee et al., 2014). Former studies indicated that high concentration of PM2.5 is the primary factor causing haze formation (Wang et al., 2012; Cheng et al., 2013; Li and Zhang, 2014). In this article we define Case 3 and Case 5 as haze period: The PM2.5 rapidly increased to extremely high values (PM2.5 = 170 µg m−3 and 173 µg m−3, respectively), as well as the number concentration of particles with size from 200 to 2000 nm measured by SPAMS. The BC number concentration detected by SP2 peaks during Case 3 and 5. However, BC-containing particle number with size in the range of 200–600 nm measured by SPAMS was relatively low. The ƒV and ƒPb was also low during these two periods. One likely reason is that, during Case 3 and 5, the sizes of many BC particles detected by SP2 were smaller than the lower end of the SPAMS detection range.

Fig. 4 shows the temporal variations of size distributions of BC particles and cores as well as their coating thickness with 60 min resolution. It is found that the BC particle sizes (from SPAMS) were relatively large on October 29 (Case 3), but their BC core sizes were smaller and their coating were thicker, compared to other sampling time. Note large amount of BC particles with size from 600 to 800 nm were detected by SPAMS while only a small amount of BC particles at these sizes were detected by SP2, which was due to the decreased detection efficiency for large particle size in SP2 (Dp > 600 nm) (Schwarz et al., 2006; McMeeking et al., 2010).

Fig. 4. (a) Temporal variations of entire particle size distributions for BC-containing particles (200–1200 nm) detected by SPAMS with 60 min resolution. (b), (c), (d) and (e) shows the temporal variation of BC core diameter, coating thickness and particle size (detected by SP2) and number fraction of different types detected by SPAMS with 60 min time resolution, respectively.Fig. 4. (a) Temporal variations of entire particle size distributions for BC-containing particles (200–1200 nm) detected by SPAMS with 60 min resolution. (b), (c), (d) and (e) shows the temporal variation of BC core diameter, coating thickness and particle size (detected by SP2) and number fraction of different types detected by SPAMS with 60 min time resolution, respectively.

For Case 2, there were many BC particles with large core sizes (150–200 nm) and thin coating thickness (40–80 nm). Temporal variation of number concentration of such particles was plotted in Fig. 6(d). This type of BC particles was also present on October 31 (Case 4), although the PM2.5 concentration was lower (PM2.5 < 35 µg m−3), indicating that regional pollution caused the increase of BC number concentration. Presumably, the air masses from the East China Sea carried such BC particles to the sampling site, leading to the fast increase of this type particles.

 
3.4.2 Size distribution and source identification of BC particles

The average size distribution of BC particles detected by SP2 was bimodal (Fig. 5(a)). Similar to our previous study, here, we used a Dp = 280 nm to separate the condensation mode particles (left side) and the droplet mode particles (right side). The separation of condensation mode (Dva = 200–550 nm) and droplet mode (Dva = 550–1200 nm) was confirmed by the SPAMS data (Fig. 5(b)). Here the SPAMS size distribution was based on the number fraction of BC-containing particles in all detected particles. Similar particle size distributions were also found in other studies in China (Huang and Yu, 2008; Zhang et al., 2014). Distinct chemical compositions were found between these two modes (Fig. 5(b)). EC and NaKEC particles exhibited higher number fractions in the condensation mode than in the droplet mode, while KEC and ECOC particles exhibited higher number fractions in the droplet mode. As shown in Fig. 5(c), the condensation mode mainly consisted of fresh traffic emissions with a small BC core (60–80 nm) and thin coating thickness (40–60 nm). The droplet mode mainly included biomass burning (BB) and deeply aged traffic-emitted BC particles, which were consistent to our previous study (Gong et al., 2016). There are a large amount of BC particles with large core (150–200 nm) and thin coating thickness (40–80 nm) in droplet mode, which can be attributed to the distinct emission sources and meteorological conditions.

Fig. 5. (a) Dp number size distribution histogram for the SP2-detected BC particles; (b) Dva number fraction distribution of SPAMS-detected BC-containing particles color coded by the particle type; (c) BC particle number distribution as a function of BC core diameter and coating thickness (the condensation and droplet modes are separated by a solid black line).Fig. 5. (a) Dp number size distribution histogram for the SP2-detected BC particles; (b) Dva number fraction distribution of SPAMS-detected BC-containing particles color coded by the particle type; (c) BC particle number distribution as a function of BC core diameter and coating thickness (the condensation and droplet modes are separated by a solid black line).

Time series of hourly averaged particle number of EC and NaKEC with size in the range of 0–400 nm and the BC particles with small core (60–80 nm) and thin coating thickness (40–60 nm) were shown in Fig. 6(a). As shown in Fig. S2(a), a good correlation (R2 = 0.493) between them suggested that they might originate from the same source. EC and NaKEC are considered to be the tracers of traffic emission (Li et al., 2018). The diurnal pattern of this BC type detected by SP2 further confirmed its association with traffic activities, as it showed two significant peaks during morning and evening rush hours (Fig. S2(b)). Mass spectra of EC and NaKEC (0–400 nm) was presented in Fig. 8(a); it can be found that strong signals of BC fragment ions were present in both positive and negative ion mass spectra. In addition, sodium (+23Na+) and potassium (+39K+) in positive ion mass spectra also exhibited. Very small signals of secondary species, such as nitrate (−46NO2 and −62NO3) and sulfate (−97HSO4) were present in the negative ion mass spectra. Therefore, BC particles with small core (60–80 nm) and CT (40–60 nm) were probably emitted by fresh traffic emission. 

Fig. 6. Temporal variation of different type particle number with 60 min resolution. (a) EC and NaKEC (0–400 nm) type particle compared with BC particle with core (60–80 nm) and CT (40–60 nm); (b) ECOC (0–700 nm) type particle compared with BC particle with core (60–80 nm) and CT (100–160 nm); (c) KEC (0–1200 nm), BC particles with core (80–130 nm) and CT (160–300 nm), and K+ mass concentration measured by MARGA; (d) EC and NaKEC (400–800 nm) type particle number compared with BC particle number with core (150–200 nm) and CT (40–80 nm).  Fig. 6. Temporal variation of different type particle number with 60 min resolution. (a) EC and NaKEC (0–400 nm) type particle compared with BC particle with core (60–80 nm) and CT (40–60 nm); (b) ECOC (0–700 nm) type particle compared with BC particle with core (60–80 nm) and CT (100–160 nm); (c) KEC (0–1200 nm), BC particles with core (80–130 nm) and CT (160–300 nm), and K+ mass concentration measured by MARGA; (d) EC and NaKEC (400–800 nm) type particle number compared with BC particle number with core (150–200 nm) and CT (40–80 nm).
 

The highly aged traffic emissions had small core size (60–80 nm) and thick coating (110–160 nm in thickness). Traffic-emitted BC particles grew rapidly to the droplet mode, indicating that the ageing of traffic-emitted BC particles was fast during the heavy air pollution episode, which has been reported by our previous study (Gong et al., 2016). Time series of this type BC and ECOC (0–700 nm) measured by SPAMS was shown in Fig. 6(b), and the good correlation was shown in Fig. S3 (R2 = 0.535). Higher proportion of highly aged BC particles in haze days (Case 3 and Case 5) than clean days (Case 1, Case 2 and Case 4). The maximum particle number occurred in Case 5 that the PM2.5 reached 173 µg m−3 (at 09:00 LT on November 3). Temporal variation of relative coating thickness (RCT; particle diameter/BC core size [Dp/Dc]) of highly aged BC particles (Dc = 60–80 nm and CT = 110–160 nm) was shown in Fig. 7. It is found that the value of the RCT was between 3 and 8, indicating that such BC particles were highly aged in the polluted atmosphere. It is worth noting that the RCT was also positively correlated with the total BC particle number detected by SPAMS in Fig. 4(a).

Fig. 7. Temporal variation of the relative coating thickness (RCT) distribution of highly aged traffic-emitted BC particles detected by SP2 with resolutions of 0.2 RCT and 60 min.Fig. 7. Temporal variation of the relative coating thickness (RCT) distribution of highly aged traffic-emitted BC particles detected by SP2 with resolutions of 0.2 RCT and 60 min.

The previous studies show that fresh BB particles are usually coated by a thick layer of organics (Schwarz et al., 2008; Sahu et al., 2012; Liu et al., 2014; Gong et al., 2016). Here, SP2-detected particles with larger core sizes (80–130 nm) and thicker coating (160–300 nm) were selected and compared with the number concentration of KEC from SPAMS and K+ mass concentration from monitor for aerosols and gases in air (MARGA), as shown in Fig. 6(c). Significant correlations were shown in Fig. S4 (R2 = 0.624 and R2 = 0.831, respectively), supporting the conclusion that the BC particles with larger core size and thicker coating were from biomass burning.

Temporal variation of BC particle with core size (150–200 nm) and coating thickness (40–80 nm) number concentration was shown in Fig. 6(d), which exhibits four major peaks, corresponding to BC peak events. Three BC peak events (Case 1, 2 and 4) occurred in clean days with relatively good air quality (PM2.5 < 35 µg m−3). Obviously, higher BC concentration (Case 2 on October 27) are observed during clean days compared to haze periods (Case 3 on October 29), although lower PM2.5 concentration is observed, which is probably indicative of the BC aerosol originating from the nearby source regions. Time series of this BC particle type, and EC and NaKEC (400–800 nm) type number concentration from SPAMS were shown in Fig. 6(d). A correlation was shown in Fig. S5 (R2 = 0.442). Mass spectra extracted from Case 2 was shown in Fig. 8(b), characterized by intense +40Ca+ and +56CaO+ signals, as well as fragments of vanadium (+51V+) in the positive ion mass spectra and relatively strong signals for −46NO2 and −62NO3 in the negative ion mass spectra. However, there was very small signals of sulfate, indicating that this type of BC particle had not undergone much ageing in the atmosphere, thus corresponding to thinner coating thickness, suggesting that the BC particles with larger core size (150–200 nm) and thinner coating thickness (40–80 nm) were likely to originate from nearby source regions without undergoing long-range transport and ageing. All of the above mass spectra characteristics meet the criteria for the identification of heavy machinery because calcium containing soot is a well-established tracer for heavy-duty diesel vehicle (Li et al., 2013).

Fig. 8. Averaged mass spectra of different types of BC particles. Major peaks are labeled with the most probable assignments. (a) EC and NaKEC (0–400 nm); (b) EC and NaKEC (400–800 nm).Fig. 8. Averaged mass spectra of different types of BC particles. Major peaks are labeled with the most probable assignments. (a) EC and NaKEC (0–400 nm); (b) EC and NaKEC (400–800 nm).

Source regions responsible for BC particle peak events can be deduced by performing an air mass backward trajectory analysis, with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Godlowska et al., 2015). The BC number concentration in the sampling site was influenced by these air masses and the representative trajectories are shown in Fig. 9. Most trajectories are classified into three different types with pollution originating from (1) local areas (2) long-range transport from the North China Plain (NCP) and (3) transport from East China Sea.

Fig. 9. Representative HYSPLIT backward trajectories with the air masses from local areas, North China Plain (NCP), and East China Sea. The five figures represent Case 1, Case 2, Case 3, Case 4 and Case 5 in turn.
Fig. 9.
 Representative HYSPLIT backward trajectories with the air masses from local areas, North China Plain (NCP), and East China Sea. The five figures represent Case 1, Case 2, Case 3, Case 4 and Case 5 in turn.

As shown in Figs. 9(a), 9(b) and 9(d), all of the three cases (Case 1, Case 2 and Case 4) were influenced by air mass from the East China Sea. From the Fig. 2, it is found that the number fractions of V-containing and Pb-containing BC particles with aerodynamic particle size in the range from 200 to 600 nm were slightly higher than other two cases (Case 3 and Case 5). And temporal variation of number concentration of BC particle with core size (150–200 nm) and coating thickness (40–80 nm) have peaks in these three cases (Case 1, Case 2 and Case 4). This kind of BC is carried by the air masses from the East China Sea, carrying small amounts of V-containing particles, which indicates that this type of BC emitted by ship or the heavy-duty machinery nearby the port.

As shown in Fig. 9(c), Case 3 is impacted by long-range transports from NCP, and BC particles number concentration measured by SPAMS were extremely high while BC number concentration detected by SP2 was low (Fig. 3). Most of BC particles during Case 3 were large (Dva > 600 nm) while the number fraction of pure EC was relatively low (Fig. 4(e)), indicating the ageing process during the long-range transport grew BC particles to larger sizes. In contrast, the BC particle peak during Case 5 was caused by local pollution. BC particles with large core size and thin coating thickness were likely to be from the ports near our sampling site.

 
4 CONCLUSIONS


In this study, we characterized the ambient BC particles during both clean and polluted periods in wintertime Shanghai by analyzing their size distribution and mixing state with an SP2 and a SPAMS, which can detect the physical and chemical properties of BC aerosol at the resolution of a single particle. The SP2-measured number concentration averaged 268 particles cm−3 and peaked at 1147 particles cm−3 on November 3, 2017, at 16:00 LT. The number and mass size distributions of the BC cores fell approximately in the ranges of 60–300 nm and 60–500 nm with peaks around 60 nm and 180 nm, respectively.

The internally mixed BC particles displayed a bimodal size distribution: The condensation mode, which was distinguished by a small core (60–80 nm) and a thin coating (40–60 nm), primarily consisted of fresh traffic emissions, whereas the droplet mode comprised biomass burning emissions, heavily aged traffic emissions and heavy-duty diesel emissions (from engines or ships). The droplet mode particles originating from traffic and biomass burning exhibited a small core (60–80 nm) with a thick coating (100–160 nm) and a medium-sized core (80–130 nm) with an extremely thick coating (160–300 nm), respectively. Interestingly, BC particles arriving in air masses from the East China Sea contained a large core (150–200 nm) surrounded by a thin layer (40–80 nm), most likely identifying heavy machinery near ports as the source.

 
ACKNOWLEDGMENTS


This work was supported by the Key Research and Development Program of Ningxia Province in China (2020BEB04003), and Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention Program (FDLAP19002).


REFERENCES


  1. Alexander, D.T.L., Crozier, P.A., Anderson, J.R. (2008). Brown carbon spheres in East Asian outflow and their optical properties. Science 321, 833–836. https://doi.org/10.1126/science.1155296

  2. Ault, A.P., Moore, M.J., Furutani, H., Prather, K.A. (2009). Impact of emissions from the Los Angeles port region on San Diego air quality during regional transport events. Environ. Sci. Technol. 43, 3500–3506. https://doi.org/10.1021/es8018918

  3. Baumgardner, D., Kok, G., Raga, G. (2004). Warming of the Arctic lower stratosphere by light absorbing particles. Geophys. Res. Lett. 31, L06117. https://doi.org/10.1029/2003gl018883

  4. Bi, X., Zhang, G., Li, L., Wang, X., Li, M., Sheng, G., Fu, J., Zhou, Z. (2011). Mixing state of biomass burning particles by single particle aerosol mass spectrometer in the urban area of PRD, China. Atmos. Environ. 45, 3447–3453. https://doi.org/10.1016/j.atmosenv.2011.03.034

  5. Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., DeAngelo, B.J., Flanner, M.G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P.K., Sarofim, M.C., Schultz, M.G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., et al. (2013). Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. 118, 5380–5552. https://doi.org/10.1002/jgrd.50171

  6. Cheng, Z., Wang, S., Jiang, J., Fu, Q., Chen, C., Xu, B., Yu, J., Fu, X., Hao, J. (2013). Long-term trend of haze pollution and impact of particulate matter in the Yangtze River Delta, China. Environ. Pollut. 182, 101–110. https://doi.org/10.1016/j.envpol.2013.06.043

  7. Chirico, R., DeCarlo, P.F., Heringa, M.F., Tritscher, T., Richter, R., Prevot, A.S.H., Dommen, J., Weingartner, E., Wehrle, G., Gysel, M., Laborde, M., Baltensperger, U. (2010). Impact of aftertreatment devices on primary emissions and secondary organic aerosol formation potential from in-use diesel vehicles: Results from smog chamber experiments. Atmos. Chem. Phys. 10, 11545–11563. https://doi.org/10.5194/acp-10-11545-2010

  8. Dall'Osto, M., Harrison, R.M. (2006). Chemical characterisation of single airborne particles in Athens (Greece) by ATOFMS. Atmos. Environ. 40, 7614–7631. https://doi.org/10.1016/j.atmosenv.2006.06.053

  9. Ding, S., Liu, D., Zhao, D., Hu, K., Tian, P., Zhou, W., Huang, M., Yang, Y., Wang, F., Sheng, J., Liu, Q., Kong, S., Cui, P., Huang, Y., He, H., Coe, H., Ding, D. (2019). Size-related physical properties of black carbon in the lower atmosphere over Beijing and Europe. Environ. Sci. Technol. 53, 11112–11121. https://doi.org/10.1021/acs.est.9b03722

  10. Furutani, H., Jung, J., Miura, K., Takami, A., Kato, S., Kajii, Y., Uematsu, M. (2011). Single-particle chemical characterization and source apportionment of iron-containing atmospheric aerosols in Asian outflow. J. Geophys. Res. 116, D18204. https://doi.org/10.1029/2011jd015867

  11. Gao, R.S., Schwarz, J.P., Kelly, K.K., Fahey, D.W., Watts, L.A., Thompson, T.L., Spackman, J.R., Slowik, J.G., Cross, E.S., Han, J.H., Davidovits, P., Onasch, T.B., Worsnop, D.R. (2007). A novel method for estimating light-scattering properties of soot aerosols using a modified single-particle soot photometer. Aerosol Sci. Technol. 41, 125–135. https://doi.org/10.1080/02786820601118398

  12. Godlowska, J., Hajto, M.J., Tomaszewska, A.M. (2015). Spatial analysis of air masses backward trajectories in order to identify distant sources of fine particulate matter emission. Arch. Environ. Prot. 41, 28–35. https://doi.org/10.1515/aep-2015-0015

  13. Gong, X., Zhang, C., Chen, H., Nizkorodov, S.A., Chen, J., Yang, X. (2016). Size distribution and mixing state of black carbon particles during a heavy air pollution episode in Shanghai. Atmos. Chem. Phys. 16, 5399–5411. https://doi.org/10.5194/acp-16-5399-2016

  14. Heal, M.R., Kumar, P., Harrison, R.M. (2012). Particles, air quality, policy and health. Chem. Soc. Rev. 41, 6606–6630. https://doi.org/10.1039/c2cs35076a

  15. Healy, R.M., O'Connor, I.P., Hellebust, S., Allanic, A., Sodeau, J.R., Wenger, J.C. (2009). Characterisation of single particles from in-port ship emissions. Atmos. Environ. 43, 6408–6414. https://doi.org/10.1016/j.atmosenv.2009.07.039

  16. Healy, R.M., Sciare, J., Poulain, L., Kamili, K., Merkel, M., Mueller, T., Wiedensohler, A., Eckhardt, S., Stohl, A., Sarda-Esteve, R., McGillicuddy, E., O'Connor, I.P., Sodeau, J.R., Wenger, J.C. (2012). Sources and mixing state of size-resolved elemental carbon particles in a European megacity: Paris. Atmos. Chem. Phys. 12, 1681–1700. https://doi.org/10.5194/acp-12-1681-2012

  17. Heringa, M.F., DeCarlo, P.F., Chirico, R., Tritscher, T., Dommen, J., Weingartner, E., Richter, R., Wehrle, G., Prevot, A.S.H., Baltensperger, U. (2011). Investigations of primary and secondary particulate matter of different wood combustion appliances with a high-resolution time-of-flight aerosol mass spectrometer. Atmos. Chem. Phys. 11, 5945–5957. https://doi.org/10.5194/acp-11-5945-2011

  18. Huang, X.F., Yu, J.Z. (2008). Size distributions of elemental carbon in the atmosphere of a coastal urban area in South China: Characteristics, evolution processes, and implications for the mixing state. Atmos. Chem. Phys. 8, 5843–5853. https://doi.org/10.5194/acp-8-5843-2008

  19. Huang, X.F., Sun, T.L., Zeng, L.W., Yu, G.H., Luan, S.J. (2012). Black carbon aerosol characterization in a coastal city in South China using a single particle soot photometer. Atmos. Environ. 51, 21–28. https://doi.org/10.1016/j.atmosenv.2012.01.056

  20. Janssen, N.A.H., Hoek, G., Simic-Lawson, M., Fischer, P., van Bree, L., ten Brink, H., Keuken, M., Atkinson, R.W., Anderson, H.R., Brunekreef, B., Cassee, F.R. (2011). Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5. Environ. Health Perspect. 119, 1691–1699. https://doi.org/10.1289/ehp.1003369

  21. Jayne, J.T., Leard, D.C., Zhang, X.F., Davidovits, P., Smith, K.A., Kolb, C.E., Worsnop, D.R. (2000). Development of an aerosol mass spectrometer for size and composition analysis of submicron particles. Aerosol Sci. Technol. 33, 49–70. https://doi.org/10.1080/027868200410840

  22. Khalizov, A.F., Xue, H., Wang, L., Zheng, J., Zhang, R. (2009). Enhanced light absorption and scattering by carbon soot aerosol internally mixed with sulfuric acid. J. Phys. Chem. A 113, 1066–1074. https://doi.org/10.1021/jp807531n

  23. Kondo, Y., Komazaki, Y., Miyazaki, Y., Moteki, N., Takegawa, N., Kodama, D., Deguchi, S., Nogami, M., Fukuda, M., Miyakawa, T., Morino, Y., Koike, M., Sakurai, H., Ehara, K. (2006). Temporal variations of elemental carbon in Tokyo. J. Geophys. Res. 111, D12205. https://doi.org/10.1029/2005jd006257

  24. Laborde, M., Mertes, P., Zieger, P., Dommen, J., Baltensperger, U., Gysel, M. (2012). Sensitivity of the Single Particle Soot Photometer to different black carbon types. Atmos. Meas. Tech. 5, 1031–1043. https://doi.org/10.5194/amt-5-1031-2012

  25. Lee, K.Y., Wong, C.K.C., Chuang, K.J., Bien, M.Y., Cao, J.J., Han, Y.M., Tian, L., Chang, C.C., Feng, P.H., Ho, K.F., Chuang, H.C. (2014). Methionine oxidation in albumin by fine haze particulate matter: An in vitro and in vivo study. J. Hazard. Mater. 274, 384–391. https://doi.org/10.1016/j.jhazmat.2014.04.029

  26. Leskinen, A.P., Jokiniemi, J.K., Lehtinen, K.E.J. (2007). Characterization of aging wood chip combustion aerosol in an environmental chamber. Atmos. Environ. 41, 3713–3721. https://doi.org/10.1016/j.atmosenv.2006.12.016

  27. Li, H., Zhang, Q., Zhang, Q., Chen, C., Wang, L., Wei, Z., Zhou, S., Parworth, C., Zheng, B., Canonaco, F., Prevot, A.S.H., Chen, P., Zhang, H., Wallington, T.J., He, K. (2017). Wintertime aerosol chemistry and haze evolution in an extremely polluted city of the North China Plain: Significant contribution from coal and biomass combustion. Atmos. Chem. Phys. 17, 4751–4768. https://doi.org/10.5194/acp-17-4751-2017

  28. Li, K., Ye, X., Pang, H., Lu, X., Chen, H., Wang, X., Yang X., Chen J., Chen Y. (2018). Temporal variations in the hygroscopicity and mixing state of black carbon aerosols in a polluted megacity area. Atmos. Chem. Phys. 18, 15201–15218. https://doi.org/10.5194/acp-18-15201-2018

  29. Li, L., Huang, Z., Dong, J., Li, M., Gao, W., Nian, H., Fu, Z., Zhang, G., Bi, X., Cheng, P., Zhou, Z. (2011). Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles. Int. J. Mass Spectrom. 303, 118–124. https://doi.org/10.1016/j.ijms.2011.01.017

  30. Li, L., Tan, G.B., Zhang, L., Fu, Z., Nian, H.-Q., Huang, Z.X., Zhou, Z., Li, M. (2013). Analysis of diesel exhaust particles using single particle aerosol mass spectrometry. Chin. J. Anal. Chem. 41, 1831–1836. https://doi.org/10.3724/sp.j.1096.2013.30545

  31. Li, M., Zhang, L. (2014). Haze in China: Current and future challenges. Environ. Pollut. 189, 85–86. https://doi.org/10.1016/j.envpol.2014.02.024

  32. Lim, M.C.H., Ayoko, G.A., Morawska, L., Ristovski, Z.D., Jayaratne, E.R. (2007). The effects of fuel characteristics and engine operating conditions on the elemental composition of emissions from heavy duty diesel buses. Fuel 86, 1831–1839. https://doi.org/10.1016/j.fuel.2007.11.025

  33. Liu, D., Allan, J., Whitehead, J., Young, D., Flynn, M., Coe, H., McFiggans, G., Fleming, Z.L., Bandy, B. (2013). Ambient black carbon particle hygroscopic properties controlled by mixing state and composition. Atmos. Chem. Phys. 13, 2015–2029. https://doi.org/10.5194/acp-13-2015-2013

  34. Liu, D., Allan, J.D., Young, D.E., Coe, H., Beddows, D., Fleming, Z.L., Flynn, M.J., Gallagher, M.W., Harrison, R.M., Lee, J., Prevot, A.S.H., Taylor, J.W., Yin, J., Williams, P.I., Zotter, P. (2014). Size distribution, mixing state and source apportionment of black carbon aerosol in London during wintertime. Atmos. Chem. Phys. 14, 10061–10084. https://doi.org/10.5194/acp-14-10061-2014

  35. Liu, D., Joshi, R., Wang, J., Yu, C., Allan, J.D., Coe, H., Flynn, M.J., Xie, C., Lee, J., Squires, F. (2019). Contrasting physical properties of black carbon in urban Beijing between winter and summer. Atmos. Chem. Phys. 19, 6749–6769. https://doi.org/10.5194/acp-19-6749-2019

  36. Liu, J., Fan, S., Horowitz, L.W., Levy, H., II (2011). Evaluation of factors controlling long-range transport of black carbon to the Arctic. J. Geophys. Res. 116, D04307. https://doi.org/10.1029/2010jd015145

  37. Liu, S., Aiken, A.C., Gorkowski, K., Dubey, M.K., Cappa, C.D., Williams, L.R., Herndon, S.C., Massoli, P., Fortner, E.C., Chhabra, P.S., Brooks, W.A., Onasch, T.B., Jayne, J.T., Worsnop, D.R., China, S., Sharma, N., Mazzoleni, C., Xu, L., Ng, N.L., Liu, D., et al. (2015). Enhanced light absorption by mixed source black and brown carbon particles in UK winter. Nat. Commun. 6, 8435. https://doi.org/10.1038/ncomms9435

  38. Liu, Z., Lu, X., Feng, J., Fan, Q., Zhang, Y., Yang, X. (2017). Influence of ship emissions on urban air quality: A comprehensive study using highly time-resolved online measurements and numerical simulation in Shanghai. Environ. Sci. Technol. 51, 202–211. https://doi.org/10.1021/acs.est.6b03834

  39. McMeeking, G.R., Hamburger, T., Liu, D., Flynn, M., Morgan, W.T., Northway, M., Highwood, E.J., Krejci, R., Allan, J.D., Minikin, A., Coe, H. (2010). Black carbon measurements in the boundary layer over western and northern Europe. Atmos. Chem. Phys. 10, 9393–9414. https://doi.org/10.5194/acp-10-9393-2010

  40. Moffet, R.C., de Foy, B., Molina, L.T., Molina, M.J., Prather, K.A. (2008). Measurement of ambient aerosols in northern Mexico City by single particle mass spectrometry. Atmos. Chem. Phys. 8, 4499–4516. https://doi.org/10.5194/acp-8-4499-2008

  41. Moteki, N., Kondo, Y. (2007). Effects of mixing state on black carbon measurements by laser-induced incandescence. Aerosol Sci. Technol. 41, 398–417. https://doi.org/10.1080/02786820701199728

  42. Peng, J., Hu, M., Guo, S., Du, Z., Zheng, J., Shang, D., Zamora, M.L., Zeng, L., Shao, M., Wu, Y.S., Zheng, J., Wang, Y., Glen, C.R., Collins, D.R., Molina, M.J., Zhang, R. (2016). Markedly enhanced absorption and direct radiative forcing of black carbon under polluted urban environments. PNAS 113, 4266–4271. https://doi.org/10.1073/pnas.1602310113

  43. Reid, J.S., Koppmann, R., Eck, T.F., Eleuterio, D.P. (2005). A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmos. Chem. Phys. 5, 799-825. https://doi.org/10.5194/acp-5-799-2005

  44. Sahu, L.K., Kondo, Y., Moteki, N., Takegawa, N., Zhao, Y., Cubison, M.J., Jimenez, J.L., Vay, S., Diskin, G.S., Wisthaler, A., Mikoviny, T., Huey, L.G., Weinheimer, A.J., Knapp, D.J. (2012). Emission characteristics of black carbon in anthropogenic and biomass burning plumes over California during ARCTAS-CARB 2008. J. Geophys. Res. 117, D16302. https://doi.org/10.1029/2011jd017401

  45. Schnaiter, M., Linke, C., Mohler, O., Naumann, K.H., Saathoff, H., Wagner, R., Schurath, U., Wehner, B. (2005). Absorption amplification of black carbon internally mixed with secondary organic aerosol. J. Geophys. Res. 110, D19204. https://doi.org/10.1029/2005jd006046

  46. Schwarz, J.P., Gao, R.S., Fahey, D.W., Thomson, D.S., Watts, L.A., Wilson, J.C., Reeves, J.M., Darbeheshti, M., Baumgardner, D.G., Kok, G.L., Chung, S.H., Schulz, M., Hendricks, J., Lauer, A., Kaercher, B., Slowik, J.G., Rosenlof, K.H., Thompson, T.L., Langford, A.O., Loewenstein, M., Aikin, K.C. (2006). Single-particle measurements of midlatitude black carbon and light-scattering aerosols from the boundary layer to the lower stratosphere. J. Geophys. Res. 111, D16207. https://doi.org/10.1029/2006jd007076

  47. Schwarz, J.P., Gao, R.S., Spackman, J.R., Watts, L.A., Thomson, D.S., Fahey, D.W., Ryerson, T.B., Peischl, J., Holloway, J.S., Trainer, M., Frost, G.J., Baynard, T., Lack, D.A., de Gouw, J.A., Warneke, C., Del Negro, L.A. (2008). Measurement of the mixing state, mass, and optical size of individual black carbon particles in urban and biomass burning emissions. Geophys. Res. Lett. 35, L13810. https://doi.org/10.1029/2008gl033968

  48. Shiraiwa, M., Kondo, Y., Iwamoto, T., Kita, K. (2010). Amplification of light absorption of black carbon by organic coating. Aerosol Sci. Technol. 44, 46–54. https://doi.org/10.1080/02786820903357686

  49. Song, X.H., Hopke, P.K., Fergenson, D.P., Prather, K.A. (1999). Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A. Anal. Chem. 71, 860–865. https://doi.org/10.1021/ac9809682

  50. Stephens, M., Turner, N., Sandberg, J. (2003). Particle identification by laser-induced incandescence in a solid-state laser cavity. Appl. Opt. 42, 3726–3736. https://doi.org/10.1364/ao.42.003726

  51. Tan, J.H., Duan, J.C., Chen, D.H., Wang, X.H., Guo, S.J., Bi, X.H., Sheng, G.Y., He, K.B., Fu, J.M. (2009). Chemical characteristics of haze during summer and winter in Guangzhou. Atmos. Res. 94, 238–245. https://doi.org/10.1016/j.atmosres.2009.05.016

  52. Targino, A.C., Krecl, P. (2016). Local and regional contributions to black carbon aerosols in a mid-sized city in Southern Brazil. Aerosol Air Qual. Res. 16, 125–137. https://doi.org/10.4209/aaqr.2015.06.0388

  53. Wang, J., Zhang, Q., Chen, M., Collier, S., Zhou, S., Ge, X., Xu, J., Shi, J., Xie, C., Hu, J. (2017). First chemical characterization of refractory black carbon aerosols and associated coatings over the Tibetan Plateau (4730 m asl). Environ. Sci. Technol. 51, 14072–14082. https://doi.org/10.1021/acs.est.7b03973

  54. Wang, J., Liu, D., Ge, X., Wu, Y., Shen, F., Chen, M., Zhao, J., Xie, C., Wang, Q., Xu, W. (2019). Characterization of black carbon-containing fine particles in Beijing during wintertime. Atmos. Chem. Phys. 19, 447–458. https://doi.org/10.5194/acp-19-447-2019

  55. Wang, Q., Cao, J., Tao, J., Li, N., Su, X., Chen, L.W.A., Wang, P., Shen, Z., Liu, S., Dai, W. (2013a). Long-term trends in visibility and at Chengdu, China. PLoS One 8, e68894. https://doi.org/10.1371/journal.pone.0068894

  56. Wang, Q., Huang, R.J., Cao, J., Han, Y., Wang, G., Li, G., Wang, Y., Dai, W., Zhang, R., Zhou, Y. (2014). Mixing state of black carbon aerosol in a heavily polluted urban area of China: Implications for light absorption enhancement. Aerosol Sci. Technol. 48, 689–697. https://doi.org/10.1080/02786826.2014.917758

  57. Wang, Q.Y., Huang, R.J., Cao, J.J., Tie, X.X., Ni, H.Y., Zhou, Y.Q., Han, Y.M., Hu, T.F., Zhu, C.S., Feng, T., Li, N., Li, J.D. (2015). Black carbon aerosol in winter northeastern Qinghai-Tibetan Plateau, China: the source, mixing state and optical property. Atmos. Chem. Phys. 15, 13059–13069. https://doi.org/10.5194/acp-15-13059-2015

  58. Wang, T., Jiang, F., Deng, J., Shen, Y., Fu, Q., Wang, Q., Fu, Y., Xu, J., Zhang, D. (2012). Urban air quality and regional haze weather forecast for Yangtze River Delta region. Atmos. Environ. 58, 70–83. https://doi.org/10.1016/j.atmosenv.2012.01.014

  59. Wang, X., Williams, B.J., Wang, X., Tang, Y., Huang, Y., Kong, L., Yang, X., Biswas, P. (2013b). Characterization of organic aerosol produced during pulverized coal combustion in a drop tube furnace. Atmos. Chem. Phys. 13, 10919–10932. https://doi.org/10.5194/acp-13-10919-2013

  60. Wang, Y., Khalizov, A., Levy, M., Zhang, R. (2013c). New Directions: Light absorbing aerosols and their atmospheric impacts. Atmos. Environ. 81, 713–715. https://doi.org/10.1016/j.atmosenv.2013.09.034

  61. Wu, Y., Zhang, R., Tian, P., Tao, J., Hsu, S.C., Yan, P., Wang, Q., Cao, J., Zhang, X., Xia, X. (2016). Effect of ambient humidity on the light absorption amplification of black carbon in Beijing during January 2013. Atmos. Environ. 124, 217–223. https://doi.org/10.1016/j.atmosenv.2015.04.041

  62. Wu, Y., Liu, D., Wang, J., Shen, F., Chen, Y., Cui, S., Ge, S., Wu, Y., Chen, M., Ge, X. (2019). Characterization of size-resolved hygroscopicity of black carbon-containing particle in urban environment. Environ. Sci. Technol. 53, 14212–14221. https://doi.org/10.1021/acs.est.9b05546

  63. Xie, C., Xu, W., Wang, J., Liu, D., Ge, X., Zhang, Q., Wang, Q., Du, W., Zhao, J., Zhou, W. (2019a). Light absorption enhancement of black carbon in urban Beijing in summer. Atmos. Environ. 213, 499–504. https://doi.org/10.1016/j.atmosenv.2015.04.041

  64. Xie, C., Xu, W., Wang, J., Wang, Q., Liu, D., Tang, G., Chen, P., Du, W., Zhao, J., Zhang, Y. (2019b). Vertical characterization of aerosol optical properties and brown carbon in winter in urban Beijing, China. Atmos. Chem. Phys. 19, 165–179. https://doi.org/10.1016/j.atmosenv.2015.04.041

  65. Zhang, D., Zhang, R.Y. (2005). Laboratory investigation of heterogeneous interaction of sulfuric acid with soot. Environ. Sci. Technol. 39, 5722–5728. https://doi.org/10.1021/es050372d

  66. Zhang, G., Bi, X., He, J., Chen, D., Chan, L.Y., Xie, G., Wang, X., Sheng, G., Fu, J., Zhou, Z. (2014). Variation of secondary coatings associated with elemental carbon by single particle analysis. Atmos. Environ. 92, 162–170. https://doi.org/10.1016/j.atmosenv.2014.04.018

  67. Zhang, R., Khalizov, A.F., Pagels, J., Zhang, D., Xue, H., McMurry, P.H. (2008). Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing. PNAS 105, 10291–10296. https://doi.org/10.1073/pnas.0804860105

  68. Zhou, J., Zhang, R., Cao, J., Chow, J.C., Watson, J.G. (2012). Carbonaceous and ionic components of atmospheric fine particles in Beijing and their impact on atmospheric visibility. Aerosol Air Qual. Res. 12, 492–502. https://doi.org/10.4209/aaqr.2011.11.0218

Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.