As major chemical components of airborne fine particulate
matter (PM2.5), organic carbon (OC) and elemental carbon (EC) have
vital impacts on air quality, climate change, and human health. Because OC
and EC are closely associated with fuel combustion, it is helpful for the
scientific community and policymakers assessing the efficacy of air
pollution control measures to study the impact of control measures
and regional transport on OC and EC levels. In this study, hourly mass
concentrations of OC and EC associated with PM2.5 were
semi-continuously measured from March 2013 to February 2018. The results
showed that annual mean OC and EC concentrations declined from 14.0 to 7.7 µgm-3 and from 4.0 to 2.6 µgm-3, respectively, from
March 2013 to February 2018. In combination with the data of OC and EC in
previous studies, an obvious decreasing trend in OC and EC concentrations
was found, which was caused by clean energy policies and effective air
pollution control measures. However, no obvious change in the ratios of OC
and EC to the PM2.5 mass (on average, 0.164 and 0.049, respectively)
was recorded, suggesting that inorganic ions still contributed a lot
to PM2.5. Based on the seasonal variations in OC and EC, it appeared
that higher OC and EC concentrations were still observed in the winter
months, with the exception of winter of 2017–2018. Traffic policies executed
in Beijing resulted in nighttime peaks of OC and EC, caused by heavy-duty
vehicles and heavy-duty diesel vehicles being permitted to operate from 00:00
to 06:00 (China standard time, UTC+8, for all times throughout the paper). In addition, the fact that there was no traffic restriction in
weekends led to higher concentrations on weekends compared to weekdays.
Significant correlations between OC and EC were observed throughout the
study period, suggesting that OC and EC originated from common emission
sources, such as exhaust of vehicles and fuel combustion. OC and EC levels
increased with enhanced SO2, CO, and NOx concentrations while the
O3 and OC levels were enhanced simultaneously when O3 concentrations
were higher than 50 µgm-3. Non-parametric wind regression analysis
was performed to examine the sources of OC and EC in the Beijing area. It
was found that there were distinct hot spots in the northeast wind sector at
wind speeds of approximately 0–6 km h-1, as well as diffuse signals in the
southwestern wind sectors. Source areas further away from Beijing were
assessed by potential source contribution function (PSCF) analysis. A
high-potential source area was precisely pinpointed, which was located in
the northwestern and southern areas of Beijing in 2017 instead of solely in
the southern areas of Beijing in 2013. This work shows that improvement of
the air quality in Beijing benefits from strict control measures; however,
joint prevention and control of regional air pollution in the regions is
needed for further improving the air quality. The results provide a
reference for controlling air pollution caused by rapid economic development
in developing countries.
Introduction
Worldwide attention on atmospheric organic carbon (OC) and elemental carbon
(EC) has been paid by the public and the scientific community because OC and
EC have vital effects on air quality, atmospheric visibility, climate, and
human health (Bond et al., 2013; Boucher et al., 2013; WHO, 2012). OC is composed of thousands of organic compounds
and occupies 10 %–50 % of the ambient PM2.5 mass (Seinfeld and
Pandis, 1998) while EC, which is emitted from fuel combustion, represents
1 %–13 % of the ambient PM2.5 mass (Shah et al., 1986; Tao et al.,
2017; Malm et al., 1994). Considering that OC and EC occupy
high fractions of the PM2.5, a decline in OC and EC concentrations will
improve air quality. Due to the light-scattering potential of OC and the
light absorption ability of EC, high concentrations of OC and EC can impair
the atmospheric visibility. In addition, OC and EC can affect the
atmospheric energy balance through scattering and absorbing incoming and
outgoing solar and terrestrial radiation (direct effect) and through
modifying the microphysical properties of clouds, like influencing cloud
condensation nuclei and/or ice nuclei (indirect effects). Direct and
indirect effects of OC and EC remain one of the principal uncertainties in
estimates of anthropogenic radiative forcing (Boucher et al., 2013). In
particular, black carbon (BC also called EC) coated with secondary particles
can enhance aerosol radiative forcing (Wang et al., 2013; Zhang et al.,
2008). BC is found to aggravate haze pollution in megacities (Ding et al.,
2016; Zhang et al., 2018). Most of all, OC and EC adversely affect human
health. As important constituents of OC, polycyclic aromatic hydrocarbons
(PAHs) are well-known as carcinogens, mutagens, and teratogens and therefore
pose a serious threat to the health and the well-being of humans
(Boström et al., 2002). Short-term epidemiological studies provide
sufficient evidence of all-cause and cardiovascular mortality and
cardiopulmonary hospital admissions associated with daily variations in BC
concentrations; in addition, cohort studies proved that all-cause and
cardiopulmonary mortality are linked with long-term average BC exposure
(WHO, 2012). Thus, long-term continuous observations of OC and EC are a
prerequisite to further study air quality, atmospheric visibility, climate
effects, and human health. However, long-term continuous observations of OC
and EC in China are scarce.
In the world, China is considered to be one of the regions of high emissions of
OC and EC due to high energy consumption and increasing vehicle population,
accompanying rapid economic development and urbanization for decades
(http://www.stats.gov.cn/tjsj/ndsj/2017/indexch.htm, last access: 26 June 2019). As the capital of
China, Beijing with a residential population of 21.7 million, domestic
tourists of 2.9×102 million, and foreign tourists of
approximately 3.3 million in 2017 (http://tjj.beijing.gov.cn/English/AD/, last access: 26 June 2019)
faces severe air pollution problems, which have attracted worldwide
attention. A series of studies on OC and EC have already been performed in
Beijing. Lang et al. (2017) indicated that OC showed a downward trend and EC
had almost no change before 2003; both increased from 2003 to 2007, but
decreased after 2007. The decline in OC concentrations was associated with
coal combustion and motor vehicle emission control measures, while that in
EC was caused by the replacement of fossil fuel and control of biomass
emissions. Tao et al. (2017) stated that the nearly 30 % reduction in
total carbon (TC) in recent years in Beijing can be taken as a real trend.
Lv et al. (2016) found that the concentrations of OC and EC remained
unchanged from 2000 to 2010 in Beijing. Yang et al. (2011a) conducted a
long-term study of carbonaceous aerosol from 2005 to 2008 in urban Beijing
and found a decline in the ratio of carbonaceous species to the PM2.5
mass in contrast to what was observed 10 years earlier, which indicated that
the importance of carbonaceous species in PM2.5 had decreased. In
addition, pronounced seasonal variations were recorded with the highest
concentrations occurring in winter and the lowest ones in summer. Overall,
this previous research seems somewhat inconsistent and
seldom focused on studying the impact of air pollution control measures
and regional transport on the carbonaceous aerosol levels in detail.
Notably, a series of the strictest measures on emission abatement and
pollution control were implemented in China in September 2013
(Jin et al., 2016). Substantial manpower and material resources
have been put into improving the air quality in the past 5 years and
significant measures are being taken for the atmospheric environment and
ecosystem (Gao et al., 2017). To evaluate the effectiveness
of air pollution control measures, it is necessary to conduct a long-term
continuous observation of OC and EC and to study their long-term variation.
Most of the previous studies showed average information for certain periods
based on filter sampling and laboratory analysis and did not reflect the
dynamic evolution processes of OC and EC with hourly resolution, which can
provide important and detailed information for the potential health risk in
the area with frequent occurrence of air pollution episodes. In addition,
long-term measurements in urban areas of China with high population density
were scarce (Yang et al., 2005, 2011a; Zhang et al., 2011; Li et al., 2015;
Chang et al., 2017) and the knowledge of long-term continuous hourly
observations is still lacking, which is still important for recognizing the
influence of source emissions on air quality.
Based on the abovementioned background, it is necessary to perform a
long-term continuous hourly observation to explore the characteristics of OC
and EC, to examine the relationship between OC and EC and with major air
pollutants and their sources so as to better assess the influence of
emission control measures on the OC and EC levels. In this study,
inter-annual, seasonal, weekly, and diurnal variation in OC and EC was
investigated. The influence of local and regional anthropogenic sources was
evaluated using non-parametric wind regression (NWR) and potential
contribution source function (PSCF) methods. This study will be helpful for
improving the understanding of the variation and sources of OC and EC
associated with PM2.5 and assessing the effectiveness of local and
national PM control measures, and it provides a valuable dataset for
atmospheric modelling study and assessing the health risk. It is also the
first time that a continuous hourly measurement for a 5-year period based on
the thermal–optical method is reported for urban Beijing.
ExperimentalDescription of the site
The study site (39∘58′28′′ N, 116∘22′16′′ E, 44 m
above ground) was set up in the second floor in the campus of the state key
laboratory of atmospheric boundary physics and atmospheric chemistry of the
Institute of Atmospheric Physics, Chinese Academy of Science (Fig. 1). The
site is approximately 1 km south of the 3rd Ring Road (main road), 1.2 km
north of the 4th Ring Road (main road), 200 m west of the G6 highway
(which runs north–south) and 50 m south of the Beitucheng West Road (which
runs east–west). The annual average vehicular speeds in the
morning and evening traffic peaks were approximately 27.8 and 24.6 km h-1,
respectively, in the past 5 years. During the whole study period the
level of traffic congestion is mild based on the traffic performance index
published by the Beijing Traffic Management Bureau
(http://www.bjtrc.org.cn/, last access: 26 June 2019), which indicated 1.5–1.8 times more time will be
taken to publicly travel during traffic peaks than during smooth traffic.
The study site is surrounded by residential zones, a street park, and a
building of ancient relics without industrial sources. The experimental
campaign was performed from 1 March 2013 to 28 February 2018. The periods
of 1 March 2013 to 28 February 2014, 1 March 2014 to 28 February 2015,
1 March 2015 to 29 February 2016, 1 March 2016 to 28 February 2017, and
1 March 2017 to 28 February 2018 are, hereinafter, called 2013,
2014, 2015, 2016, and 2017, respectively, for short.
Map with location of the sampling site (the asterisk in the right
figure indicates the sampling site).
Instrumentation
Concentrations of PM2.5-associated OC and EC were hourly measured with
semi-continuous thermal–optical transmittance method OC/EC analysers (model
4, Sunset Laboratory Inc., Oregon, USA). The
operation and maintenance are strictly executed according to standard
operating procedures (SOPs, https://www3.epa.gov/ttnamti1/spesunset.html, last access: 26 June 2019).
Volatile organic gases are removed by an inline parallel carbon denuder
installed upstream of the analyser. A round 16 mm quartz filter is used to
collect PM2.5 with a sampling flow rate of 8 L m-1. A modified NIOSH
thermal protocol (RT-Quartz) is used to measure OC and EC. The sampling
period is 30 min and the analysis process lasts for 15 min. Calibration is
performed according to the SOP. An internal standard CH4 mixture (5.0 %; ultra-high-purity He) is automatically injected to calibrate the
analyser at the end of every analysis. In addition, off-line calibration was
conducted with an external amount of sucrose standard (1.06 µg) every
3 months. The quartz fibre filters used for sample collection were
replaced by new ones before the laser correction factor dropped below 0.90.
After replacement, a blank measurement of the quartz fibre filters is
carried out. The uncertainty of the TC measurement has been estimated to be
approximately ±20 % (Peltier et al., 2007). The
analyzers/monitors for O3, CO, SO2, NOx, and PM2.5 and
their precision, detection limits, and calibration methods have been
described in detail elsewhere (Ji et al., 2014). Briefly, O3 was
measured using an ultraviolet photometric analyser (model 49i, Thermo Fisher
Scientific (Thermo), USA), CO with a gas filter correlation non-dispersive
infrared method analyser (model 48i, Thermo, USA), SO2 using a
pulsed-fluorescence analyser (model 43i, Thermo, USA), NO–NO2–NOx
with a chemiluminescence analyser (model 42, Thermo, USA), and PM2.5
using a U.S. Environmental Protection Agency Federal Equivalent Method
analyser of PM2.5 (SHARP 5030, Thermo, USA). Meteorological data such
as wind speed (WS), wind direction (WD), relative humidity (RH), and
atmospheric temperature (T) were recorded via an automatic meteorological
station (model AWS310; Vaisala, Finland). The data were processed using an
Igor-based software (Wu et al., 2018) and the commercial software of Origin.
NWR and PSCF methodsNWR method
NWR is a source-to-receptor source identification model, which provides a
meaningful allocation of local sources (Henry et al., 2009; Petit et al.,
2017). Wind analysis results using NWR were obtained using a new Igor-based
tool, named ZeFir, which can perform a comprehensive investigation of the
geographical origins of the air pollutants (Petit et al., 2017). The
principle of NWR is to smooth the data over a fine grid so that
concentrations of air pollutants of interest can be estimated by any coupling
of wind direction (θ) and wind speed (u). The smoothing is based on a
weighing average where the weighing coefficients are determined using a
weighting function K(θ, u, σ, h)=K1(θ,
σ)×K2 (u, h) (i.e. Kernel functions). The estimated
value (E) given θ and u is calculated by Eqs. (1)–(3):
1E(θ|u)=∑i=1NK1θ-Wiσ×K2u-Yih×Ci∑i=1NK1θ-Wiσ×K2u-Yih,2K1(x)=12π×e-0.5x2-∞<x<∞,3K2(x)=0.75×(1-x2)-1<x<1,
where σ and h were smoothing parameters, which can be suggested by
clicking on the button “suggest estimate” in the software ZeFir; Ci, Wi, and
Yi are the observed concentration of a pollutant of interest and resultant wind
speed and direction, respectively, for the ith observation in a time period
starting at time ti; N is the total number of observations.
After the calculation, graphs of the estimated concentration and the joint
probability are generated. The NWR graph of the air pollutant of interest,
acquired directly via the NWR calculation, represents an integrated picture
of the relationship of estimated concentration of the specific pollutant,
wind direction and wind speed. The graph of the joint probability for the
wind data, equivalent to a wind rose, shows the occurrence probability
distribution of the wind speed and wind direction.
PSCF method
The PSCF method is based on the residence time probability analysis of air
pollutants of interest (Ashbaugh et al., 1985). Source locations and
preferred transport pathways can be identified (Poirot and Wishinski, 1986;
Polissar et al., 2001; Lupu and Maenhaut, 2002). The potential locations of
the emission sources are determined using backward trajectories. A detailed
description can be found in Wang et al. (2009). In principle, the PSCF is
expressed using Eq. (4):
PSCF(i,j)=wij×(mij/nij),
where wij is an empirical weight function proposed to reduce the
uncertainty of nij during the study period, mij is the total number
of endpoints in (i, j) with concentration value at the receptor site exceeding
a specified threshold value (the 75th percentiles for OC and EC each year
were used as threshold values to calculate mij), and nij is the number
of back-trajectory segment endpoints that fall into the grid cell (i, j) over
the period of study. The National Oceanic and Atmospheric Administration
Hybrid Single-Particle Lagrangian Integrated Trajectory model
(https://ready.arl.noaa.gov/HYSPLIT.php, last access: 26 June 2019) was used for calculating the 48 h
backward trajectories terminating at the study site at a height of 100 m
every 1 h from 1 March 2013 to 28 February 2018. In this study, the domain
for the PSCF was set in the range of (30–70∘ N, 65–150∘ E) with the grid cell size of 0.25∘×0.25∘.
Results and discussionLevels of OC and EC
Statistics for the OC and EC concentrations from 1 March 2013 to 28 February 2018 are summarized in Table 1. Benefiting from the Air Pollution
Prevention and Control Action Plan and increasing atmospheric
self-purification capacity (ASC, shown in Table S1 in the Supplement), a decline in annual
average concentrations is, on the whole, recorded. In detail, the annual
average concentrations of both OC and EC peaked in 2014 and then started to
decline gradually during the remainder of the study period. Nonetheless, the
annual average concentrations of PM2.5 generally decreased from
2013 to 2017. To assess whether the decreases are statistically significant,
two-tailed paired t tests were applied for OC, EC, and PM2.5 using their
monthly average concentrations in 2013 and 2016 as paired datasets. At a
confidence level of 98 %, from March to October, the paired data are
statistically different, indicating that the concentrations of OC, EC, and
PM2.5 declined during the above period from 2013 to 2016; however, the
concentrations of OC, EC and PM2.5 during November and February from
2013 to 2016 are not statistically different. The decline in OC and EC
concentrations is closely associated with decreasing coal consumption,
increasing usage of natural gases, and the implementation of a stricter
vehicular emission standard and increasing atmospheric self-purification
capacity (Tables S1–S3). Knowledge of the relative contribution of OC and EC
to PM2.5 is important in formulating effective control measures for
ambient PM (L. Wang et al., 2016). The ratios of OC and EC to PM2.5
varied little during the whole study period, suggesting that vehicular
emission might be an important contributor of OC and EC although several
other pollution sources also contributed to the OC and EC loadings. The
ratios of OC to PM2.5 ranged from 15.5 % to 17.8 % with the average of
16.4 %, while those of EC to PM2.5 ranged from 4.5 % to 5.2 % with
the average of 4.9 %. OC accounted, on average, for 77.0%±9.3 %
of the total carbon (TC, the sum of OC and EC), while EC amounted for 23.0%±9.3 % of the TC. These results are consistent with those in
previous studies (L. Wang et al., 2016; Tao et al., 2017; Lang et al., 2017).
The contribution of TC to PM2.5, 21.3%±15.8 %, is also
similar to that reported in previous studies, listed in Table S4, for
example, at urban sites of Hong Kong, China (23.5 %–23.6 % in 2013), and Hasselt
(23 %) and Mechelen (24 %) in northern Belgium, rural sites in Europe
(19 %–20 %), and some sites in India (on average 20 %; Bisht et al.,
2015; Ram and Sarin, 2010, 2012), but lower than those
observed historically at multiple sites in China (on average 27 %; L. Wang
et al., 2016), with Beijing (27.6 %, from March 2005 to February 2006),
Chongqing (28.3 %, from March 2005 to February 2006), Shanghai (34.5 %, from March 1999 to May 2000), and Guangzhou (26.4 %, December 2008
to February 2009), in Budapest (40 %), Istanbul (30 %), and many sites
in the USA, like Fresno (43.2 %), Los Angeles (36.9 %) and
Philadelphia (33.3 %) (Na et al., 2004). Compared to
previous studies in Beijing (Table S4), the TC-to-PM2.5 ratio became
smaller in this study, indicating a relatively lower contribution from
carbonaceous aerosols to PM2.5 in this study. The difference in the
TC/PM2.5 ratio could be ascribed to two factors. One factor is the
difference in characteristics of sampling locations between that in our
study and those in the earlier studies. However, our site and those in the
previous studies used for comparison are all located in urban areas of
Beijing (Chaoyang and Haidian districts). It is reasonable to
assume that they are affected by common sources since the surrounding
environments exhibit similar features. In addition, the annual average
PM2.5 concentrations in both districts published by the Ministry of
Environmental Protection, China (http://106.37.208.233:20035/, last access: 26 June 2019), were quite
comparable to each other from 2013 to 2017 (y=0.99x, r2=0.92),
indicating that both areas had particle pollution of a similar degree. The
other factor is that the contribution from secondary inorganic ions to the
PM2.5 became more important because of a stronger atmospheric oxidation
capacity (the annual average O3 concentrations were 102, 109, 116, 119,
and 136 µgm-3, respectively, from 2013 to 2017 in the
Beijing–Tianjin–Hebei region; published by http://106.37.208.233:20035/, last access: 26 June 2019), which could give rise to a lower TC-to-PM2.5 ratio. A higher TC-to-PM2.5 ratio suggests that there is a
lower contribution from secondary inorganic ions to PM2.5, while a
lower ratio may indicate a larger contribution from secondary inorganic ions
to PM2.5. The carbonaceous aerosol (the sum of multiplying the measured
OC by a factor of 1.4 and EC) represented, on average, 27.7%±16.7 %
of the observed PM2.5 concentration, making it a dominant contributor
to PM2.5.
Medians, averages, and associated standard deviations for the OC, EC,
and PM2.5 concentrations (in micrograms per cubic metre) and averages for the
OC/PM2.5, EC/PM2.5, and TC/PM2.5 ratios from March 2013 to
February 2018.
Mean or median OC and EC mass concentrations (in micrograms per cubic metre) observed in major megacities of the world published in the literature and obtained in this study.
MegacitiesMethodPeriodNumber or frequency of samplingOCECLiteratureAthensTOTMay 2008 to April 2013Once every day2.10.54Paraskevopoulou et al. (2014)BeijingTOTMarch 2017–February 2018Hourly7.72.6This studyHong KongTORFrom July to October 2014 and December 2014 to March 2015N=1617.82.2Chen et al. (2018)LhasaTORMay 2013 to March 2014Once eachweek3.272.24Li et al. (2016)Los AngelesTOTMarch 2017–February 2018Once every 3 d2.880.56U.S. EPA*MexicoTOTMarch 2006Hourly5.4–6.40.6–2.1Yu et al. (2009)MumbaiTOTMarch–May 2007, October–November 2007 and December–January 2007–200815 d in a season20.4–31.35.0–9.2Villalobos et al. (2015)New DelhiTORJanuary 2013–May 2014N=9517.710.3Sharma and Mandal (2017)New YorkTOTMarch 2017–February 2018Once every 3 d2.880.63U.S. EPA*ParisTOTFrom 11 September 2009 to 10 September 2010Once every day3.01.4Bressi et al. (2013)São PauloTOT2014Once eachTuesday10.27Pereira et al. (2017)SeoulTOTFrom January 2014 to December 2014Hourly4.11.6Park et al. (2015)ShanghaiTOTFrom July 2013 to June 2014Hourly8.43.1Xu et al. (2018)TianjinTORFrom 23 December 2013 to 16 January 2014N=2530.538.21Wu et al. (2015)TokyoTOTFrom 27 July to 15 August 2014Once every day2.20.6Miyakawa et al. (2016)TorontoTOT1 December 2010–30 November 2011Hourly3.390.5Sofowote et al. (2014)WuhanTOTFrom August 2012 to July 2013Once every 6 d16.92.0Zhang et al. (2015)Xi'anTOR4 months of 2010N=5618.66.7Wang et al. (2015)
Table 2 lists recently published results for OC and EC mass concentrations
in major megacities. Although the observation periods were not same, a
comparative analysis of OC and EC concentrations between different
megacities could show the status of energy consumption for policymakers,
drawing lessons and experience from other countries. It is obvious that the
PM2.5-associated OC and EC levels in the megacities in the developing
countries were far higher than those in the developed countries. The
PM2.5-associated OC and EC concentrations in Beijing were higher than
those in Athens, Greece (2.1 and 0.54 µgm-3); Los Angeles (2.88
and 0.56 µgm-3) and New York (2.88 and 0.63 µgm-3), USA;
Paris, France (3.0 and 1.4 µgm-3); Seoul, South Korea (4.1 and 1.6 µgm-3); Tokyo, Japan (2.2 and 0.6 µgm-3); and Toronto,
Canada (3.39 and 0.5 µgm-3). That is because clean energy has
been widely used and strict control measures are taken to improve the air
quality step by step in developed countries. Of the megacities in the
developing countries, OC and EC concentrations in Beijing were lower than
those in most other megacities, like Mumbai and New Delhi, India, and Xi'an
and Tianjin, China, but close to those in Shanghai and Hong Kong, China, and
higher than those in Lhasa, China. These differences/similarities indicate
that OC and EC gradually declined in Beijing and that a series of measures
had progressive effects. However, to further improve the air quality, more
synergetic air pollution abatement measures of carbonaceous aerosols and
volatile organic compound (VOC) emissions need to be performed.
OC/EC ratios in main domestic and foreign cities.
CitiesPeriodMethodOC/ECReferencesDomestic citiesBeijing1999–2000TOR2.7He et al. (2001)2000TOT7.0Song et al. (2006)2001–2002EA2.6Duan et al. (2006)2005–2006TOT3.0Yang et al. (2011b)2008TOT2.2Yang et al. (2011a)2008–2010TOR4.4Hu et al. (2015)2009–2010TOR2.9Zhao et al. (2013)2009–2010TOT3.4Zhang et al. (2013)2012–2013TOT7.0Z. S. Wang et al. (2016)2013TOT5.0Ji et al. (2018)2014TOT4.8Ji et al. (2018)2013TOT3.6This study2014TOT3.0This study2015TOT3.0This study2016TOT3.0This study2017TOT2.9This studyBaojiMarch 2012–March 2013TOR5.3Niu et al. (2016)Chengdu2009–2010 annualTOR2.5Tao et al. (2013)2009–2013TOR4.4Shi et al. (2016)2011 annualTOR2.4Tao et al. (2014)2012–2013 annualTOT4.1Chen et al. (2014)Chongqing2005–2006 annualTOR4.7Yang et al. (2011b)2012–2013 annualTOT3.8Chen et al. (2014)May 2012–May 2013TOT3.6Y. Chen et al. (2017)Ya'anJune 2013–June 2014TOT13.3Li et al. (2018)Hangzhou2004–2005 annualEA2.0G. Liu et al. (2015)Hong KongJuly–October 2014 andTOR3.5Chen et al. (2018)December 2014–March 2015LhasaMay 2013– March 2014TOR1.5Li et al. (2016)Nanjing2014 annualTOT1.8D. Chen et al. (2017)2011–2014 annualTOR2.6Li et al. (2015)Ningbo2009–2010 annualTOR2.8Liu et al. (2013)Neijiang2012–2013 annualTOT4.5Chen et al. (2014)QinglingMarch 2012–March 2013TOR6.3Niu et al. (2016)Shanghai2009 annualTOR3.4Zhao et al. (2015a)2011TOT2.6Chang et al. (2017)2012TOT2.9Chang et al. (2017)2012 annualTOR5.4Zhao et al. (2015b)2013TOT3.4Chang et al. (2017)ShijiazhuangFour seasons (2009–2010)TOR2.7Zhao et al. (2013)Tianjin2009–2010TOR2.7Zhao et al. (2013)Xi'an2010 annualTOR2.7Wang et al. (2015)March 2012–March 2013TOR4.0Niu et al. (2016)March 2012–March 2013TOR4.0Niu et al. (2016)March 2012–March 2013TOR3.8Niu et al. (2016)December 2014–November 2015TOT10.4Dai et al. (2018)WeinanMarch 2012–March 2013TOR4.4Niu et al. (2016)WuhanFrom August 2012 to July 2013TOT8.5Zhang et al. (2015)Foreign citiesAthensMay 2008–April 2013TOT3.9Paraskevopoulou et al. (2014)Los AngelesMarch 2017–February 2018TOT5.1U.S. EPA*New DelhiJanuary 2013–May 2014TOR1.7Sharma and Mandal (2017)New YorkMarch 2017–February 2018TOT4.6U.S. EPA*Paris11 September 2009–10 September 2010TOT2.1Bressi et al. (2013)São Paulo2014TOT1.5Pereira et al. (2017)SeoulJanuary 2014–December 2014TOT2.6Park et al. (2015)Tokyo27 July–15 August 2014TOT3.7Miyakawa et al. (2016)Toronto1 December 2010–30 November 2011TOT6.8Sofowote et al. (2014)
Figure 2 shows the mass fractions of carbonaceous aerosols in different
PM2.5 levels classified according to PM2.5 concentrations during
the whole study period. There were 571, 561, 310, 169, 142, and 74 d for
excellent, good, lightly polluted (LP), moderately polluted (MP), heavily polluted (HP),
and severely polluted (SP) air quality levels during the whole period. The criteria used to classify the air quality were as follows: excellent, good, LP, MP, HP, and SP were based on the daily average PM2.5 concentration, i.e., excellent (0< PM2.5≤35µgm-3), good (35< PM2.5≤75µgm-3), lightly polluted (75< PM2.5≤115µgm-3), moderately polluted (115< PM2.5≤150µgm-3), heavily polluted (150< PM2.5≤250µgm-3), and severely polluted (PM2.5>250µgm-3), respectively. It was
obvious that OC and EC concentrations increased with the degradation of air
quality. OC and EC concentrations were 6.3 and 1.7, 10.2 and 2.9, 13.7 and
4.1, 17.3 and 5.3, 24.6 and 7.9, and 35.5 and 11.3 µgm-3 for
excellent, good, slightly polluted, moderately polluted, heavily
polluted, and severely polluted air quality days, respectively. However, the percentages of OC and EC
accounting for PM2.5 decreased with the deterioration of air quality. OC
and EC made up 31.5 % and 8.3 %, 18.9 % and 5.4 %, 14.7 %
and 4.4 %, 13.4 % and 4.1 %, 12.9 % and 4.2 %, and 11.4 %
and 3.6 % during excellent, good, slightly polluted, moderately polluted,
heavily polluted, and severely polluted air quality days, respectively. The
percentage for OC decreased from 31.5 % to 11.4 % while that for EC
decreased from 8.3 % to 3.6 % with the deterioration of air quality,
indicating that PM2.5 constituents other than OC and EC contributed
more to the increased PM2.5 levels. This is consistent with previous
studies showing that secondary inorganic ions play a more important role in
the increase in PM2.5 concentrations (Ji et al., 2014, 2018).
Variation in average OC, EC, and PM2.5 concentrations (a) and
in the percentages of OC, EC, and other components in PM2.5(b) for
different air quality levels.
Inter-annual variation in OC and EC
To evaluate the effect of the clean air act over a prolonged period, our OC
and EC data were combined with the data of previous studies for Beijing (He
et al., 2011; Zhao et al., 2013; Ji et al., 2016; Tao et al., 2017; Lang et
al., 2017). As shown in Fig. 3, a decreasing trend in OC and EC
concentrations is, on the whole, observed. Table S2 summarizes a variety of
policies and actions to reduce pollutant emissions in power plants,
coal-fired boilers, residential heating, and traffic areas in Beijing since
2002. Although the gross domestic product, population, energy consumption,
and vehicular population rapidly increased (Table S3), the general
decreasing trends in OC and EC concentrations could be attributed to the
combined effect of the more stringent traffic emission standards and traffic
restriction, the energy structure evolving from intensive coal and diesel
consumption to natural gas and electricity, and
retrofitting with SO2 and NO2 removal facilities to meet the new
emission standards applicable to different coal-fired facilities, etc. In
particular, there is an obvious dividing line of OC and EC concentrations in
2010. After 2010, the OC and EC concentrations became substantially lower
than those observed previously. In addition to the measures mentioned in
Table S2, the relocation of Shougang Group, which is one of
China's largest steel companies, and other highly polluting factories out of
Beijing might have helped to some extent; all the small coal mines in
Beijing were shut down and plenty of yellow label (heavily polluting) vehicles
were forced off road. Note that the OC and EC levels in 2008 and 2009 were
also somewhat lower, which was caused by a series of radical measures to
improve the air quality for the Olympic Games in 2008 and a decline in
industrial production because of China's export crash in 2009. This suggests that a stringent clean air act and improving
industry standards played important roles in the air quality improvement.
Variation in the annual mean OC and EC concentrations in PM2.5
from 2002 to 2018 in Beijing. The variation in NO2 and SO2
concentrations and in the number of fire spots counted for the domain of
(30–70∘ N, 65–150∘ E) is also shown.
In this study, the fire spots were counted in the domain of (30–70∘ N, 65–150∘ E) using the MODIS Fire Information for Resource
Management System (Giglio, 2013). Note also that the effective control of
biomass burning might contribute to the decrease in OC and EC
concentrations. In Fig. 3, it can be seen that the annual average EC
concentration and fire spot counts exhibit a rather similar variation from
2004 to 2017, except in the year 2012, which suggests that the EC levels are
somewhat correlated with the biomass burning; this might indicate that
biomass burning contributed somewhat to the EC levels. The reduction in fire
spot counts from 2014 to 2017, which resulted from efforts to control
agricultural field residue burning since 2013, helped to reduce the EC
concentrations to some extent, but the low EC levels during 2014–2017 are
likely mostly due to the implementation of the clean air act. With regard to
the anomaly in the year 2012, based on the MODIS data for this year, a very
non-uniform distribution of fire spots in the Beijing–Tianjin–Hebei (BTH) region was observed, with
a distinct decrease in fire spot counts in Beijing, but higher fire spot
counts in the southern Hebei Province; this may be ascribed to the fact that
the policy of banning straw burning in summer and autumn was executed to
different degrees in the whole region, with better implementation in the Beijing
area and worse action in other parts
(http://www.beijing.gov.cn/zfxxgk/110029/qtwj22/2012-12/11/content_357114.shtml, last access: 26 June 2019). In addition, for the years from 2002 to 2017, the highest
precipitation volume in Beijing was recorded in 2012, i.e. 733.2 mm, and
the rainy days mainly occurred in the intensive straw burning periods,
accounting for 76.4 % of all rainy days in 2012. Frequent wet
scavenging might have suppressed the EC concentrations during the intensive
straw burning periods, so that the annual EC level for 2012 was comparable
to that recorded from 2011 onward.
Similar to OC and EC, the annual mean SO2 and NO2 concentrations
also showed a decreasing trend. As is well-known, SO2 originates from coal
combustion and sulfur-containing oil (Seinfeld and Pandis, 1998). With the
replacement of coal for industrial facilities, residential heating, and
cooking by clean energy (e.g. natural gases, electricity, and lower sulfur
content in oil), a clear decline in annual SO2 concentrations was
observed in the Beijing area starting from 2002. Compared to SO2,
the annual decreasing rate of NO2 was relatively lower. In addition to the
power plants and other boilers, traffic emissions are another major source
of NO2. The rapid increase in vehicle population may partly offset the
great effort in reducing coal consumption to lower NO2 levels
despite the transition to more stringent traffic emission standards.
Monthly and seasonal variations
Figure S1 shows the monthly mean OC and EC concentrations at our study site
for the whole 5-year period. Similar variations are observed with generally
higher mean OC and EC levels in the cold season (from November to February
next year when the centralized urban residential heating is provided) and
lower ones in the warm season (from April to October). The highest average
OC and EC concentrations were 24.1±18.7µgm-3 in December 2016 and 9.3±8.5µgm-3 in December 2015, respectively.
However, the lowest OC and EC levels were not observed in the warm months;
they were 5.0±4.6µgm-3 in January 2018 and 1.5±1.7µgm-3 in December 2017; this was associated
with both frequent occurrence of cold air mass and the implementation of a
winter radical pollution control action plan (Chen and Chen, 2019) in
Beijing from November 2017. Overall, the increased fuel consumption for
domestic heating in addition to unfavourable meteorological conditions (lower
mixing layer height, temperature inversion, and calm wind) in the colder
months is considered to lead to higher OC and EC levels (Ji et al., 2014).
In addition, the lower air temperature in the cold months led to shifting
the gas–particle equilibrium of semi-volatile organic compounds (SVOCs) into
the particle phase, leading to higher OC levels. In the cold months, the
cold start of vehicles (5.64 million vehicles in Beijing at the end of 2017)
also increased the emission of OC. In the warm season, lower OC and EC
levels were observed, which could be attributed to the following factors: no
extra energy consumed for domestic heating, strong wet scavenging by
frequent precipitation occurring in these months, and more unstable
atmospheric conditions favourable for pollutant dispersion; in addition,
during this period, the monthly mean OC and EC concentrations generally
decreased from year to year. In contrast, for the cold season, the monthly
mean OC and EC concentrations did not show a clear decreasing trend from
year to year. In addition to more intensive energy consumption in the
cold season, the EC and OC levels could also be enhanced strongly by
regional transport and stagnant meteorology, leading to ground surface
accumulation in the autumn and winter (Wang et al., 2019; Yi et al., 2019);
this would have counteracted the efficacy of the energy structure change in
the Beijing–Tianjin–Hebei region in the past few years. It is worth pointing
out that, on a year-to-year basis, the monthly average OC and EC
concentrations in the cold seasons of 2017 and 2018 were generally lower
than those in 2016, demonstrating to some extent the effectiveness of the
execution of the radical pollution control measures for cities in the Beijing–Tianjin–Hebei region. The interquartile ranges of
OC and EC in the warm months were narrower than in the cold months,
indicating that there was more substantial variation in concentration in the
latter months. The larger variation in the colder months could be caused by
the cyclic accumulation and scavenging processes. In this region, due to
these processes, the concentration of particulate matter increases rapidly
when the air mass back trajectories change from the northwest and north to
the southwest and south over successive days in Beijing; in contrast, the
concentration of particulate matter declines sharply when a cold front
causes a shift of back trajectories from the southwest and south to the
north and northwest (Ji et al., 2012). The accumulation processes are
closely associated with unfavourable meteorological conditions, which give
rise to higher OC and EC concentrations, while more scavenging of aerosols
by cold fronts leads to lower levels.
As for the seasonality in OC and EC, similar seasonal variations are observed
in the various years with generally higher mean concentrations in autumn and
winter and lower levels in spring and summer (Fig. 4). Remarkably, the OC
and EC concentrations in the autumn and winter of 2017 were lower than those
in the previous years. This was due to the combined effect of strictly controlling
anthropogenic emissions and favourable meteorological conditions.
Since September 2017, a series of the most stringent measures within the
Action Plan on Prevention and Control of Air Pollution was implemented to
improve the air quality; these measures included restricting industrial
production by shutting down thousands of polluting plants, suspending the
work of iron and steel plants in 28 major cities, and limiting the use of
vehicles and reducing coal consumption as a heating source in northern
China. In addition, air quality improvement in the autumn and winter of
2017 was closely tied to frequent cold fronts accompanied by strong winds,
which was favourable for dispersing the pollutants. The average OC and EC
concentrations in the winter were 1.69 and 1.14, 2.17 and 1.93, 1.49 and
2.14, 2.41 and 2.29, and 0.80 and 0.88 times higher than those in the summer
for 2013, 2014, 2015, 2016, and 2017, respectively. The difference in the
ratios for 2017 was due to the series of the most stringent measures taking
effect and favourable meteorology. The Beijing municipal government in
particular has made great efforts to replace coal with natural gases and
electricity-powered facilities. In addition, “new energy vehicles” are
increasingly used to replace gasoline vehicles.
Seasonal variations in OC and EC concentrations from March 2013 to
February 2018. The mean (square in the box), median (horizontal line in the box), 25th and 75th percentiles (lower and upper ends of the box), 10th and 90th percentiles (lower and upper whiskers), and maximum and minimum (hollow circles) are shown.
Diurnal variation and weekly pattern for OC and EC
As can be seen in Figs. S2 and S3, a clear diurnal variation is observed for
both OC and EC in each year. This variation is closely tied to the combined
effect of diurnal variation in emission strength and evolution of the planetary boundary layer (PBL).
The pattern for EC with higher concentrations in the nighttime and lower levels in the daytime (from 09:00 to 16:00) is largely
linked to vehicular emissions. The EC concentrations increased starting
from 17:00, corresponding with the evening rush hour, emission from
nighttime heavy-duty diesel trucks (HDDTs) and heavy-duty vehicles (HDVs) and
the formation of a stable nocturnal PBL. As regulated by the Beijing Traffic
Management Bureau (http://www.ebeijing.gov.cn/feature_2/BeijingDrivingLicenseApplication, last access: 26 June 2019), HDVs and HDDTs
are allowed to enter the urban area inside the 5th Ring Road from 00:00
to 06:00. At other times, both the higher PBL height and lower
traffic intensity resulted in lower EC concentrations. The amplitude of the
diurnal variation in the EC concentrations was smaller in the last 3
years. The maximum peak concentration (22:00–07:00) was 1.68, 1.62, 1.43,
1.40, and 1.40 times higher than that observed in the low period
(13:00–15:00) for 2013, 2014, 2015, 2016, and 2017, respectively. Similar to
EC, the diurnal pattern for OC was also characterized by higher
concentrations in the nighttime (from 20:00 to 04:00) and lower levels in the
daytime (from 14:00 to 16:00). However, the formation of secondary organic
carbon from gas-phase oxidation of VOCs with increased solar radiation
during midday gave rise to a small additional peak of OC. Like for EC, the
amplitude of the diurnal variation in the OC concentrations was smaller in
the last 3 years. The maximum peak concentration (19:00–03:00) was 1.47,
1.47, 1.30, 1.34, and 1.26 times higher than that observed in the low
period (14:00–16:00) for 2013, 2014, 2015, 2016, and 2017, respectively. Unfortunately no diurnal variation in traffic counts is available but
the hourly average traffic counts in 2015, 2016, and 2017 could be found in the
Beijing Transportation Annual Report,
http://www.bjtrc.org.cn/List/index/cid/7.html (last access: 26 June 2019). Considering that
the hourly average traffic counts varied little in urban Beijing and they
were 5969, 5934, and 6049 h-1 in 2015, 2016, and 2017, respectively, the
small amplitude of the diurnal variation in the last 3 years might be
related to local emission intensities; these might have been significantly
affected by the enforcement of a series of traffic emission control measures
since 2015, including more strict restriction of emission from heavy-duty
diesel vehicles, public buses, wider usage of electric public buses, and
scrappage of all the high-emitting (yellow-labelled) vehicles (Table S2). All these actions led to a decline in emissions of OC and EC and
narrowed the amplitude of the diurnal variation in the EC concentration.
Separate diurnal variations in OC and EC for each season in each year are
shown in Figs. S4 and S5. Similar patterns are observed in the four
seasons but the difference between peak and valley levels is larger in the
winter than in the other three seasons. The larger variation in the winter
is due to the additional emission from residential heating and more
unfavourable meteorological conditions (Ji et al., 2016).
The difference in diurnal pattern between weekdays and weekends was also
examined; see Figs. S6 and S7. Similar diurnal variations are found on
weekdays and weekends. The maximum peak concentration for EC
(22:00–07:00) was 1.55, 1.43, 1.55, 1.51, 1.51, 1.46, and 1.59 times higher
than the low concentration (13:00–15:00) for Monday, Tuesday, Wednesday,
Thursday, Friday, Saturday, and Sunday, respectively, while the maximum peak
concentration for OC (19:00–03:00) was 1.41, 1.32, 1.38, 1.43, 1.37, 1.31, and
1.43 times higher than the low concentration (14:00–16:00) for Monday,
Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, respectively. In
contrast to previous studies (Grivas et al., 2012; Jeong et al., 2017; Chang
et al., 2017), OC and EC exhibited statistically significant higher
concentrations on weekends than on weekdays in this study (statistically
significant based on the analysis of the weekly data using t test statistics,
p<0.05). The average OC and EC concentrations on Saturday and Sunday
were 13.2±11.8 and 3.9±2.7
and 12.0±10.4 and 3.7±3.6µgm-3,
respectively, whereas the average OC and EC levels during the weekdays were
11.8±10.8 and 3.6±3.5µgm-3,
respectively. This indicates that there is no significant decline in
anthropogenic activity in the weekends compared to weekdays. In fact,
enhanced anthropogenic emissions could be caused by no limit on driving
vehicles based on license plate on weekends. The larger OC and EC
concentrations on the weekend are thus mainly attributed to enhanced traffic
emissions, which is consistent with higher NO2 and CO concentrations on
the weekend (on average 56.6±35.9µgm-3 for NO2 and
1.16±1.18 mg m-3 for CO on weekdays (number of samples =29492); 57.8±37.0µgm-3 for NO2 and 1.25±1.18 mg m-3 for CO on weekends (number of samples =11881)).
Relationship between OC and EC and with gaseous pollutants
The relationship between particulate OC and EC is an important indicator
that can give information on the origin and chemical transformation of
carbonaceous aerosols (Chow et al., 1996). Primary OC
and EC are mainly derived from vehicular emissions, coal combustion, biomass
burning, etc. in urban areas (Bond et al., 2013). Primary OC and EC could
correlate well with each other under the same meteorology. However, the
correlation would become gradually less significant with the enhancement of
secondary OC formation via complex chemical conversion of VOCs
(gas-to-particle or heterogeneous conversion). In addition, it should be
noted that EC is more stable than OC (Bond et al., 2013). Hence, the
relationship between OC and EC can to some extent be used as a parameter
reflecting the source types and contributions (Blando and Turpin, 2000).
Figure 5 presents the regression between the OC and EC concentrations for the
PM2.5 samples of the separate years 2013 to 2017. Significant
correlations (R2 ranging from 0.87 to 0.66) were observed with the
slopes declining from 3.6 to 2.9 throughout the study period. The
significant correlations suggest that in most cases OC and EC originated
from similar primary sources. The slopes are consistent with the OC/EC
ratios ranging from 2.0 to 4.0 for urban Beijing in previous studies (He et
al., 2001; Dan et al., 2004; Zhao et al., 2013; Ji et al., 2016). In
addition, the average OC/EC ratios observed in this study are comparable to
those observed at other urban sites with vehicular emission as a dominant
source in China and foreign countries, but lower than those in cities where
coal is an important source of energy (Table 3). The decline in
the OC/EC ratio may be caused by decline in coal consumption and restrictions on biomass burning. Coal combustion, biomass burning, and secondary formation
give rise to higher OC/EC ratios while vehicular emissions result in lower
ratios (Cao et al., 2005).
Relationship between OC and EC using the Deming regression method
from 2013 to 2017 (the dashed line indicates a OC/EC ratio of 3:1).
EC and part of OC originate from primary anthropogenic emissions,
including fossil fuel combustion and biomass burning (Bond et al., 2013),
and secondary OC is formed along with ozone formation. Hence, long-term and
concurrent measurement of OC, EC, SO2, NOx, CO, and O3 is
helpful for understanding emission features or formation processes and
for providing tests to current emission inventories. The variation in OC
and EC as a function of the SO2, NOx, CO, and O3 concentration
is shown in Fig. 6. There is a clear increase in OC and EC with increasing
SO2, NOx, and CO, suggesting that the latter played a role in the
enhancement of the former and that these various species shared common
sources although they have a different lifetime. OC and EC increased, on
average, by approximately 8.9 and 5.7 µgm-3,
respectively, with an increase of 2 mg m-3 in CO. Considering
that CO has a long lifetime (Liang et al., 2004) and that its
increase depends on source strength and meteorology, high CO concentrations
usually occur in the heating season when unfavourable meteorological
conditions prevail. At very high CO concentrations, the increase in OC
becomes slower than that in EC. This can be explained by local
emissions becoming dominant because unfavourable meteorological conditions
corresponding with high CO concentrations resulted in a weak exchange of
air on the regional scale. The OC/EC ratio declined at very high CO
concentrations. This could be because vehicular emissions played an
important role in the OC and EC loadings (Ji et al., 2019). As documented by
previous studies (Schauer et al., 2002; Na et al., 2004), emission of
gasoline vehicles results in an OC/EC ratio varying from 3 to 5 while that
of diesel vehicles is below 1. The above results are consistent with
previous studies which showed that gasoline and diesel vehicles give rise to
higher CO emissions (Wu et al., 2016).
OC and EC concentrations as a function of the SO2, CO, NOx,
and O3 concentration.
Given that NOx and CO have some common emission sources
(Hassler et al., 2016), the OC and EC levels were also
analysed in different intervals of NOx concentrations. Both OC and EC
are enhanced with increasing NOx concentrations. Their enhancements
were 5.0 and 2.1 µgm-3, respectively, for an
increase in NOx concentration of 40 µgm-3. Although NOx
is highly reactive and has a short lifetime (Seinfeld and Pandis, 1998) in
contrast to CO, the OC/EC ratio also declined at very high NOx concentrations, albeit to a lesser extent than was the case at very high CO
concentrations. As was the case for high CO concentrations, more stable
meteorological conditions and local emissions prevailed when higher
concentrations of NOx were observed. In fact, 63.5 % of all NOx
emissions come from vehicular emissions based on the statistical data of air
pollutant emissions in Beijing (http://www.bjepb.gov.cn/bjhrb/xxgk/ywdt/zlkz/hjtj37/827051/index.html, last access: 26 June 2019).
Examining the variation in OC and EC for different intervals of SO2
concentrations allows us to further study the impacts of industrial
production or coal combustion on the OC and EC levels. Similar to the
relationship between CO and carbonaceous species, the OC and EC
concentrations enhanced with increasing SO2 concentrations. Their
enhancements were 2.8 and 0.7 µgm-3, respectively,
for an increase in SO2 concentration of 10 µgm-3. An increase
in the OC/EC ratio occurred at large SO2 concentrations, suggesting
that coal consumption provided a substantial contribution to the OC and EC
levels in Beijing. Because oil with a low sulfur content has been widely
used in Beijing since 2008 and little coal was used in the urban areas of
Beijing, the SO2 mostly originated from industrial production in the
surrounding areas of Beijing and from coal combustion for residential
heating in the suburban and rural areas of Beijing. Previous studies also
showed that a higher OC/EC ratio is due to coal consumption and not from
vehicular emissions (Cao et al., 2005). Hence, coal combustion (for
industrial production) on the regional scale led to the enhancement of both
the OC/EC ratio and SO2 concentrations in Beijing via long-range
transport.
Emissions of primary air pollutants lead through multiple pathways to the
formation of ozone and secondary organic carbon (SOC) (Seinfeld and Pandis,
1998), both of which are the principal components of photochemical smog. The
relationship between OC and O3 is of use for understanding their
variation and formation. The OC concentrations were highest for an O3
concentration of 50 µgm-3, which is approximately the average
O3 concentration in Beijing in winter (Cheng et al.,
2018). During the period of an O3 concentration of 50 µgm-3,
low atmospheric temperature (9.4±9.9∘C), relatively
high RH (59.2±23.7 %), lower WS (1.1±0.8 m s-1), and higher
NOx concentrations (72.7±57.5 ppb) were observed and a lower
mixed layer height was recorded in winter (Tang et al., 2016), which were
favourable for accumulation and formation of OC. A relatively lower
temperature is beneficial for condensation/absorption of SVOCs into existing
particles (Ji et al., 2019), which would then experience further chemical
reactions to generate secondary organic aerosol (SOA). Note that a low
temperature does not significantly reduce SOA formation rates (Huang et al.,
2014) in the winter. In addition, processes including aqueous-phase
oxidation and NO3-radical-initiated nocturnal chemistry may contribute
to or even dominate SOA formation during winter (Hallquist et al., 2009;
Rollins et al., 2012; Huang et al., 2014). Hence, the above factors gave
rise to the higher OC concentration at an O3 concentration of 50 µgm-3, particularly in winter. In addition, scattering and absorbing
effects of aerosols that were trapped in the lower mixed layer height led to
less solar radiation reaching the ground and further restrained the O3
formation in the cold season (Xing et al., 2017; J. Wang et al., 2016). OC
declined when O3 concentrations increased from 50 to 100 µgm-3. Usually moderate O3 concentrations accompanying lower OC
concentrations are caused by increasing T (19.5±8.3∘C), increasing WS (2.0±1.3 m s-1), and less titration of relatively
lower observed NO concentrations (6.4±14.6 ppb). It can also be seen
that there was a concurrent increasing trend of OC and ozone when the
O3 concentration was above 100 µgm-3, which generally
occurred in the warmer season. In addition to the impact of meteorological
conditions, such a trend might not be dominated by gas-to-particle
partitioning of low-volatility organic compounds but by the oxidation of
VOCs driven by hydroxyl radicals to generate both SOC and O3 with
relatively long lifetimes (>12 h; Wood et al., 2010).
Impact of atmospheric transport on the OC and EC concentrations
Figures 7 and 8 show the results of the NWR analysis applied to 1 h
PM2.5-associated OC and EC concentrations measured from 2013 and 2017
in Beijing. Figure S8 presents the gridded emissions of OC and BC for the
Beijing–Tianjin–Hebei (BTH) region and China, based on emission inventories
(Zheng et al., 2018). The NWR results exhibit distinct hot spots (higher
concentrations) in the northeast wind sector at wind speeds of approximately
0–6 km h-1, which were closely associated with local emissions under stagnant
meteorological conditions (low wind speed), as well as diffuse signals in
the southwestern wind sector. The joint probability data in Figs. 7 and 8
show prevailing winds were from N to E and from S to W with wind speeds of
approximately 1–6 km h-1 and of approximately 4–9 km h-1, respectively. Note
further that the hot spots of OC are broader than those of EC in the graphs
of estimated concentrations; this might be due to the fact that VOCs
(the precursors of SOC) emitted from upwind areas at a relatively higher
WS in contrast to EC, including the SW wind sector, led to an increase in OC
concentrations at the receptor site while the EC concentrations slowly
decreased due to dilution and deposition.
Wind analysis results using NWR on 1 h OC concentrations measured in
Beijing from 2013 to 2017 (wind speed in kilometres per hour).
Wind analysis results using NWR on 1 h EC concentrations measured in
Beijing from 2013 to 2017.
Considering that the NWR analysis can only provide an allocation of local
sources, the PSCF analysis is a helpful complement to investigate potential
advection of pollution over larger geographical scales (Petit et al., 2017).
Figure 9 presents the PSCF results for OC and EC for the years 2013 to 2017.
Similar to the NWR analysis, the PSCF results indicated that local emissions
and regional transport from southerly areas were important contributors to
the OC and EC loadings during the whole study period. Only slight
differences in the potential source regions are observed between the
different years. In 2013, a clear high potential source area was recorded
for both OC and EC; it was located in the southern plain areas of Beijing,
particularly in the adjacent areas of the Hebei, Henan, Shandong, Anhui, and
Jiangsu provinces. This was because there were intensified anthropogenic
emissions from these provinces in 2013. The high pollutant emissions were caused by
rapid economic growth, urbanization, and an increase in vehicle population,
energy consumption, and industrial activity in the southern plain areas of
Beijing (Zhu et al., 2018), which resulted in a high
aerosol loading in the downwind areas. This result is consistent with
previous studies (Ren et al., 2004; Wu et al., 2014; Ji et al., 2018). In
contrast to 2013, in the years 2014 to 2017 the high potential source
regions for OC and EC stretched to the juncture of Inner Mongolia and the
Shaanxi and Shanxi provinces, and even to the juncture of Inner Mongolia and
the Ningxia Hui Autonomous Region and Inner Mongolia and Gansu
Province. This is consistent with coal power plants being abundant in the
above areas (F. Liu et al., 2015). As is well-known, coal power plants are also
important emitters of SO2, and those emissions were seen in satellite
images (Li et al., 2017; Zhang et al., 2017), thus providing evidence for
those sources. The potential source areas for OC and EC were similar in 2013
and 2014. Overall, the potential source areas were more intense for OC than
for EC. The emission of OC precursors (i.e. volatile organic compounds)
from the Hebei, Henan, Shandong, Anhui, Jiangsu, Shanxi, and Shaanxi
provinces led to OC concentrations downwind via chemical conversion during atmospheric transport. The widest potential source areas for OC and EC
were recorded in 2016 and they expanded into the eastern areas of Xinjiang
Uyghur Autonomous Region. They are probably associated with the economic
boom in western areas of China. In 2015, the potential source areas were,
like in 2013 and 2014, also more intense for OC than for EC. Although the
winter action plan was enforced in Beijing, Tianjin, and 26 surrounding
cities (the so-called “2+26 cities”), whereby the industrial output was
curtailed, inspections of polluting factories were ramped up and small-scale
coal burning was banned at the end of 2017, there was still a clear spatial
difference in emission of air pollutants, with relatively higher PM2.5
concentrations in the southern areas of Beijing. Hence, these areas still
contributed substantially to OC and EC loading in Beijing.
Potential source areas for OC and EC in Beijing from 2013 to 2017.
The colour code denotes the PSCF probability. The measurement site is
indicated with a red circle. The identification of the provinces is given in Fig. S9.
As found in earlier studies (Ji et al., 2018; Zhu et al., 2018), the
southern areas of Beijing were main source areas. Despite the ever-stringent
air pollution control measures, which are enforced in key areas of China,
the economic boom in the western areas of China gave rise to substantial air pollution, and high emissions were also recorded in the adjacent areas of several provinces and the northwestern areas of China. To further improve the air quality in Beijing,
strict emission restrictions should be launched in the above areas and joint
control and prevention of air pollution should be enforced on the regional
scale. It should be avoided that polluted enterprises, which are closed in
key regions, are moved to the western areas of China or to areas where there
is no supervision and control of the emission of air pollutants.
Conclusions
In this study, hourly mass concentrations of OC and EC in PM2.5 were
semi-continuously measured from 1 March 2013 to 28 February 2018 at a
study site in Beijing. The inter-annual, monthly, seasonal, and diurnal
variations in OC and EC are presented, the relationship between the
carbonaceous species and other pollutants was examined, and the source
regions were assessed using both NWR and PSCF analysis. The impact of the
air pollution control measures and of the regional transport on carbonaceous
species in PM2.5 was investigated. The following main
conclusions can be drawn.
OC and EC occupied a high fraction of the observed PM2.5 concentrations, making it a dominant contributor of PM2.5. Their
concentrations increased with degrading air quality whereas their
percentage in PM2.5 declined, which was consistent with previous
studies showing that secondary inorganic ions played a relatively more
important role in increasing PM2.5 concentrations.
A clear decline in OC and EC levels was observed after a series of
energy policies for air pollution abatement and control had been
implemented. To further improve air quality, more synergistic air pollution
abatement measures of carbonaceous aerosols and VOCs emissions are needed.
OC and EC showed marked seasonal, monthly, weekly, and diurnal
variations. The seasonal patterns were characterized by higher
concentrations in the colder months (from November to February) and lower
ones in the warm months (from April to October) of the various years. Because
of stringent measures for air pollution abatement, the difference between
the winter and summer levels decreased. The EC diurnal pattern was
characterized by higher concentrations in the nighttime
and lower ones in the daytime. The higher OC and EC
levels during the weekend can be attributed to the traffic regulation in
Beijing. The diurnal fluctuation in OC and EC was closely tied to a combined
effect of change in emission sources and evolution of the PBL.
Significant correlations between OC and EC were observed throughout the
study period, suggesting that OC and EC originated from common sources, such
as vehicle exhaust, coal combustion, etc. The contribution of coal
combustion and biomass burning decreased and this resulted in lower OC/EC
ratios. The OC and EC concentrations increased with higher SO2, CO, and
NOx levels, while the O3 and OC concentrations increased
simultaneously for O3 levels above 50 µgm-3.
Local emissions and regional transport played an important role in the
OC and EC concentrations. Higher concentrations were observed for winds from
the northeast sector at wind speeds of approximately 0–6 km h-1, but there were
also diffuse signals in the southwestern wind sectors. The potential source
regions of OC and EC stretched to the broader areas in northwestern and
western regions where coal and coal power plants are abundant. Some slight
differences in the potential source regions were observed from 2013 to 2017,
which was closely associated with the economic boom in the western areas of
China. In addition, the southern areas of Beijing still contributed a lot to
OC and EC loading in Beijing.
In summary, this study will be helpful for improving the understanding of
sources of OC and EC associated with PM2.5 and for assessing the
effectiveness of local and national PM control measures. In addition, it
provides valuable datasets for modelling studies and for assessing the
health risk.
Data availability
The data are available on request to the lead corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8569-2019-supplement.
Author contributions
DJ, WM, and YW designed the research. DJ, WM, JH, ZW, WG,
WD, YW, JX, BH, and YS performed the research. DJ, ZW, and WM
analysed the data. DJ, JH, and WM wrote and edited the paper. All
other authors commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
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 Key Research and Development Program
of China (2016YFC0202701 and 2017YFC0210000), the Beijing Municipal Science
and Technology projects (D17110900150000 and Z171100000617002), the CAS Key
Technology Talent Program, and the National Research Program for Key Issues
in Air Pollution Control (DQGG0101 and DQGG0102). The authors would like to
thank all members of the LAPC/CERN in IAP, CAS, for maintaining the
instruments used in the current study. We would also like to thank NOAA for
providing the HYSPLIT and TrajStat models.
Financial support
This research was supported by the National Key Research and Development Program of China (grant nos. 2016YFC0202701 and 2017YFC0210000), the Beijing Municipal Science and Technology projects (grant nos. D17110900150000 and Z171100000617002), the CAS Key Technology Talent Program, and the National Research Program for Key Issues in Air Pollution Control (grant nos. DQGG0101 and DQGG0102).
Review statement
This paper was edited by Jianmin Chen and reviewed by three anonymous referees.
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