Introduction
Ambient air quality is mainly affected by particulate matter
(PM2.5 and PM10) and gaseous pollutants such as ozone (O3),
nitrogen oxides (NOx), carbon monoxide (CO), and sulfur dioxide
(SO2). Particulate matter comes from both natural sources (e.g.,
windborne dust, volcanoes) and anthropogenic activities such as fossil and
biomass fuel combustion (Chow and Watson, 2002). In addition to the net
downward transport of O3 by eddy diffusion from the stratosphere aloft,
tropospheric O3 is a well-known secondary gaseous pollutant, and it is
formed through the photochemical oxidation of volatile organic compounds
(VOCs) and NOx under the irradiation of sunlight (Logan, 1985; Roelofs
and Lelieveld, 1997). These chemicals have both received extensive attention
either due to their harmful impact on human health (Pope and Dockery, 1999;
Shao et al., 2006; Streets et al., 2007; Liu et al., 2013) and vegetation
(Feng et al., 2014) or significant effects on climate change (Seinfeld et
al., 2004; IPCC, 2007; Mercado et al., 2009). Moreover, some critical
interactions have been verified to exist between the gaseous pollutants
and/or particulate matter (Zhang et al., 2004; Cheng et al., 2016). For
instance, in the presence of high NH3 and low air temperature, ammonium
nitrate (NH4NO3) is formed in regions with HNO3 and NH3,
which is an important constituent of PM2.5 under high NOx
conditions (Seinfeld and Pandis, 2006). To some extent, such interactions
further improve or deteriorate the air quality. The oxidation of SO2
leads to acid deposition but also contributes to the formation of sulfate
aerosols (Meagher et al., 1978; Saxena and Seigneur, 1987), which in turn
will influence the solar radiation and photochemistry (Dickerson et
al., 1997) and further weaken the formation of secondary pollutants.
Therefore, clear understanding of their characteristics, sources, transport,
and formation mechanisms, including interactions, is crucial for gaining
comprehensive information on complex air pollution.
The Yangtze River Delta (YRD) region is located in the east of China, and it
includes the megacity of Shanghai and the well-industrialized areas of
southern Jiangsu Province and northern Zhejiang Province, with over 10 large
cities such as Hangzhou, Suzhou, Wuxi, and Changzhou lying along the mid-YRD
(Fig. 1). Being one of the most rapidly growing regions in China in terms of
transportation, industries, and urbanization, it has become a hot spot for
air pollution over the past 3 decades, together with the Pearl River Delta
(PRD) and Beijing–Tianjin–Hebei (BTH) regions. To date, numerous combined
studies of O3 and PM2.5 were implemented in representative urban
cities in the YRD region such as Shanghai (Geng et al., 2007; Ding et
al., 2013; L. Li et al., 2016; Miao et al., 2017a) and Nanjing (Wang et
al., 2002, 2003; Kang et al., 2013; Chen et al., 2016). On the contrary, in
Hangzhou (29.25–30.5∘ N, 118.34–120.75∘ E), the capital
city of Zhejiang Province in the YRD region, which lies along the mid-YRD,
only a few sole studies of PM2.5 or O3 have been sporadically
conducted. PM2.5 measurements in the city of Hangzhou have only been
performed in the past 5 years, mostly covering a short-term period in winter
(Jansen et al., 2014; Yu et al., 2014; Liu et al., 2015; J. Wu et al., 2016).
Furthermore, there was still certain discrepancy about the origin of
PM2.5. J. Wu et al. (2016) concluded that local vehicle emissions were a
major contributor to PM2.5, while results from Yu et al. (2014)
suggested that cross-border transport rather than local emissions control
high PM2.5 concentration and formation. Similarly, the photochemical
pollution in the city of Hangzhou was also not well understood. To the best
of our knowledge, the pioneer measurements of O3 in or around Hangzhou
started in the 1990s at the Lin'an site, a regional station located in the
east of Zhejiang Province (50 km away from Hangzhou) (Luo et al., 2000).
Subsequent studies at this site depicted the first picture of the seasonal
variations of O3 and its precursors (Wang et al., 2001, 2004). In the
city of Hangzhou, only short-term measurements of O3 were recently made
during the summertime of 2013 (Li et al., 2017). Hence, there is a large
knowledge gap concerning seasonal characteristics of these pollutants and a
discrepancy in their origin; these are both crucial for fully understanding
the complex combined pollution of PM2.5 and O3 in the city of
Hangzhou.
Location of the NRCS in the YRD region (left) and in the city of
Hangzhou (top right).
To supplement the seasonal picture of air pollution in YRD, we conducted
continuous measurements of trace gases (O3, NOx, NOy, CO, and
SO2) and particulate matter (PM2.5 and PM10) during
January–December 2013 at a regional site, NRCS (National Reference
Climatological Station), in Hangzhou, which is also an integrated measurement
site for the research of climate change and the atmospheric environment. This
study presents the first results of 1-year measurements of trace gases and
particulate matter in the city of Hangzhou and investigates the characteristics and
causes of these chemicals by discussing their seasonal characteristics,
interspecies correlations, the concentration dependence on local emissions and
regional transport, and specific photochemical pollution and haze cases.
Results and discussion
Concentration levels
To evaluate the overall concentration level of gaseous and particulate
pollution at NRCS, we selected a Grade II standard of the Chinese Ambient Air
Quality Standards (CAAQS, GB 3095-2012), which was released in 2012 by the
China State Council and implemented thorough the whole nation in 2016 (MEP,
2012). Inferred from the Grade II CAAQS for PM2.5
(75 µgm-3 for 24 h average) and PM10
(150 µgm-3 for 24 h average), 62 and 26 days of PM2.5
and PM10 exceedances with daily averages of 102.2 and
195.3 µgm-3 were classified thorough the period,
respectively, mostly occurring in winter. For O3, about 38 days
exceedances (75 ppbv for daily maximum 8 h average for the Grade II CAAQS)
in total were found during the whole period, mostly covering the period from May to
September. This suggests that Hangzhou was suffering from heavy haze and
photochemical pollution in cold and warm seasons. Concerning SO2, the
annual mean was 10.9 ppbv in this study, nearly half of the yearly mean of
SO2 Grade II CAAQS (21 ppbv). It was reasonably attributed to the
powerful measure of the Chinese government to control the emission of SO2
starting in 1990 (He et al., 2002; Qi et al., 2012). Table 2 summarizes a
statistical analysis of these species and lists the comparison with the
previous results in other typical regions in China. In general, with respect
to all these chemicals, our results were generally comparable with those
observed by other contemporaneous measurements in Hangzhou and the other
cities in YRD. As expected, regional differences among YRD, PRD, and BTH
could also be found, as illustrated in Table 2. For instance, observed
PM2.5, PM10, and CO concentrations were higher in BTH than those in
YRD and PRD through the comparison among provincial capital cities in China
during 2011–2014 (Chai et al., 2014; Wang et al., 2014), which has been
extrapolated to show more emissions from coal-based industries and coal and
biomass-burning-based domestic home heating in BTH in winter (Zhang et
al., 2012; Yang et al., 2013; Chai et al., 2014). Moreover, slight decreases
in PM2.5 and PM10 at NRCS were both evidenced by their respective
difference between 2013 and 2010–2011 (Table 2), coincident with the results
derived from the satellite data and ground monitoring in China (Ma et
al., 2016; Seltenrich, 2016). For NOy, only rough comparison was
implemented due to very limited measurements executed in China. The yearly
mean NOy concentration of 63.7 ppbv in this study was slightly higher
than 54.6 ppbv in Beijing (Y. Wu et al., 2016). It is interesting to note
that slightly higher NOy at NRCS possibly indicated more abundance of
nitrogen oxides in Hangzhou. Additionally, the daytime mean concentrations
were comparable with those at nighttime for PM2.5 in nearly all seasons
but were higher for O3 due to the daily variations in solar radiation and air
temperature; the reverse is true for CO, NOx, and NOy.
Mean species levels for different seasons and different times of day
and comparisons with other previous data reported in typical regions in
China. SD represents standard deviation.
Species
Areas
Location
Period
The whole day
Day time (08:00–17:00)
Nighttime (18:00–07:00)
Mean
SD
Max
Mean
SD
Max
Mean
SD
Max
PM2.5
YRD
This study
DJF
74.2
49.3
406.4
75.1
50.5
406.4
73.6
48.4
325.5
(µgm-3)
MAM
47.1
26.2
201.1
47.7
26.6
201.1
46.7
25.9
154.0
JJA
34.6
22.5
181
35.1
25.7
181.0
34.3
20.0
139.6
SON
52.5
34.4
272.4
51.7
33.3
238.1
53.1
35.1
272.4
Xiacheng District, Hangzhou (Sep–Nov 2013) monthly mean: 69 µgm-3a
NRCS, Hangzhou (2012) annual mean: 50.0 µgm-3b
Hangzhou (Sep 2010–Nov 2011 during non-rain days) annual average:106–131 µgm-3c
Nine sites in Nanjing (2013) AM: 55–60 µgm-3, JJA: 30–60 µgm-3, SON: 55–85 µgm-3d
Nanjing (Mar 2013–Feb 2014) annual mean: 75±50 µgm-3e
Shanghai (Mar 2013–Feb 2014) annual mean: 56±41 µgm-3e
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 87±67 µgm-3e
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 52±28 µgm-3e
PM10
YRD
This study
DJF
113.1
71.7
589.6
115.3
73.6
589.6
111.5
70.4
481.6
(µgm-3)
MAM
77.1
42.3
484.1
79.3
41.0
249.1
75.6
43.2
484.1
JJA
54.9
31.6
231.4
55.7
34.8
231.4
54.4
29.2
183.8
SON
85.6
51.2
344.2
84.8
48.6
341.3
86.1
53.0
344.2
Hangzhou (Mar 2013–Feb 2014) annual mean: 98±59 µgm-3e
Hangzhou (Sep 2010–Nov 2011 during non-rain days) annual average: 127–158 µgm-3c
Hangzhou (Sep 2001–Aug 2002) annual mean: 119.2 µgm-3f
Nanjing (Mar 2013–Feb 2014) annual mean: 134±73 µgm-3e
Shanghai (Mar 2013–Feb 2014) annual mean: 80±47 µgm-3e
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 109±62 µgm-3e
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 72±35 µgm-3e
O3
YRD
This study
DJF
13.8
13.1
70.9
17.7
14.1
70.9
10.2
10.9
58.5
(ppbv)
MAM
29.8
24.0
141.2
42.4
27.3
141.2
20.0
15.1
105.9
JJA
31.3
26.0
145.4
48.8
26.6
145.4
18.2
15.8
118.7
SON
25.9
22.5
100.1
37.0
25.1
100.1
16.3
14.3
99.5
Hangzhou (Mar 2013–Feb 2014) annual mean: 44±21 ppbv (8 h O3)e
Nanjing (Mar 2013–Feb 2014) annual mean: 42±20 ppbv (8 h O3)e
Shanghai (Mar 2013–Feb 2014) annual mean: 48±21 ppbv (8 h O3)e
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 45±27 ppbv (8 h O3)e
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 45±24 ppbv (8 h O3)e
SO2
YRD
This study
DJF
14.5
10.2
71.2
16.2
10.2
71.2
13.3
10.2
64.6
(ppbv)
MAM
11.3
9.1
75.1
11.7
9.6
75.1
11.0
8.7
59.3
JJA
8.6
6.5
51.0
8.0
6.3
51.0
9.0
6.6
46.7
SON
9.6
7.2
63.8
10.3
7.1
58.3
9.0
7.3
63.8
Hangzhou Xiacheng District (12–19 Oct 2013) daily mean: 5.7–9.7 ppbva
Hangzhou (Mar 2013–Feb 2014) annual mean: 9±4 ppbve
Nanjing (Mar 2013–Feb 2014) annual mean: 12±6 ppbve
Shanghai (Mar 2013–Feb 2014) annual mean: 7±5 ppbve
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 9±8 ppbve
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 7±3 ppbve
Continued.
Species
Areas
Location
Period
The whole day
Day time (08:00–17:00)
Nighttime (18:00–07:00)
Mean
SD
Max
Mean
SD
Max
Mean
SD
Max
CO
YRD
This study
DJF
1.4
0.7
3.8
1.4
0.7
3.3
1.4
0.7
3.8
(ppmv)
MAM
0.7
0.2
2.2
0.7
0.3
2.2
0.7
0.2
1.7
JJA
0.5
0.2
2.0
0.5
0.2
1.9
0.5
0.2
2.0
SON
0.8
0.3
3.4
0.7
0.3
1.9
0.8
0.3
3.4
Hangzhou (Mar 2013–Feb 2014) annual mean: 0.7±0.3 ppmve
Nanjing (Mar 2013–Feb 2014) annual mean: 0.8±0.4 ppmve
Shanghai (Mar 2013–Feb 2014) annual mean: 0.7±0.3 ppmve
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 1.1±0.7 ppmve
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 0.8±0.2 ppmve
NO2
YRD
This study
DJF
37.4
20.1
146.9
35.7
19.5
126.3
38.5
20.5
146.9
(ppbv)
MAM
28.7
12.9
94.8
25.3
12.1
94.8
31.0
12.9
87.4
JJA
17.3
10.2
61.4
13.0
9.2
46.1
20.3
9.7
61.4
SON
28.4
15.2
94.1
25.1
13.3
86.2
30.7
16.0
94.1
Hangzhou (Mar 2013–Feb 2014) annual mean: 13±9 ppbve
Nanjing (Mar 2013–Feb 2014) annual mean: 26±11 ppbve
Shanghai (Mar 2013–Feb 2014) annual mean: 20±9 ppbve
BTH
Beijing (Mar 2013–Feb 2014) annual mean: 25±11 ppbve
PRD
Guangzhou (Mar 2013–Feb 2014) annual mean: 24±10 ppbve
NOx
YRD
This study
DJF
60.5
34.7
199.8
58.0
32.1
168.9
62.3
36.3
199.8
(ppbv)
MAM
40.0
19.8
131.4
36.5
19.2
129.2
42.5
19.8
131.4
JJA
24.3
14.8
99.6
18.6
14.1
99.6
28.2
14.0
83.1
SON
41.0
24.3
153.4
36.6
21.1
123.7
44.2
25.8
153.4
NOy
YRD
This study
DJF
84.7
48.4
295.2
82.4
44.6
263.7
86.4
51.1
295.2
(ppbv)
MAM
66.0
33.6
248.8
62.9
34.6
248.8
68.2
32.8
204.1
JJA
43.6
27.6
259.5
36.8
29.3
259.5
48.5
25.2
167.7
SON
70.2
37.9
319.3
65.5
35.6
319.3
73.6
39.1
251.8
Nanjing SORPES 2013 monthly mean: 30–70 ppbvg
Shanghai May–June 2005 daily mean: 24–39 ppbvh
BTH
Beijing 2011–2015 annual mean: 54.6±4.7 ppbva
YRD
Guangzhou Apr–May 2004: 24–52 ppbvh
a J. Wu et al. (2016). b Qi et
al. (2015). c Sun et al. (2013). d Chen et
al. (2016). e Wang et al. (2014). f Cao et
al. (2009). g Ding et al. (2013). h Xue et al. (2014a).
Seasonal characteristics
Figure 2 shows seasonal variations of atmospheric O3 (a), CO (b),
NO (c), NOx (d), NOy (e), Ox (f), PM2.5 (g),
PM10 (h), and SO2 (i). Ozone exhibits a distinguished seasonal
variation, with a broad peak in
late spring and middle summer (a maximum in May and a secondary maximum in
July) and a minimum in winter (November to January). Its observed behavior at
NRCS is different from what has been disclosed in previous studies conducted
in southern and northern China, such as a summer minimum and an autumn
maximum of O3 found in Hong Kong and an early summer (June) broad
maximum recorded in Beijing (Ding et al., 2008; Lin et al., 2008, 2009; Xue
et al., 2014b; Zhang et al., 2014; Sun et al., 2016). Recently, Ding et
al. (2013) presented two peaks of O3 appearing in summer (July) and
early autumn (September) at the Xianlin site in the suburban area northeast
of Nanjing (about 239 km away from the NRCS). Regarding the geographical
location of Hangzhou, which is upwind of the YRD under the influence of the
southeasterly summer monsoon, the emissions in the YRD region and the solar
radiation might be the main causes of an O3 formation in summer,
resulting in a different seasonal cycle of O3 compared to other
continent sites in the west/northwest of the YRD. In fact, the CO and
NOy data (Fig. 2b and e) show that these precursors were still at fairly
high levels (about 500 and 35 ppbv, respectively) in summer. The low O3
level in winter, especially at night, can be attributed to the lower
temperature, weaker solar radiation, and in particular the strong destruction
of O3 by chemical titration of NO from local emissions or regional
transport as discussed below (Lin et al., 2008, 2009, 2011). Note that a
slight drop of O3 was found in June compared with other months in
summer, mainly attributed to the more frequent rainy days (23 days) and
larger rainfall in June (346 mm) than those in May (15 days) and July
(5 days) during summertime (Table 1).
Seasonal variations of atmospheric O3 (a),
CO (b), NO (c), NOx (d),
NOy (e), Ox (f), PM2.5 (g),
PM10 (h), and SO2 (i). Bold solid lines show the
monthly averages, solid circles show the median values, and thin lines
represent percentiles of 75 and 25 %.
For PM2.5 and PM10, Fig. 2g and h both display overall
well-defined seasonal variations with the maximum in winter (December) and
the minimum in summer (July). In cold seasons the emission of particulate
matter is normally high due to more emission of fossil fuels because of heating
in northern China (Zhang et al., 2009), which contributed to the enhancement
of particulate matter and other tracer gases (i.e., CO and NOx) at the NRCS site via long-distance transport (see discussion in Sect. 3.4). Furthermore,
in winter, temperature inversion and low mixing layer contribute to a decrease
in particulate suspension and advection (Miao et al., 2015a). Also, dry/wet
deposition should have strong seasonal variations because high precipitation
favors wet deposition and high soil humidity, and the growth of deciduous
plants may also favor the dry deposition of particulate matter in warm
seasons (Zhang et al., 2001). The relatively low concentrations of PM2.5
and PM10 in summer may also be partly due to an increased vertical
mixing (i.e., a higher boundary layer height) and more convection (Ding et
al., 2013; Miao et al., 2015b). PM2.5 mass concentration also shows
strong month-to-month variations. The simultaneous drop of PM2.5 and
PM10 concentrations together with other primary pollutants (i.e.,
SO2, CO and NOy) in February was mainly ascribed to the winter
break of the Chinese Spring Festival, which started at the end of January and
lasted until mid-February. Notably, the seasonal pattern for PM was similar
to NOx, which suggested that traffic and heating emissions were
important to the PM2.5 variation.
Other trace gases (CO, NOx, NOy, and SO2) all
revealed clear seasonal variations but also some unique month-to-month
variation patterns (Fig. 2a–f and i). Similar seasonal patterns among CO,
NOx, and SO2 were generally found, with pronounced minimums
appearing in summer and higher levels in autumn and winter. Similar reasons
to particulate matter could interpret these seasonal patterns, such as the
variation in the boundary layer height and the long-distance transport as
mentioned above. Last but not least was the photochemistry. During
summer, it is most active, accelerating the transformation of primary gaseous
pollutants, whereas in winter, a weaker photochemical reaction cannot remove
the gases as quickly as in the warmer seasons from the atmosphere.
NOy concentration increased at the end of autumn, with a maximum in
December together with a sharp peak of NO. The time series implied that in
December there was a multi-day episode of NOx with high mixing ratios of
NO and NO2, both reaching up to 100 ppbv, and these days were generally
correlated with northwesterly wind, suggesting fresh emissions from factories
in the industrial zone in the northwest. The potential ozone, Ox
(O3 + NO2), is usually used as an estimate of atmospheric total
oxidant (Lin et al., 2008). In winter (Fig. 2f), an
abnormally high level of Ox was found. The high level of
NO2 in Ox was expected to be originated from the significant
titration of high NO by O3 in November and December (Fig. 2a).
As shown in Fig. 2i, SO2 displayed a strong increase in winter but a
significant drop in November. This pronounced winter peak was mainly due to
the increased coal consumption for heating as mentioned above. The drop was
associated with the PM2.5 maximum and a relatively high RH (Fig. 2g and
Table 1), suggesting a possible role of heterogeneous reactions
(Ravishankara, 1997).
Scatter plots of NOy with O3 (NOy–O3) color-coded
with air temperature (a), NOy–PM2.5 color-coded with relative
humidity (b), NOy–SO2 color-coded with relative
humidity (c), and O3–PM2.5 color-coded with air
temperature (d).
Interspecies correlations
Interspecies correlation can normally be used as a way to acquire some
insights on their chemical formation, removal processes, and interactions. As
displayed in Figs. 3 and 4, we present scatter plots of NOy–O3,
NOy–PM2.5, NOy–SO2, O3–PM2.5, and
NOy–CO correlations based on the whole dataset, and we further
differentiate these correlations under typical environmental or
meteorological impacts with color-coded parameters (i.e., relative humidity,
air temperature, and O3 concentration). Clearly, an overall negative
correlation was found between O3 and NOy during the whole period
(Fig. 3a). The color data showed that a negative correlation mainly appeared
with data of low air temperature, implying a high
titration of freshly emitted NO with O3 during the cold seasons and at
nighttime. In contrast, a positive correlation between O3 and NOy
dominated under high air temperature, which usually occurred in the daytime
of warm seasons within a moderate level of NOy (< 150 ppbv). These
findings suggested a strong local photochemical production of O3 in
summer, leading to its seasonal variations as illustrated in Fig. 2a.
As illustrated in Fig. 3b, a good positive correlation was found between
PM2.5 and NOy, suggesting that PM2.5 was highly correlated
with fossil combustion at this site. Some green data in the plot show very
high NOy concentration together with low PM2.5, suggesting that the
concentration of NO air masses is high during December. Figure 3b shows that
high RH data were very scattered but PM2.5 / NOy data were
not, implying a negligible interference of humidity on TEOM PM2.5 measurement during the study
period, even under high RH conditions in summer.
SO2 and NOy show a moderate to good correlation (see Fig. 3c).
Specifically, a better correlation and higher SO2 / NOy ratio
were gained from air with low humidity. Nevertheless, the point distribution
was much more scattered for the humid air masses, and the ratio of
SO2 / NOy was clearly low, confirming a higher conversion of
SO2 to sulfate and/or deposition in humid conditions (Khoder, 2002; Su
et al., 2011). In this study, the averaged ratio of SO2 / NOy
during 18 February–30 April was 0.017, which is lower compared with that
previously reported at Lin'an during the same months 12 years ago (Wang et
al., 2004). This is mainly owed to a great reduction in SO2
emission from power plants but an increased NOx emission associated with
a huge consumption of petroleum fuels in the past decade in this region
(Zhang et al., 2009).
A scatter plot of O3 with PM2.5 color-coded with air temperature is
depicted in Fig. 3d. During periods of moderate to high air temperature, a
significant positive correlation was elucidated between O3 and
PM2.5, and the reverse negative correlation was found under low
temperatures. The positive correlation for warm air might reflect a formation
of secondary fine particulates in summer associated with high O3, which
was confirmed by our comparison of the ratio of the averaged PM2.5
concentrations in the typical O3 exceedances events (OE) to that in
nearby non-O3 exceedances (NOE) events
(PM2.5(OE) / PM2.5(NOE)) with the ratios for
other gaseous pollutants (Table S1 in the Supplement). The secondary
particulate formation may be related to a high conversion rate of SO2
and NOx to sulfate and nitrate under a high concentration of oxidants
(Khoder, 2002; Sun et al., 2013). Additionally, it was also associated with
the formation of secondary organic aerosols with high O3 concentrations
(Kamens et al., 1999; Lambe et al., 2015; Palm et al., 2017), which were
primarily produced through the photo-oxidation of BVOCs (Claeys, et
al., 2004; Böge et al., 2013). As inferred above, significant emission of
BVOCs was speculated around NRCS in summer. Note that it is necessary to
implement more detailed investigations related with chemical elements, ion,
and organic and elemental carbon (OC/EC) analysis of particulate
matter. The anti-correlation for cold air might be caused by the titration
effect of high NO concentration in relation to high primary PM2.5 in
cold seasons, which was also reflected by the consistency of the seasonal
variations in NO and PM2.5.
(a) Scatter plots of NOy with CO color-coded with O3
mixing ratios are shown, as well as the inset (b) showing the scatter plot with
O3 mixing ratios above 80 ppbv.
Seasonal cluster analysis of the 72 h air mass back trajectories
starting at 100 m from the NRCS site in Hangzhou.
(a) Seasonal weighted potential source contribution
function (WPSCF) maps of PM2.5 in Hangzhou. The sampling site is marked
by the star and the WPSCF values are displayed in color. (b) The
zoomed view of Fig. 6a. (c) Seasonal and spatial distributions of
PM2.5 emissions (kg km2 mon-1) at the surface layer in
China. The sampling site is marked by the star.
(a) Same as Fig. 6a but for O3. (b) The
zoomed view of Fig. 7a.
Time series of meteorological parameters and chemical species
before, during, and after the haze period. The gray shaded area indicates the
Phase I (28 November–1 December) and Phase II (10–12 December), and the
yellow shaded area represents haze
events Phase III (2–9 December) and Phase IV (13–15 December).
The geopotential height field (GH) (indicated by color bars) and
wind field (WF) (black vectors) for 925 hPa at 20:00 LT during
2–9 December 2013. All panels represent 2–5 and 6–9 December from left to
right on the top and bottom. The NRCS is marked by the star.
Same as Fig. 8 but during the photochemical pollution period. The orange
shaded area represents Phase I (28–30 May and 20–22 June), the cyan
shaded area indicates Phase II (9–12 July), and the other area
represents Phase III (1–3 May, 20–22 May, and 9–11 August).
Figure 4 shows a good positive correlation between CO and NOy
color-coded with O3 mixing ratios. For CO lower than 3.2 ppmv during
the whole period, an increase of NOy generally led to lower O3
concentrations, but CO showed a reverse pattern. As VOCs and CO have a common
origin, VOCs show a similar behavior to CO in the ozone photochemistry in
typical urban regions (Atkinson, 2000; Guo et al., 2004). Our results
suggested a VOC-limited regime throughout the year in Hangzhou, consistent
with the results reported in other cities of the YRD region (e.g., Shanghai
and Nanjing) (Geng et al., 2007; Ding et al., 2013). As specifically shown in
Fig. 4b, atmospheric O3 (above 80 ppbv) mainly occurred in the
afternoon (14:00–16:00 LT) in the summer and early autumn, exhibiting an
increased trend with the increasing NOy within air masses, with a
moderate CO mixing ratio of 0.25–1.5 ppmv, and the reverse trend for CO was
not expected to be significantly increased. This indicated that the
transition from a VOC-limited regime to an optimum O3 production zone
(even NOx-limited regime) probably occurred at the NRCS site in warmer
seasons. We speculated that this change was mainly attributed to the larger
emission of biogenic VOCs (BVOCs) compared to cold seasons. As reviewed by
Calfapietra et al. (2013), the VOC-limited conditions, in which O3
production is limited by a high concentration of NOx, are often observed
in urban areas. However, if high BVOC emitters are common in urban areas,
they could move the VOC / NOx ratio toward optimal values for
O3 formation, resulting in this ratio being reached in the city centers.
As depicted in Sect. 2.1, our study site is situated adjacent to Prince Bay
Park (area 0.8 km2) and in the northeastern part of the famous scenic
spot of West Lake (area 49 km2). These two regions were both
urban green parks with high vegetation coverage. Moreover, the primary tree species in these two regions,
Liquidambar formosana and Cinnamomum camphora, respectively, are the major
contributors to the emissions of isoprene and monoterpene (Chang et
al., 2012), favoring the formation of O3. Air masses from Prince Bay
Park and West Lake famous scenic spot were confirmed to be transported to the
NRCS site during warmer seasons, as illustrated in Figs. S1 and 8b. In view
of the strong temperature dependence of isoprenoid emission (Guenther et
al., 1995), a significantly increased emission of BVOCs was expected in warm
seasons, and thus it disturbed the original balance between VOCs and NOx
relative to cold seasons. Our conclusion was generally in line with the
contemporaneous study implemented by L. Li et al. (2016), who found that a
VOC-limited regime accounted for 47 % of the ozone formation during the
summer in Hangzhou and that the others are under a NOx-limited regime,
taking BVOCs into consideration. Recently, Li et al. (2017) also deduced that
the summer ozone mostly presented a VOC-limited regime and a transition
region alternately in the city of Hangzhou.
Dependences of pollutant concentrations on local emission and
regional transport
To obtain an overview of the impact of wind on the pollutants' concentrations, we draw the seasonal wind dependence
maps of pollutants' concentrations with wind sectors (see Fig. S2 for
details). In total, similar seasonal patterns of the wind dependence map were
found between CO and PM2.5, SO2, and NOy (NOx), in good
agreement with their seasonal patterns as shown in Sect. 3.2. For CO and
PM2.5, their top 10 % concentrations were generally related with all
the directions throughout the year at speeds lower than 2 m s-1, while
their bottom 10 % were associated with other directions of wind, except
northerly, at higher wind speeds. It is necessary to pay attention to the
scatter points of the top 10 % of concentrations distributed in a
northerly direction with high wind speed. With respect to the wind direction
and transport, as the wind speed increases, pollutants' concentrations should
have been decreasing due to the more effective local dilution, thus the
increase instead might indicate potential sources in these directions.
To address this issue and further investigate the relative contribution of
local emission and regional transport, we employed the trajectory clustering
and WPSCF, along with the comparison with the emission inventories. The 72 h
back trajectories from the NRCS site were computed using the HYSPLIT model
for four seasons. As shown in Fig. 5, we obtained six clusters using the
clustering algorithm for four seasons with seven dominant paths distributed
in the east (E), northeast (NE), north (N), northwest (NW), west (W),
southwest (SW), and southeast (SE). The length of the cluster-mean
trajectories indicates the transport speed of air masses. In this analysis,
the long and fast-moving trajectories were disaggregated into groups
originating from more distant SE and SW regions during summer and NW and N
regions during other seasons. Members of this cluster have extremely long
transport patterns, some of them even cross over Inner Mongolia and Mongolia
(e.g., N and NW). Trajectories belonging to S–SW and E–SE typically
followed flow patterns from the South Sea and Pacific Ocean, respectively.
Otherwise, some trajectories have short transport patterns, indicative of
slow-moving air masses. Most of the pollution episodes within this group are
probably enriched from regional and local emission sources. Such trajectories
were also identified during every season in our study. For instance, the air
masses associated with cluster 4 (in spring, autumn, and winter) and
cluster 1 in summer predominantly originated from local areas and nearby
provinces with significant pollution sources, such as Jiangsu, Anhui, and
Shanghai.
Table 3 summarizes the percentages of these identified trajectory clusters on
a seasonal basis as well as the corresponding mean concentrations of PM2.5 and
other trace gases related to each trajectory cluster. As inferred from Table
3, the clusters exhibited larger variability and season dependence: the
predominant clusters were W (42.7 %) in spring, SW (53.9 %) in
summer, NW (35.5 %) in autumn, and N (54.9 %) in winter,
respectively. It is of interest to note that some trajectory clusters with
small percentages are highly related with high pollutants' concentrations.
In summer, a few PM2.5 pollution cases (only 8.4 % of the summertime
trajectories) with mean concentration as high as 51.5 µgm-3
were related with the N trajectories traveling across the cluster of strongly industrialized
cities (i.e., Suzhou, Wuxi, and Changzhou).
Mean concentrations of PM2.5 (µgm-3) and other
trace gases (units of ppmv for CO but ppbv for other gases) in the identified
trajectory clusters within the four-season period, together with the percentages
of each trajectory cluster.
Season
Cluster
Percent
PM2.5
O3
SO2
CO
NOx
( %)
Spring
1
12.1
45.0
28.3
10.7
0.7
38.3
2
16.6
44.3
31.6
13.2
0.7
39.1
3
16.0
35.3
30.5
9.7
0.6
34.5
4
42.6
52.4
23.2
11.4
0.8
42.5
5
5.5
38.2
34.2
11.2
0.7
37.9
6
7.2
58.1
34.2
11.9
0.8
43.8
Summer
1
8.4
51.5
24.6
7.9
0.8
29.2
2
8.6
34.2
35.2
9.2
0.5
22.8
3
22.6
24.0
28.7
7.9
0.4
21.7
4
31.3
38.2
36.8
9.1
0.5
24.4
5
19.4
38.7
27.2
8.9
0.6
28.7
6
9.7
22.4
26.7
7.5
0.4
17.6
Autumn
1
23.6
42.1
27.4
9.9
0.7
36.9
2
32.5
50.7
24.6
8.2
0.8
39.4
3
8.3
21.7
19.8
8.0
0.5
22.0
4
7.8
68.6
34.8
8.4
0.8
38.8
5
11.9
49.9
22.6
10.1
0.7
40.8
6
15.9
79.6
21.6
12.9
0.9
62.0
Winter
1
7.1
60.9
16.6
15.4
1.3
53.7
2
24.2
83.3
14.4
15.9
1.4
65.4
3
16.4
47.3
14.0
11.9
1.1
42.7
4
21.8
75.9
11.9
13.5
1.5
63.1
5
16.8
67.0
11.7
13.1
1.5
53.7
6
13.7
102.1
14.4
16.9
1.4
81.0
Furthermore, we depicted the seasonal WPSCF maps (a), the corresponding
zoomed maps (b), and the emissions maps (c) for PM2.5, O3, CO,
NOx, and SO2, respectively, denoted with letters a, b, and c in
the figure captions. Here we presented the results of two representative
species, PM2.5 (Fig. 6a, b, and c) and O3 (Fig. 7a, b), and those of
the other species were included in the Supplement (Figs. S3a, S5c). Judging
from the WPSCF maps, together with their corresponding zoomed views and the
calculated emissions maps, a few distinct features were summarized. (1) Local
emissions were significant for the primary pollutants such as CO
(Fig. S3), NOx (Fig. S4), SO2 (Fig. S5), and PM2.5 (Fig. 6) on
a seasonal scale. For O3, local photochemistry dominated during spring,
summer, and autumn (Fig. 7a, b) due to strong photochemical reactivity.
(2) The potential sources of CO and NOx had similar patterns on spatial
and seasonal scales, with higher values in the NW during spring, covering the
mid-YRD regions across Anhui Province and reaching the border of Henan
Province, and in the NW and N during autumn and winter, covering most of
Jiangsu Province and part of Shandong Province, including the cities of Jinan and
Zibo. (3) Higher values for SO2 were located in the city of Ningbo and on the
coast of the Yellow Sea during spring, in the southeastern region from the East Sea
during summer, probably due to ship emissions (Fan et al., 2016), but in the
inland cities such as Shaoxing and Quzhou of Zhejiang Province during
autumn and Anhui Province during winter. In total, along with the air mass
trajectories, the WPSCF maps for these primary pollutants were generally in
line with their respective corresponding species' emissions (Figs. 6c, S3c,
S4c, and S5c). Although no seasonal patterns in emission maps were found, the
emissions of these pollutants exhibited interspecies similarity and strong
spatial dependence with the industrialization level.
In terms of PM2.5, the potential sources showed distinct seasonal
variations such as southeastern regions of Jiangxi Province and the northwestern
area of Zhejiang Province during spring and in the western cities of North
Korea (Pyongyang) and South Korea (Seoul) with the northeasterly air mass
across the Yellow Sea during summer. As illustrated in Fig. 6a and b, the
contributions from local emissions were both found to be more significant for
autumn and winter than spring and summer, covering all the cities in Zhejiang
Province, especially in the south and southwest (e.g., cities of Lishui,
Jinhua, and Quzhou). Moreover, we found higher WPSCF values located
in the central cities of Jiangsu Province in autumn and the expanded area
towards the whole Jiangsu and Anhui Province and the southeastern coastal cities
(e.g., Wenzhou, Ningbo in Zhejiang Province, Fuzhou in Fujian Province) in
winter, revealing that cross-boundary transport is crucial to the pollution of
particulate matter. This result has been confirmed by Yu et al. (2014), who
also found that such transport dominated in the city of Hangzhou during the heavy
haze episode (3–9 December 2013).
For O3, its potential sources should be interpreted with caution since
it is not directly emitted to the atmosphere and has complicated chemistry
involved with VOCs and NOx. The majority of the measured O3 is
probably formed by photooxidation in the vicinity of the measurement site
(Fig. 7b), specified as the local contribution, but clear differences
associated with regional transport are illustrated in Fig. 7a. In spring,
high O3 concentrations were connected with air masses coming from
western and southwestern regions (e.g., Anhui, Jiangxi, and mid-Guangdong
Province) and northwestern areas such as Jiangsu, Henan, and Shandong
Province; in summer, more extensive potential sources were elucidated to be
located in the eastern, southern, and southwestern regions of China, covering
the southern part of Zhejiang Province, southeastern cities of Jiangxi
Province, almost the whole of Fujian Province, the eastern part of Guangdong
Province, and the mid-Zhejiang Province (e.g., cities of Quzhou, Jinhua, and
Ningbo). A very interesting finding should be pointed out that air masses
that were transported from the offshore areas of the Yellow Sea, East Sea,
and South Sea to southeastern Zhejiang, Jiangsu, and Fujian Province,
respectively, were also found to be highly relevant to the elevated O3
at the NRCS site. This was also well evidenced by seasonal and spatial
distributions of O3 volume mixing ratio simulated by MOZART-4/GEOS-5 (see
Fig. S6). We speculated that the recirculation of pollutants by sea- and
land-breeze circulations around the cities along the YRD and Hangzhou Bay,
which has been confirmed by M. M. Li et al. (2015, 2016), was largely
responsible for the increased concentration of O3 at the NRCS site. Such
an increase in O3 concentrations in urbanized coastal areas has been
observed and modeled in a number of studies (Oh et al., 2006; Levy et
al., 2008; Martins et al., 2012). Thus, our study further emphasizes the
importance of local thermally induced circulation for air quality.
Cases studies for haze (high PM2.5) and photochemical
pollution (high O3) episodes
To elucidate the specific causes of high PM2.5 and O3 episodes
including the transport and local photochemical formation, we chose two
typical cases for detailed interpretations, and these are presented here. In
this study, the haze pollution episode is defined as the event that consists
of continuous days with daily averaged PM2.5 concentration exceeding
75 µgm-3, which has also been used to distinguish non-haze
and haze episode in other studies (Yu et al., 2014; J. Wu et al., 2016). With
respect to this campaign, there were two non-haze episodes (Phases I:
28 November–1 December and II: 10–12 December) and their subsequent severe
haze pollution episodes (Phases III: 2–9 December and IV: 13–15 December)
at the NRCS site, as illustrated in Fig. 8. Phase III showed that high
PM2.5 (up to 406 µgm-3) appeared on 7 December, and
broad PM2.5 peaks (around 300 µgm-3) occurred before and after 2 days.
Simultaneously, CO, SO2, and NOx also reached very high levels on
this day, confirming that the common origin of CO and PM2.5 is from
heating and combustion and the rapid conversion of SO2 and NOx to
sulfate and nitrate in PM2.5 in winter. But for O3, its level
reached as low as 11.5 ppbv at 15:00 LT on that day, owing to the weak
photochemical activity under the severe haze pollution. Along with the high
NO2 concentration (around 120 ppbv), it could not produce sufficient
conversion oxidants (OH and HO2 radicals) for the gas-phase oxidation of
SO2 (Poppe at al., 1993; Hua et al., 2008), while the increased relative
humidity during 6–8 December possibly favored the aqueous-phase oxidation of
SO2.
Moreover, according to the results obtained from the backward trajectory
cluster and WPSCF analysis during 2–9 December 2013 (Fig. S7), we found an
apparent contribution from the transported air mass from northwestern regions
such as Jiangsu Province and Anhui Province. Our results were in good
agreement with contemporaneous measurement in Hangzhou (J. Wu et al., 2016).
Subsequently, at the end of this episode, significant drops of these species
except O3 were observed from 00:00 to 23:00 LT on 9 December (i.e., 189
to 41.6 µgm-3 for PM2.5, 2.3 to 1.0 ppmv for CO, and
145 to 47.9 ppbv for NOx). Weather chart and wind data suggested that
the region of NRCS was always controlled by a strong continental high
pressure system originating from the northwest before 8 December (Fig. 9a–f)
but that this rapidly changed to be dominated under a strong marine high pressure
system coming from the east at 02:00 LT on 9 December (Fig. 9g–h), which
brought clean maritime air passing over the Yellow Sea and thus caused such
decreases in these pollutants. However, it quickly turned back to be
controlled under a continental high pressure system described above, carrying
pollutants from the city clusters to the NRCS site. This could account for the
accumulations of these species during the intermediate period (Phase II). The subsequent Phase IV with high PM2.5 episode was also found to be
governed by a stagnant high pressure over the YRD region (Fig. S8).
For the photochemical pollution events, we selected three cases with O3
exceedances (74.6 ppbv) during May–August according to Grade II standard of
CAAQS. As displayed in Fig. 10, they were Phase I (28–30 May and
20–22 June), with a rapid buildup and decrease of O3 within 3 days,
Phase II (9–12 July), representing a distinct accumulation process of
O3 exceedances, and Phase III (1–3 and 20–22 May, and 9–11 August),
with high O3 levels within three consecutive days. For 28 May in
Phase I, the weather chart suggested that a strong anticlockwise cyclone was
located over the YRD. In this case, the cyclone (i.e., low pressure) caused
favoring conditions for pollution diffusion, e.g., cloudy weather and high
wind velocities. Then, a strong clockwise anticyclone from the northwest,
sweeping over the cluster of cities (i.e., Nanjing and Shanghai), rapidly
moved adjacent to the NRCS site on 29 May. It carried the primary pollutants
such as CO, SO2, and NOx from these megacities, and secondary
products (i.e., O3 and some NOz) were further produced via complex
photochemical reactions under such synoptic conditions. As the orange shaded
area shows in Fig. 10, the hourly maximums of O3 and PM2.5 were
observed to be as high as 141.2 ppbv and 135.8 µgm-3 at
13:00 LT on 29 May. Following this day, the cyclone dominated this region
again and caused a sudden decrease in atmospheric pollutants. Also, a similar
case was found during 20–22 June under such changes in synoptic weather. For
Phase II (9–12 July), a typical accumulation process was observed, with the
daily maximums of atmospheric pollutants increasing from 90.4 to 142.9 ppbv
for O3, 77.6 to 95.3 µgm-3 for PM2.5, and 80.2 to
125.2 ppbv for NOy. The examination of the day-to-day 925 hPa synoptic
chart derived from NCEP reanalysis suggested that a high pressure system
governed over the YRD during 9–11 July, with southwesterly prevailing wind.
The air masses recorded at this site mainly came from the most polluted city
clusters in the southwest (e.g. Zhejiang, Jiangxi, and Fujian Province).
Meanwhile, the stagnant synoptic condition (i.e., low wind speed) favored the
accumulation of primary pollutants such as CO and NOx. Secondary
pollutants O3 and PM2.5 were also rapidly formed via photochemical
oxidation, and they further accumulated under such synoptic conditions,
together with continuous high temperature (daily mean around 33 ∘C).
On 12 July, a typhoon named Soulik moved to a location a few hundred kilometers away from the NRCS site,
bringing southeasterly maritime air over the YRD. Daily maximum O3
reached 142.8 ppbv at 12:00 LT, even with a low concentration of precursors
(i.e., 0.48 ppmv for CO and 16.0 ppbv for NOx), suggesting high
photochemical production efficiency of O3 in this region in summer. This
phenomenon has also been found in the multi-day episode of high O3 in
Nanjing during 20–21 July 2011 (Ding et al., 2013). In this phase,
PM2.5 mass concentration showed very good correlation (R=0.79,
p<0.001) with O3 during the daytime (09:00–17:00 LT), possibly
indicating a common origin of BVOCs due to the significant vegetation
emission as discussed above, in addition to high biomass production in the
southern part of the YRD (Ding et al., 2013). For Phase III (1–3 and
20–22 May, and 9–11 August), there were mostly sunny days with low wind
speed and moderate/high air temperature, which were both beneficial factors
for the photochemical formation of O3, together with sufficient
precursors (NOx and VOCs) in the summer and early autumn over YRD. For
1–3 and 20–22 May, daily maximum T values were moderate (around
25 ∘C versus 31 ∘C), while the daily maximums of NOx
reached as high as 43–95 and 50–90 ppbv, with both favoring the
photochemical formation to produce the continuous high O3 concentrations
(daily maximums: 96–133 ppbv via 104–133 ppbv). The reverse case is also
true during 9–11 August, on which the daily maximum T and NOx ranged
40.6–41.4 ∘C and 33–44 ppbv, respectively, resulting in the
production of continuously high O3 from 98.8 to 130.5 ppbv.
Photochemical age and ozone production efficiency during
photochemical pollution and haze period
Photochemical age is often used to express the extent of photochemistry,
which can be estimated using an indicator such as NOx / NOy
(Carpenter et al., 2000; Lin et al., 2008, 2009, 2011; Parrish et al., 1992).
Air masses with fresh emissions have an NOx / NOy ratio close
to 1, while there is a lower NOx / NOy ratio for the
photochemical aged air masses. In this study, for the haze events mentioned
above, the average and maximum NOx / NOy ratios were as high as
0.80 and 0.99, respectively, indicating that photochemical conversion of
NOx is not absent but fairly slow. This was quite consistent with the
largely weakened photochemistry due to the low intensity of UV radiation in
winter. In contrast, during the photochemical pollution period, they were as
low as 0.53 and 0.14 for the average and minimum ratio. The simultaneous
measurements of atmospheric O3, NOx, and NOy can provide an
insight into calculating the ozone production efficiency (OPE) for different
seasons. From the data of Ox and NOz, the ratio of
Δ(Ox) / Δ(NOz) can be calculated as a kind of
observation-based OPE (Trainer et al., 1993; Sillman, 2000; Kleinman et
al., 2002; Lin et al., 2011). In this study, the mean values of NOz and
Ox between 07:00 and 15:00 LT were used to calculate the OPE values
through linear regression. In addition, these data were also confined to
sunny days and wind speeds below 3 m s-1, reflecting the local
photochemistry as possible. The OPE value during the photochemical pollution
period (SOPE) mentioned above was 1.99, generally within the reported range
of 1–5 in the PRD cities, but lower than 3.9–9.7 in the summer in Beijing
(Chou et al., 2009; Ge et al., 2012). Meanwhile, the OPE value of 0.77 during
the haze period (HOPE) was also comparable
with the reported value of 1.1 in winter in Beijing (Lin et al., 2011). The
smaller winter OPE value in Hangzhou might be ascribed to the weaker
photochemistry and higher NOx concentration. At a high NOx level,
OPE tends to decrease with the increased NOx concentration (Ge et
al., 2010; Lin et al., 2011). In Hangzhou, the NOx level is frequently
higher than needed for producing photochemical O3, and excessive
NOx causes net O3 loss rather than accumulation. In this study,
75 % of daily OPE values were negative, which can be explained by two
factors. To some extent, due to the geographical location and unique climate
characteristics of Hangzhou as depicted above, the interference of
non-beneficial meteorological conditions existed in the formation of local
O3 deriving from photochemistry, i.e., strong wind, frequent rainy days.
The other factor points to the consumption of O3 by excessive NOx,
which was also confirmed by the conclusion that Hangzhou was mostly in the
VOC-limited regime as discussed in Sect. 3.2. Such a circumstance was also
observed at the rural site of Gucheng in the North China Plain (NCP) and in
the urban area of Beijing (Lin et al., 2009, 2011). Taking the average of
SOPE of 1.99 and the average daytime increment of NOz (ca. 20 ppbv), we
estimated an average photochemical O3 production of about 39.8 ppbv
during the photochemical pollution period. In contrast, the lower average
photochemical O3 production was estimated to be 10.78 ppbv during the
haze period based on HOPE, which might act as a significant source of surface
O3 in winter in Hangzhou.