Particulate matter (PM) pollution on the peripheries of Chinese megacities
can be as serious as in cities themselves. Given the substantial vehicular
emissions in inner-city areas, the direct transport of primary PM (e.g.,
black carbon and primary organics) and effective formation of secondary PM
from precursors (e.g.,
Traffic emissions are one of the main contributors to air quality
deterioration in rapidly expanding urban China (Kelly and Zhu, 2016; Zhang et
al., 2017). Pollutants emitted from vehicles – such as
The rapid economic development in the Pearl River Delta (PRD) region has led to a rapid deterioration in air quality, especially due to a sharp increase in PM (Chan and Yao, 2008; Ho et al., 2003; Li et al., 2017). With densely populated cities including two megacities, Guangzhou and Shenzhen, and other smaller cities, the PRD region is developing into a giant city cluster. There are, nevertheless, less populated areas between these cities that can serve as a “buffer” zone in terms of regional air quality. Due to air dispersion patterns and regulatory strategies, air pollutants in highly urbanized regions can greatly influence PM levels in peripheral regions. For example, highly polluting vehicles such as heavy-duty diesel trucks are banned from the inner areas of many Chinese megacities during the day but are nevertheless active at night and in the early morning, especially on the peripheries of these megacities. This regulatory policy has resulted in nighttime peaks in vehicular pollutants, which have been commonly observed in many Chinese cities (Zhang and Cao, 2015). Although vehicle emissions are substantially reduced relative to the without-control scenarios, there was still significantly higher emission density in east China than in developed countries with longer histories of vehicle emission control (Wu et al., 2016). Given the complex and nonlinear processes involved in the secondary production of PM such as nitrate and organic aerosols, the impacts of these adjacent buffer zones on air quality are crucial. However, relatively little attention has been paid to how air pollutants in major cities affect these adjacent areas.
Panyu district lies directly south of central Guangzhou and experiences
predominantly northerly/northeasterly winds between September and February (Zou et al., 2015). A campaign with a host of
real-time instruments including an Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) was conducted at a
site in Panyu from November to December 2014 and provides a unique
opportunity to explore how pollutants from the city center have impacted
this adjacent buffer zone, especially in terms of nitrate and secondary
organic aerosols. As will be shown, the mass fraction of nitrate increased
as PM
Guangzhou Panyu Atmospheric Composition Station (GPACS), a China
Meteorological Administration site, is located at the summit of Dazhengang
(23
In the HR-ToF-AMS measurements (DeCarlo et al., 2006), ambient air
was sampled through a PM
The AMS collected 5 min average particle mass spectra spanning from
AMS data analysis was performed using the SQUIRREL (v1.56D) and PIKA
(v1.15D) toolkits written in Igor Pro 6.37A (WaveMetrics Inc., Lake Oswego,
OR, USA). Default relative ionization efficiency (RIE) values of 1.2 for sulfate,
1.1 for nitrate, 1.3 for chloride and 1.4 for organics were used. The
ammonium RIE of 4.7 was chosen as the average from IE calibrations. A
particle collection efficiency factor (CE) of 0.7 was used to account for
particle losses within the instrument. The influence of RH in this study was
minor, as a diffusion drier was used to maintain the sampling line RH
consistently below 30 %. Under these conditions, Middlebrook's
parameterization suggests a CE of
Source apportionment for OA was performed using the newly developed
Multilinear Engine (ME-2) via the SoFi interface coded in Igor Pro (Canonaco
et al., 2013). The procedure allows an effective exploration of the solution
space, a more objective selection of the optimal solution and an estimation
of the rotational uncertainties (Canonaco et al., 2013; Crippa et al., 2014;
Elser et al., 2016; Fröhlich et al., 2015; Paatero and Hopke, 2009). We
only considered ions up to
The OA source apportionment was performed by constraining the source profile
of HOA, COA and BBOA with
Northerly winds prevailed throughout the entire campaign (Sect. S3). Located
south of central Guangzhou, the sampling site was thus severely affected by
pollutants transported from the city center. Overall, organics accounted for
46.3 % (or 24.5
The increase in the relative contribution of nitrate on highly polluted days
has also been observed in AMS studies at other locations in China, such as
Shenzhen (He et al., 2011), Beijing (Huang et al., 2010) and Changdao (Hu et
al., 2013). We used the molar concentration of total nitrate
[
Scatterplot of estimated inorganic nitrate versus nitrate from HR-ToF-AMS measurement. SL denotes the slope for the linear regression fitting.
Correlations between total nitrate (
In Fig. 3, the total (particle
Gas-to-particle partitioning of nitrate species to form particulate nitrate
can be affected by concentrations of ammonium and sulfate. An increase in
ammonium or a decrease in sulfate can facilitate the formation of particulate
nitrate (Seinfeld and Pandis, 2006). A number of studies have
indicated that a molar ratio of ammonium to sulfate of 1.5 demarcates the
observation of particulate nitrate (Griffith
et al., 2015; Huang et al., 2011a; Liu et al., 2015b; Pathak et al., 2004).
Under ammonium-rich (AR,
Time series of nitrate, excess ammonium and ammonium-to-sulfate molar ratio.
Distribution of nitrate species between
The partitioning of nitrate between the gas and particle phases shows
significant differences between November and December (Fig. 5). The average
ratio of nitrate to
In summary, the
Organics contributed most to the PM
Elemental ratios of organic PM
Diurnal variations in H : C
The mass spectra of all OA factors
Elemental analysis of OA (ratios of H : C, O : C and OM : OC) provides useful information for assessing OA characteristics and their evolution. Ions in the high-resolution mass spectra were used to calculate the elemental ratios using the improved-ambient method (Canagaratna et al., 2015). Results obtained from the Aiken-ambient (Aiken et al., 2007) protocol are also listed in Table 1 for comparison with elemental ratios reported in the literature. We further used empirical constants (11 % for H : C, 27 % for O : C and 9 % for OM : OC) from Canagaratna et al. (2015) to estimate the ratios accounting for the possible underestimation of the O : C ratio in earlier studies.
The average O : C, H : C and OM : OC ratios showed little variation
between the 2 months, with average values of 0.53, 1.63 and 1.87,
respectively, in November, and 0.53, 1.65 and 1.87, respectively, in December.
The observed elemental ratios generally agreed with other AMS-based reported
values in the PRD (Table 1). The H : C ratio was similar to those at rural
sites in Kaiping (1.64) and Heshan (1.65) and slightly higher than that in
suburban Hong Kong (1.54 and 1.55) but lower than those at urban sites in
Shenzhen (1.81) and Mong Kok in central Hong Kong (1.84). O : C and
OM : OC ratios, however, were higher than those at urban sites
(Shenzhen and Mong Kok) and lower than that in Kaiping but similar to those
in Heshan and suburban Hong Kong. Overall, the relatively low H : C
ratio and high O : C ratio suggest that OA at this site have a higher
degree of oxygenation than those at urban sites (e.g., Shenzhen) but a lower
degree than those at rural sites (e.g., Kaiping). Figure 6 shows that the
diurnal variations in H : C, O : C, OM : OC and carbon oxidation state
(
Monthly averages of OA fraction as well as variations in OA fractions in different ranges of OA concentration.
The mass spectra of all OA factors and their mass concentrations obtained
through PMF analysis with ME-2, together with the time series of external
tracers, are shown in Fig. 7. HOA correlated well with
Diurnal variations in ME-2-resolved OA factors during
November
Comparison of SOA /
Figure 8 shows the monthly average of OA fractions as well as their variations
in different ranges of OA concentrations. HOA contributed 23.8 % and
28.4 % to total OA in November and December, respectively. However, HOA
increased almost linearly with OA concentration, highlighting the need for
traffic control to mitigate high PM concentrations in buffer areas.
SVOOA and LVOOA remained the dominated OA fractions at OA concentrations
below 70
Figure 9 shows the diurnal patterns of mass concentrations and fractions for
the five OA factors. HOA exhibited two typical peaks in both November
and December during the morning rush hour at 09:00 LT and in the evening around
21:00 LT. HOA accounted for up to 40 % of OA (95th percentile of mass
fraction in a box-and-whisker plot) especially in the evening and at night,
likely due to heavily polluting trucks passing by en route to the city center at
night (22:00 to 07:00 LT). These diurnal variations in HOA correspond to those
in the H : C ratio and
The evolution of AMS OA factors has been used to infer SOA formation via
photochemical oxidation in a nearby city, Hong Kong (Lee et al., 2013; Li et
al., 2013; Qin et al., 2016). Figure 10 shows that SOA (SVOOA
SOA against
In this study, we have found that PM
The formation of total nitrate (particulate
The nighttime emissions of OA and efficient photochemical production of SOA
during the day together accounted for continued high OA concentrations. HOA
increased almost linearly with OA concentration and contributed up to
40 % of the high organic concentrations at night. SOA (SVOOA
The data is available upon request. To obtain the data, please contact Chak K. Chan (chak.k.chan@cityu.edu.hk) or Yong Jie Li (yongjieli@umac.mo).
The authors declare that they have no conflict of interest.
This work was supported by the National Key Project of the Ministry of
Science and Technology of the People's Republic of China (2016YFC0201901) and
the National Natural Science Foundation of China (41375156). We also thank
Dr. Mai Boru for providing