ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-10395-2017The variability in the relationship between black carbon and carbon monoxide
over the eastern coast of China: BC aging during transportGuoQingfengHuMinminhu@pku.edu.cnGuoSongWuZhijunPengJianfeiWuYushenghttps://orcid.org/0000-0001-7548-8272State Key Joint Laboratory of Environmental Simulation and Pollution
Control, College of Environmental Sciences and Engineering, Peking
University, Beijing, ChinaBeijing Innovation Center for Engineering Science and Advanced
Technology, Peking University, Beijing, Chinanow at: Beijing SDL Technology Co., Ltd., Beijing, Chinanow at: Department of Physics, University of Helsinki, Helsinki, FinlandMin Hu (minhu@pku.edu.cn)6September20171717103951040322January201721March201723July20171August2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/10395/2017/acp-17-10395-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/10395/2017/acp-17-10395-2017.pdf
East Asia is a densely populated region with a myriad of primary
emissions of pollutants such as black carbon (BC) and carbon
monoxide (CO). To characterize primary emissions over the eastern
coast of China, a series of field campaigns were conducted in 2011,
including measurements from a ship cruise, island, and coastal
receptor sites. The relationship between BC and CO is presented here
for the first ship cruise (C1), the second ship cruise (C2), an
island site (Changdao Island, CD), and a coastal site (Wenling,
WL). The average BC mass concentrations were 2.43, 2.73, 1.09, 0.94,
and 0.77 µgm-3 for CD, WL, C1-YS (Yellow Sea),
C1-ES (East China Sea), and C2-ES, respectively. For those
locations, the average CO mixing ratios were 0.55, 0.48, 0.31, 0.36,
and 0.27 ppm. The high loadings of both BC and CO imply
severe anthropogenic pollution over the eastern coast of
China. Additionally, the linear correlation between BC and CO was
regressed for each location. The slopes, i.e., the ratios of ΔBC to ΔCO derived from their relationship,
correlated well with the ratios of diesel consumption to
gasoline consumption in each province/city, which reveals
vehicular emission to be the common source for BC and CO and that there are
distinct fuel structures between North and South China. The ΔBC/ΔCO values at coastal sites (Changdao Island
and Wenling) were much higher than those over the Yellow Sea and East
China Sea, and the correlation coefficients also showed a decreasing
trend from the coast to the sea. Therefore, the quantity of ΔBC/ΔCO and the correlation coefficients are possible
indicators for the aging and removal of BC.
Introduction
Atmospheric radiative forcing is caused by a variety of
particulate and gaseous air pollutants. Among the particulate matters,
black carbon (BC) impacts the Earth's climate directly through the
absorption of solar radiation and indirectly through its role as
cloud condensation nuclei (Bond et al., 2013). The absorption induced
by BC is markedly enhanced by the atmospheric oxidation and aging, as
investigated by many chamber studies (Peng et al., 2016b; Guo et al.,
2016; Schnaiter et al., 2005). BC aging includes physical
condensation–coagulation and chemical oxidation, which transform BC
from hydrophobic to hydrophilic particles (Huang et al., 2013). It not
only plays an important role in global BC distribution and budget (He
et al., 2016; Huang et al., 2013) but also has a significant
influence on optical and hygroscopic properties of BC particles (Bond
et al., 2006; He et al., 2015; Zhang et al., 2008; Khalizov et al.,
2009a). These effects will potentially result in increasing extreme
weather and weakening atmospheric circulations (Wang et al., 2013,
2016; Li et al., 2016). Among the gaseous pollutants, carbon monoxide
(CO) is an indirect greenhouse gas through the production of ozone,
methane, and carbon dioxide (Girach et al., 2014). Both BC and CO are
products of incomplete combustion of carbon-based fuels (Wang et al.,
2015). Though BC and CO are from similar sources, their emission
ratios vary significantly for different sources, so the variations in
measured ratios can indicate the presence of different sources
(McMeeking et al., 2010; Bond et al., 2004). In addition, the
source-specific emission ratio is an important constraint on global
climate and regional air quality model (Spackman et al., 2008).
Sources of BC colocated with CO will result in their concentration
correlations, since the variances in the concentrations are affected
by the same atmospheric process (Wang et al., 2011). There have been
a number of studies about the relationship between BC and CO, and most of these show high correlations (e.g., Zhou et al., 2009;
Spackman et al., 2008). They have generally been conducted at
a stationary site or a cruise, while the simultaneous measurement of
the both is rare. The slopes, i.e., the ratios of ΔBC to
ΔCO, from the linear regressions are used to indicate
different emission sources (Girach et al., 2014; Lee et al., 2013; Pan
et al., 2011) and validate BC emissions from bottom-up inventories
(Wang et al., 2011; Han et al., 2009).
For BC, its atmospheric life cycle includes emissions, transport,
aging, and removal (Bond et al., 2013). The relationship between BC
and CO is the result of a balance between emission sources and sinks
(Spackman et al., 2008; Wang et al., 2015). Thus, differences in
emission sources and removal rates (i.e., sinks) are often used to
explain differences in ΔBC/ΔCO ratios
(McMeeking et al., 2010). To a certain extent, the variability due to
emissions and transport can be accounted for in ΔBC/ΔCO values (De Gouw and Jimenez, 2009; de Gouw
et al., 2005). The atmospheric lifetime of BC is shorter than CO
owing to cloud and precipitation scavenging, which results in decreasing ΔBC/ΔCO with increasing time
and distance from source. Therefore, the variations in ΔBC/ΔCO values also reflect air mass aging and wet
removal processes in addition to sources (McMeeking et al., 2010).
The eastern coastal areas are the most developed in China and are in
the transport pathway of the Asian pollution outflow, especially
during the East Asian monsoon in winter. The air pollutants emitted
from this region and its upwind regions not only result in the
deterioration of the air quality on a regional scale but also exert
an influence on downwind countries in the Pacific Rim (Feng et al.,
2007; Peltier et al., 2008). In order to characterize the outflow of
primary emission over the eastern coast of China, campaigns
including two cruises and two coastal sites were conducted in
2011. Among these was a campaign from March to April including
both the island station and marine cruise observations.
Measurement and meteorologySampling sites and measurement
To characterize the outflow of primary emissions from East China,
a series of campaigns were conducted in the coastal regions in 2011
(Fig. 1). The first campaign was at Changdao Island (CD;
120.74∘ E, 37.92∘ N), Shandong Province, North
China, from 20 March to 24 April, along with the first cruise
observation (C1) conducted in the Yellow Sea (C1-YS) and East China Sea
(C1-ES), from 17 March to 9 April. The second was another cruise
observation in the East China Sea (C2-ES) from 28 May to 8 June. The third was at the Wenling coastal site (WL; 121.74∘ E,
28.43∘ N), Zhejiang Province, South China, from 1 to
28 November.
The coastal sites, cruise tracks, and their observation
periods for the campaigns conducted in 2011. The purple star is the
coastal site of Changdao Island (CD), and the red star is the coastal
one of Wenling (WL). The blue line is the track of the first cruise (C1)
and the green line is the track of the second cruise. The yearly mean
anthropogenic emission of BC is also colored on the map (MEIC,
http://www.meicmodel.org). Abbreviations for
provinces/cities: BJ – Beijing; TJ – Tianjin; HB – Hebei; SD –
Shandong; JS – Jiangsu; SH – Shanghai; ZJ – Zhejiang; YS – Yellow
Sea; ES – East China Sea.
As shown in Fig. 1, Changdao Island (CD) is located off the eastern
coast in North China. To its west and south are the cities of Beijing
and Tianjin and the provinces of Hebei and Shandong, which have the
largest emissions of BC in North China. Wenling (WL) is located at the
eastern coast in South China. Considerable BC is emitted in the
boundary areas of the Yangtze River Delta of Zhejiang, Jiangsu, and
Shanghai, leading to impacts on Wenling when the northwesterly
wind is predominant. A more detailed description of these two
sites can be seen in previous studies (Guo et al., 2015; Yuan
et al., 2013; Hu et al., 2013; Peng et al., 2016a). To the east of
Changdao Island and Wenling are the Yellow Sea and East China Sea, which
are marginal seas surrounded by China, North and South Korea, and Japan.
The synoptic wind flow patterns at 925 hPa averaged
over Changdao Island (a, red star, 20 March–24 April),
the first cruise (b, red line, 17 March–9 April), the
second cruise (c, red line, 28 May–8 June), and
Wenling (d, red star, 1–28 November) campaign periods
as shown in Fig. 1. The arrow length and color show the wind
speed, while the arrowhead indicates the wind direction.
A suite of online instrument was deployed for gaseous and particulate
pollutants measurements during the campaigns. For the primary emission
and BC aging are the focuses, both BC and CO hourly averaged data are
used in this work. BC mass concentration was continually measured by
an Aethalometer (AE-31, Magee
Scientific, USA) with an integration time of
5 min. Aethalometers have been widely used for BC measurement
and have shown excellent agreement with other techniques, such as thermal
and photo-acoustic methods (Zhou et al., 2009; Girach et al., 2014; Nair
et al., 2007; Hitzenberger et al., 2006). The uncertainty for BC mass
concentration was estimated to be 10 %. CO mixing ratio was
measured by a trace level enhanced CO analyzer (48i-TLE, Thermo
Scientific, USA) with an integration time of 1 min. The CO
analyzer was calibrated using a CO standard every week, and zero-checks were performed every day. The overall uncertainty for CO
measurement was estimated to be less than 10 %. For the cruise
observation, the data with simultaneously sharp increase in
concentrations of BC and CO were screened and excluded from the
dataset to avoid contamination by ship emissions.
Meteorological conditions
Figure 2a–d show the mean synoptic wind flow patterns at
925 hPa for the Changdao Island (CD), the first cruise (C1),
the second cruise (C2), and Wenling (WL) campaign periods,
respectively, as obtained from NCEP/NCAR reanalysis
(http://www.esrl.noaa.gov/psd). These flow patterns reveal the
typical impact of the East Asian monsoon over the eastern coast of China,
which includes the winter and summer monsoon. Generally, the winter
monsoon lasts from November to the following April with prevailing
northwesterly wind, while the summer monsoon continues from May to
October with predominantly southwesterly wind.
As can be seen in Fig. 2a, b, and d, Changdao Island, the Yellow Sea and
East China Sea, and Wenling were influenced by the winter monsoon
during the CD, C1, and WL campaigns, respectively, whereas the East China Sea
(Fig. 2c) was impacted by the summer monsoon during the C2 campaign. Though CD and
C1 were in the same period, C1 ended 2 weeks earlier than CD, as
indicated in Fig. 1. Consequently, though there were differences in wind speed, their flow patterns were
mostly consistent. In addition, the wind flow pattern during C1 was also
comparable with that during WL. However, there was a small
discrepancy in wind direction, which implies that they were in opposite phases of the winter monsoon – that is, the period during C1
was at the end of the winter monsoon, which would become weaker and
transition to the summer monsoon, and the period during WL was at the
start of the winter monsoon. The wind flow patterns during the first
and second cruise were almost opposite in direction (Fig. 2b
and c), which suggests that the air mass during the first cruise
mainly flowed from North China to the Yellow Sea and then to the East China
Sea, whereas during the second cruise the air mass direction was from
South China to the East China Sea.
ResultsVariability in BC and CO concentration
The average BC mass concentrations were 2.43, 1.09, 0.94, 0.77, and
2.73 µgm-3 for CD, C1-YS, C1-ES, C2-ES, and WL,
respectively. Correspondingly, the average CO mixing ratios were 0.55,
0.31, 0.36, 0.27, and 0.48 ppm. The average concentrations
between coastal sites were similar, as were the concentrations between
different sea areas. It is clear that the pollutants'
concentrations at coastal sites are higher than those in the marine
atmosphere, but BC and CO in the Yellow Sea and East China Sea still had
considerable loadings, implying severe anthropogenic pollution
from the continent.
BC and CO at Changdao Island had concentration ranges of
0.3–8.5 µgm-3 and 0.1–2.9 ppm (Fig. 3a),
while BC at Wenling had a wider range of
0.1–13.7 µgm-3 and CO had a narrower range of
0.1–1.6 ppm (Fig. 3b). This difference between coastal sites
is associated with the distinct pollutant emissions between North and
South China, which will be discussed further in the
Sect. 3.2. At the same time, except for the pollution episode on 8 April during
C1-YS, the concentrations for BC and CO over the sea were less than
4 µgm-3 and 1 ppm, respectively. The
different concentration ranges between coastal sites and sea areas are
related to the distance to the continental source. The episode in the
Yellow Sea on 8 April also occurred at Changdao Island from 7 to
8 April (shown in the dashed rectangle in Fig. 3a and b), indicating
a regional pollution episode over these areas. The peak concentrations
for BC (CO) between the island and the Yellow Sea were almost the same,
but the peak concentration for Changdao Island (7 April, 18:00 LT)
appeared 14 h earlier than that for the Yellow Sea (8 April, 08:00 LT),
which could be considered to be the transport time between the island and the
Yellow Sea during the regional pollution. In order to verify this, the
forward and backward trajectories were run starting at Changdao Island and in the Yellow
Sea, respectively
(http://www.ready.noaa.gov). The green line (Fig. S1 in the
Supplement) is the 24 h forward trajectory starting at BC peak time
for Changdao Island, and the green one (Fig. S2) is the 24 h backward
trajectory starting at BC peak time for the Yellow Sea. Both lines show
that the transport time from Changdao Island to the Yellow Sea was about
12 h, which agrees with the peak time lag of 14 h.
The time series of BC (the black lines) and CO (the lines
coded by other colors) during the campaigns of Changdao Island
(a), two cruises (b), and Wenling (c).
As illustrated in Fig. 3, the concentrations of BC and CO fluctuated
consistently over the eastern coast of China, which indicates that
they were from the same source. Apparently, the agreement during CD,
C1, and WL was much better than that during C2. In particular, BC and
CO at the Wenling site exhibited the best agreement during the period from
22 to 28 November, suggesting significant impacts from primary
emissions at the site. The reasons for the above variability will be
discussed in the next two sections.
ΔBC/ΔCO variability and
comparison with other studies in East China
Figure 4 shows the relationship between BC and CO for all
campaigns. The data points for the Yellow Sea in the first cruise (C1-YS)
all overlap with those for Changdao Island (Fig. 4a), which is
similar to the case between the East China Sea in the second cruise (C2-ES)
and Wenling (Fig. 4b). This indicates that both C1-YS and CD (or C2-ES
and WL) were influenced by the same air mass. However, the data points
within the dashed oval in Fig. 4b are separate from most of the data for the
Wenling campaign. These data points correspond to those on 19 November
(dashed rectangle in Fig. 2c), when the CO mixing ratio was highest during
the campaign and BC mass concentration was relatively low. During the campaign, heavy precipitation was recorded at midnight
on 18 November. This is in agreement with
the removal mechanism that the precipitation
can much more easily remove aged BC without affecting CO (Hertel et al., 1995; Girach et al., 2014). Therefore, the data
impacted by the precipitation are excluded in regressing the ΔBC/ΔCO slope for Wenling.
Scatter plots of BC vs. CO (a, b) and their
regression slopes and correlation coefficients (c).
The ΔBC/ΔCO values at coastal sites are
compared with those in other studies in East China (Fig. 5a) to find
possible reasons for the distinct ratios among the continental
sites. The studies which simultaneously measured BC and CO are
centered in megacities such as Beijing, Shanghai, and Guangzhou
(Han et al., 2009; Zhou et al., 2009; Andreae et al., 2008). However
there are still very few studies in the North China Plain, where the largest
amounts of BC are emitted, and ΔBC/ΔCO values are not
given in some publications although BC and CO were measured (Sun et al.,
2013). Since the continental sites are close to source regions, it is
speculated that the ΔBC/ΔCO values are
determined more by primary emission than by atmospheric
processing.
The ΔBC/ΔCO ratios in this study
and other studies in East China (a) and their function of
the ratios of diesel consumption to gasoline consumption in each
province/city (b).
The strong and positive correlation between BC and CO is attributed to
common sources such as vehicular emissions (Badarinath et al.,
2007). In the vehicular emissions, CO is primarily emitted from
gasoline vehicles, while BC emissions are dominated by diesel vehicles
(Han et al., 2009). In a previous study (Zhou et al., 2009), the
difference in ΔBC/ΔCO values between
Beijing and Shanghai was attributed to the higher percentage of
diesel vehicles in Shanghai. As shown in Fig. 5a, the ΔBC/ΔCO values in Beijing and Changdao Island in
North China are less than those in Nanjing, Shanghai, Wenling, and
Guangzhou in South China, suggesting disparate fuel structures in
North and South China. To prove this, the ΔBC/ΔCO values at different sites were compared with the ratios of
diesel consumption to gasoline consumption in each
province/city (China Energy Statistical Yearbook, 2013); these show
considerable correlation (R2=0.63, Fig. 5b), confirming that
BC and CO are mainly from vehicular emissions. A consumption ratio
less than 1 in Beijing indicates that gasoline is dominant in
North China, while a ratio more than 1 in Nanjing, Shanghai, Wenling,
and Guangzhou implies that diesel is dominant in South
China. However, the data point for Changdao Island in Shandong
Province is excluded from the regression line for other sites. The
reason for this is that Changdao Island is a rural site with little local
vehicle emission, and it was influenced by Beijing and its surrounding
regions during the Asian winter monsoon, when the predominant wind was northwesterly (Fig. 2a). The ΔBC/ΔCO value at
Changdao Island was thus less than that in Beijing.
Although the ΔBC/ΔCO values and
consumption ratios have a good correlation (R2=0.63), the
consumption ratios cannot fully explain the variability in ΔBC/ΔCO values. In one aspect, vehicular
consumption of diesel and gasoline is only a part of the total fuel
consumption. In another aspect, BC and CO are controlled not only by
emission from the local province/city but also by emission
transported from other areas on a regional scale. Moreover, other
sources such as biomass burning can also contribute to BC and CO and
change the ΔBC/ΔCO value.
BC aging during transport
The ΔBC/ΔCO variability can result from
the spatial variation of BC and CO source/sink strength (Badarinath
et al., 2007). Since most of BC emission sources are centered in East
China (Fig. 1), the ΔBC/ΔCO variability
depends on emission sources before BC leaves the continent, as
indicated by the comparison in Sect. 3.2, which elucidates the
disparate fuel structures between North and South China. When BC is
transported to the marine boundary layer, the variability in the
ΔBC/ΔCO ratio is dominantly associated
with BC aging and removal, given the insignificant anthropogenic
sources in the marine boundary layer. Therefore, the ΔBC/ΔCO values between the continental and marine atmospheres may
be an ideal comparison to reflect the aging extent of BC.
Owing to the aging and removal of BC and the longer atmospheric
lifetime of CO, the slopes, i.e., the ratios of ΔBC to
ΔCO and correlation coefficients will decrease together
from upwind to downwind areas. The ΔBC/ΔCO values for Changdao, C1-YS (excluding the episode data), and
C1-ES are 4.58, 3.49, and 1.84 µgm-3ppm-1,
respectively, showing a descending trend from north to south over the
eastern coast of China (Fig. 4c). This was consistent with the
predominant northwestern wind during the winter monsoon (Fig. 2a and
b). Meanwhile, the correlation coefficients reduce from 0.68 to 0.28
(Fig. 4c). Therefore the slopes and correlation coefficients determined from
the linear regression are possible indicators of the aging and
deposition of BC during transport. This can be evidenced by the
pollution episode in the Yellow Sea during the first cruise, where BC and
CO have a slope of 3.30 µgm-3ppm-1 and
a correlation coefficient of 0.68. Though the slope is a little
smaller than that at Changdao Island
(4.58 µgm-3ppm-1), the
correlation coefficient is the same.
Under the influence of the summer monsoon (Fig. 2c), the East China Sea is
located in the downwind of Wenling. The ΔBC/ΔCO values for Changdao Island
(4.58 µgm-3ppm-1) and C2-ES
(4.84 µgm-3ppm-1) are similar, but the ΔBC/ΔCO value for Wenling
(9.15 µgm-3ppm-1) is 2 times more than those
at CD and C2-ES, which means that the source region for C2-ES was in
South China rather than in North China. Thus, the campaigns of C2-ES and
WL can be considered as a transport process, though these two
campaigns are not simultaneously conducted. The ΔBC/ΔCO values and correlation coefficient for WL
and C2-ES during the summer monsoon also showed a descending trend, as
was the case for those for CD, C1-YS, and C1-ES during the winter monsoon
(Fig. 4c). Therefore, the decreasing slopes and correlation
coefficients from source to receptor areas indicate more aging and
easier removal of BC after outflow from the source regions. It is well
known that, at a microscopic level, BC aging is generally indicated by the
coating thickness, and the coating thickness is associated with the
mixing state and morphological variation (Khalizov et al., 2009b;
Pagels et al., 2009), which ultimately enhance BC aging. It is
shown here that, at the macroscopic level, BC aging and subsequent removal
result in variation of ΔBC/ΔCO values and
correlation coefficients in the relationship between BC and CO, which
deepens the comprehensive understanding on BC aging.
The BC average concentration for C1-ES (0.94 µgm-3)
during the winter monsoon was only a little higher than that for C2-ES
(0.77 µgm-3) during the summer monsoon. However, the
ΔBC/ΔCO value in C1-ES was 0.4 times less
than that in Changdao Island, and the ratio in C2-ES was nearly 0.55 times less than that in Wenling, indicating more aging of BC in the East
China Sea during the winter monsoon. Due to the greater extent of aging,
BC during the winter monsoon can be more hygroscopic and resulted in a
more significant radiative effect (Moffet and Prather, 2009; Bond and
Bergstrom, 2006).
Conclusions
Atmospheric campaigns including two island/coastal sites and two
cruises were conducted in 2011 to characterize the outflow of primary
emission over the eastern coast of China. Due to a large amount of
continental pollutant emissions, there were considerable loadings of
BC and CO in the coast and sea areas in East China under the
influence of the Asian monsoon. The slopes, i.e., the ratios of ΔBC to ΔCO from the relationship between BC and
CO, were regressed to deduce information about BC source and aging
during transport. The ΔBC/ΔCO values in
North China were smaller than those in South China, which revealed the
disparate fuel structures between North and South China. The ΔBC/ΔCO values were well associated with the
ratios of diesel consumption to gasoline consumption in each
province/city, which confirmed that BC and CO were primarily from
vehicular emissions. The consumption ratios imply that gasoline
was dominant in North China while diesel was dominant in South
China.
The comparison of ΔBC/ΔCO values between
the coastal site and the sea area reflected the aging and deposition
of BC. During the simultaneous measurements of Changdao Island and the
first cruise, the ΔBC/ΔCO value and the
correlation coefficient decreased with the distance from the source
under the influence of the winter monsoon. The ΔBC/ΔCO value and the correlation coefficient also
showed a decreasing trend from Wenling to the East China Sea. Therefore,
the ΔBC/ΔCO ratio and the correlation
coefficient are possible indicators for BC aging and removal after
outflow from the source regions, which deepens the comprehensive
understanding of BC aging at the macroscopic level.
The data presented in this article are available from the authors upon request (minhu@pku.edu.cn).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-10395-2017-supplement.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation
of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
This work was supported by the National Basic Research Program of China
(973 Program) (2013CB228503), National Natural Science Foundation of
China (91544214, 41421064, 21677002), China Ministry of
Environmental Protection's Special Funds for Scientific Research on
Public Welfare (201009002), and National Key Research and
Development Program of China (2016YFC0202003). We thank the CAPTAIN
team from Peking University, Peking University Shenzhen Graduate
School, and Zhejiang Province Environmental Monitoring Center for
their help and support for this research.
Edited by: Renyi Zhang
Reviewed by: two anonymous referees
ReferencesAndreae, M. O., Schmid, O., Yang, H., Chand, D., Yu, J. Z., Zeng, L. M., and Zhang, Y. H.: Optical properties and chemical composition of the atmospheric aerosol in urban Guangzhou, China, Atmos. Environ., 42, 6335–6350, 10.1016/j.atmosenv.2008.01.030, 2008.Badarinath, K. V. S., Latha, K. M., Chand, T. R. K., Reddy, R. R., Gopal, K. R., Reddy, L. S. S., Narasimhulu, K., and Kumar, K. R.: Black carbon aerosols and gaseous pollutants in an urban area in North India during a fog period, Atmos. Res., 85, 209–216, 10.1016/j.atmosres.2006.12.007, 2007.Bond, T., Doherty, S., Fahey, D., Forster, P., Berntsen, T.,
DeAngelo, B., Flanner, M., Ghan, S., Kärcher, B., and Koch, D.:
Bounding the role of black carbon in the climate system: a scientific
assessment, J. Geophys. Res.-Atmos., 118, 5380–5552,
10.1002/jgrd.50171, 2013.Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles: an investigative review, Aerosol Sci. Tech., 40, 27–67, 10.1080/02786820500421521, 2006.Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J.-H., and Klimont, Z.: A technology-based global inventory of black and organic carbon emissions from combustion, J. Geophys. Res.-Atmos., 109, D14203, 10.1029/2003JD003697, 2004.Bond, T. C., Habib, G., and Bergstrom, R. W.: Limitations in the enhancement of visible light absorption due to
mixing state, J. Geophys. Res.-Atmos., 111, D20211, 10.1029/2006JD007315, 2006.De Gouw, J. and Jimenez, J. L.: Organic aerosols in the Earth's atmosphere, Environ. Sci. Technol., 43, 7614–7618, 10.1021/es9006004, 2009.de Gouw, J. A., Middlebrook, A. M., Warneke, C., Goldan, P. D., Kuster, W. C., Roberts, J. M., Fehsenfeld, F. C., Worsnop, D. R., Canagaratna, M. R., Pszenny, A. A. P., Keene, W. C., Marchewka, M., Bertman, S. B., and Bates, T. S.: Budget of organic carbon in a polluted atmosphere: results from the New England Air
Quality Study in 2002, J. Geophys. Res.-Atmos., 110, D16305, 10.1029/2004JD005623, 2005.Feng, J. L., Guo, Z. G., Chan, C. K., and Fang, M.: Properties of organic matter in PM2.5 at Changdao Island, China – a rural site in the transport path of the Asian continental outflow, Atmos. Environ., 41, 1924–1935, 10.1016/j.atmosenv.2006.10.064, 2007.Girach, I. A., Nair, V. S., Babu, S. S., and Nair, P. R.: Black carbon and carbon monoxide over Bay of Bengal during W_ICARB: source characteristics, Atmos. Environ., 94, 508–517, 10.1016/j.atmosenv.2014.05.054, 2014.Guo, Q., Hu, M., Guo, S., Wu, Z., Hu, W., Peng, J., Hu, W., Wu, Y., Yuan, B., Zhang, Q., and Song, Y.: The identification of source regions of black carbon at a receptor site off the eastern coast of China, Atmos. Environ., 100, 78–84, 10.1016/j.atmosenv.2014.10.053, 2015.
Guo, S., Hu, M., Lin, Y., Gomez-Hernandez, M., Zamora, M. L., Peng, J. F., Collins, D. R., and Zhang, R. Y.: OH-initiated oxidation of m-xylene on black carbon aging, Environ. Sci. Technol., 50, 8605–8612, 2016.Han, S., Kondo, Y., Oshima, N., Takegawa, N., Miyazaki, Y., Hu, M., Lin, P., Deng, Z., Zhao, Y., Sugimoto, N., and Wu, Y.: Temporal variations of elemental carbon in Beijing, J. Geophys. Res.-Atmos., 114, D23202, 10.1029/2009jd012027, 2009.He, C., Liou, K. N., Takano, Y., Zhang, R., Zamora, M. L., Yang, P., Li, Q., and Leung, L. R.: Variation of the radiative properties during black carbon aging: theoretical and experimental intercomparison,
Atmos. Chem. Phys., 15, 11967–11980, 10.5194/acp-15-11967-2015, 2015.He, C. L., Li, Q. B., Liou, K. N., Qi, L., Tao, S., and
Schwarz, J. P.: Microphysics-based black carbon aging in a global CTM:
constraints from HIPPO observations and implications for global black
carbon budget, Atmos. Chem. Phys., 16, 3077–3098, 10.5194/acp-16-3077-2016, 2016.Hertel, O., Christensen, J., Runge, E. H., Asman, W. A. H., Berkowicz, R., Hovmand, M. F., and Hov, O.: Development and testing of a new variable scale air-pollution model – Acdep, Atmos. Environ., 29, 1267–1290, 10.1016/1352-2310(95)00067-9, 1995.
Hitzenberger, R., Petzold, A., Bauer, H., Ctyroky, P., Pouresmaeil, P., Laskus, L., and Puxbaum, H.: Intercomparison of thermal and optical measurement methods for elemental carbon and black carbon at an urban location, Environ. Sci. Technol., 40, 6377–6383, 2006.Hu, W. W., Hu, M., Yuan, B., Jimenez, J. L., Tang, Q., Peng, J. F., Hu, W., Shao, M., Wang, M., Zeng, L. M., Wu, Y. S., Gong, Z. H., Huang, X. F., and He, L. Y.: Insights on organic aerosol aging and the influence of coal combustion at a regional receptor site of central eastern China, Atmos. Chem. Phys., 13, 10095–10112, 10.5194/acp-13-10095-2013, 2013.Huang, Y., Wu, S., Dubey, M. K., and French, N. H. F.: Impact of aging
mechanism on model simulated carbonaceous aerosols,
Atmos. Chem. Phys., 13, 6329–6343, 10.5194/acp-13-6329-2013, 2013.Khalizov, A. F., Xue, H., Wang, L., Zheng, J., and Zhang, R.: Enhanced
light absorption and scattering by carbon soot aerosol internally
mixed with sulfuric acid, J. Phys. Chem. A, 113, 1066–1074, 10.1021/jp807531n, 2009a.Khalizov, A. F., Zhang, R., Zhang, D., Xue, H., Pagels, J., and McMurry, P. H.: Formation of highly hygroscopic soot aerosols upon internal mixing with sulfuric acid vapor, J. Geophys. Res.-Atmos., 114, D05208, 10.1029/2008JD010595, 2009b.Lee, Y. C., Lam, Y. F., Kuhlmann, G., Wenig, M. O., Chan, K. L., Hartl, A., and Ning, Z.: An integrated approach to identify the biomass burning sources contributing to black carbon episodes in Hong Kong, Atmos. Environ., 80, 478–487, 10.1016/j.atmosenv.2013.08.030, 2013.
Li, Z. Q., Lau, W. K. M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu, J., Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y., Huang, J., Wang, B., Xu, X., Lee, S. S., Cribb, M., Zhang, F., Yang, X., Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai, P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon climate interactions over Asia, Rev. Geophys., 54, 866–929, 2016.McMeeking, G. R., Hamburger, T., Liu, D., Flynn, M., Morgan, W. T., Northway, M., Highwood, E. J., Krejci, R., Allan, J. D., Minikin, A., and Coe, H.: Black carbon measurements in the boundary layer over western and northern Europe, Atmos. Chem. Phys., 10, 9393–9414, 10.5194/acp-10-9393-2010, 2010.Moffet, R. C. and Prather, K. A.: In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates, P. Natl. Acad. Sci. USA, 106, 11872–11877, 10.1073/pnas.0900040106, 2009.Nair, V. S., Moorthy, K. K., Alappattu, D. P., Kunhikrishnan, P. K., George, S., Nair, P. R., Babu, S. S., Abish, B., Satheesh, S. K., Tripathi, S. N., Niranjan, K., Madhavan, B. L., Srikant, V., Dutt, C. B. S., Badarinath, K. V. S., and Reddy, R. R.: Wintertime aerosol characteristics over the Indo-Gangetic Plain (IGP): impacts of local boundary layer processes and long-range
transport, J. Geophys. Res.-Atmos., 112, D13205, 10.1029/2006JD008099, 2007.
Pagels, J., Khalizov, A. F., McMurry, P. H., and Zhang, R. Y.: Processing of soot by controlled sulphuric acid and water condensation – mass and mobility relationship, Aerosol Sci. Tech., 43, 629–640, 2009.Pan, X. L., Kanaya, Y., Wang, Z. F., Liu, Y., Pochanart, P., Akimoto, H., Sun, Y. L., Dong, H. B., Li, J., Irie, H., and Takigawa, M.: Correlation of black carbon aerosol and carbon monoxide in the high-altitude environment of Mt. Huang in Eastern China, Atmos. Chem. Phys., 11, 9735–9747, 10.5194/acp-11-9735-2011, 2011.Peltier, R. E., Hecobian, A. H., Weber, R. J., Stohl, A.,
Atlas, E. L., Riemer, D. D., Blake, D. R., Apel, E., Campos, T., and
Karl, T.: Investigating the sources and atmospheric processing of fine
particles from Asia and the Northwestern United States measured during
INTEX B, Atmos. Chem. Phys., 8, 1835–1853,
10.5194/acp-8-1835-2008, 2008.
Peng, J. F., Hu, M., Gong, Z. H., Tian, X. D., Wang, M., Zheng, J., Guo, Q. F., Cao, W., Lv, W., Hu, W. W., Wu, Z. J., and Guo, S.: Evolution of secondary inorganic and organic aerosols during transport: a case study at a regional receptor site, Environ. Pollut., 218, 794–803, 2016a.
Peng, J. F., Hu, M., Guo, S., Du, Z. F., Zheng, J., Shang, D. J., Zamora, M. L., Zeng, L. M., Shao, M., Wu, Y. S., Zheng, J., Wang, Y., Glen, C. R., Collins, D. R., Molina, M. J., and Zhang, R. Y.: Markedly enhanced absorption and direct radiative forcing of black carbon under polluted urban environments, P. Natl. Acad. Sci. USA, 113, 4266–4271, 2016b.Schnaiter, M., Linke, C., Mohler, O., Naumann, K. H., Saathoff, H., Wagner, R., Schurath, U., and Wehner, B.: Absorption amplification of black carbon internally mixed with secondary organic
aerosol, J. Geophys. Res.-Atmos., 110, D19204, 10.1029/2005JD006046, 2005.Spackman, J. R., Schwarz, J. P., Gao, R. S., Watts, L. A.,
Thomson, D. S., Fahey, D. W., Holloway, J. S., de Gouw, J. A.,
Trainer, M., and Ryerson, T. B.: Empirical correlations between black
carbon aerosol and carbon monoxide in the lower and middle
troposphere, Geophys. Res. Lett., 35, L19816, 10.1029/2008gl035237, 2008.Sun, Y. W., Zhou, X. H., Wai, K. M., Yuan, Q., Xu, Z., Zhou, S. Z., Qi, Q., and Wang, W. X.: Simultaneous measurement of particulate and gaseous pollutants in an urban city in North China Plain during the heating period: implication of source contribution, Atmos. Res., 134, 24–34, 10.1016/j.atmosres.2013.07.011, 2013.Wang, Q. Y., Liu, S. X., Zhou, Y. Q., Cao, J. J., Han, Y. M., Ni, H. Y., Zhang, N. N., and Huang, R. J.: Characteristics of black carbon aerosol during the chinese lunar year and weekdays in Xi'an, China, Atmosphere-Basel, 6, 195–208, 10.3390/atmos6020195, 2015.
Wang, Y., Khalizov, A., Levy, M., and Zhang, R. Y.: New directions: light absorbing aerosols and their atmospheric impacts, Atmos. Environ., 81, 713–715, 2013.
Wang, Y., Ma, P. L., Jiang, J. H., Su, H., and Rasch, P. J.: Toward reconciling the influence of atmospheric aerosols and greenhouse gases on light precipitation changes in Eastern China, J. Geophys. Res.-Atmos., 121, 5878–5887, 2016.Wang, Y. X., Wang, X., Kondo, Y., Kajino, M., Munger, J. W., and Hao, J. M.: Black carbon and its correlation with trace gases at a rural site in Beijing: top-down constraints from ambient measurements on bottom-up emissions, J. Geophys. Res.-Atmos., 116, D24304, 10.1029/2011jd016575, 2011.
Yuan, B., Hu, W. W., Shao, M., Wang, M., Chen, W. T., Lu, S. H., Zeng, L. M., and Hu, M.: VOC emissions, evolutions and contributions to SOA formation at a receptor site in eastern China, Atmos. Chem. Phys., 13, 8815–8832, 10.5194/acp-13-8815-2013, 2013.
Zhang, R., Khalizov, A. F., Pagels, J., Zhang, D., Xue, H., and Mcmurry, P. H.: Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing, P. Natl. Acad. Sci. USA, 105, 10291–10296, 2008.Zhou, X., Gao, J., Wang, T., Wu, W., and Wang, W.: Measurement of black carbon aerosols near two Chinese megacities and the implications for improving emission inventories, Atmos. Environ., 43, 3918–3924, 10.1016/j.atmosenv.2009.04.062, 2009.