This study analyzed the long-term variations in carbon monoxide (CO) mixing
ratios from January 2006 to December 2017 at the Lin'an regional atmospheric
background station (LAN; 30.3∘ N, 119.73∘ E, 138 m a.s.l.) in China's Yangtze River Delta (YRD) region. The CO mixing ratios
were at their highest (0.69 ± 0.08 ppm) and lowest (0.54 ± 0.06 ppm) in winter and summer, respectively. The average daily variation in CO
exhibited a double-peaked pattern, with peaks in the morning and evening and
a valley in the afternoon. A significant downward trend of -11.3 ppb yr-1 of
CO was observed from 2006 to 2017 at the LAN station, which was in
accordance with the negative trends of the average CO mixing ratios and
total column retrieved from the satellite data (Measurements of
Pollution in the Troposphere, MOPITT) over the YRD region during the same
period. The average annual CO mixing ratio at the LAN station in 2017 was
0.51 ± 0.04 ppm, which was significantly lower than that (0.71 ± 0.12 ppm) in 2006. The decrease in CO levels was largest in autumn (-15.7 ppb yr-1), followed by summer (-11.1 ppb yr-1), spring (-10.8 ppb yr-1), and
winter (-9.7 ppb yr-1). Moreover, the CO levels under relatively polluted
conditions (the annual 95th percentiles) declined even more rapidly (-22.4 ppb yr-1, r=-0.68, p<0.05) from 2006 (0.91 ppm) to 2017 (0.58 ppm), and the CO levels under clean conditions (the annual 5th percentiles)
showed decreasing evidence but not statistically significant (r=-0.41, p=0.19) throughout the years. The long-term decline and
short-term variations in the CO mixing ratios at the LAN station were mainly
attributed to the implementation of the anthropogenic pollution control
measures in the YRD region and to events like the Shanghai Expo in 2010 and
Hangzhou G20 in 2016. The decreased CO level may influence atmospheric
chemistry over the region. The average OH reactivity of CO at the LAN
station is estimated to significantly drop from 4.1 ± 0.7 s-1 in
2006 to 3.0 ± 0.3 s-1 in 2017.
Introduction
Carbon monoxide (CO) is a key player in the atmospheric carbon cycle
(Novelli et al., 1992). In the troposphere, CO is one of the important air
pollutants with high mixing ratios. The volume mixing ratios of CO can reach
an order of 10-6 (Khalil and Rasmussen, 1990). CO is also a reactive trace gas
that considerably affects health, ecology, and climate and hence
recommended by the Global Atmosphere Watch (GAW) of the World Meteorological
Organization (WMO) for priority observation. Fossil fuel combustion (mainly
in the Northern Hemisphere), biomass combustion (mostly in the Southern
Hemisphere), and natural processes (the oxidation of organic compounds, such
as methane (CH4) and isoprene) are the main sources of CO (Crutzen et al., 1979; Holloway et
al., 2000; Thompson and Cicerone, 1986; Novelli et al., 1998; Andreae and Merlet,
2001; Bakwin et al., 1994). The major sink for CO is its reaction with OH
radicals in the troposphere (Holloway et al., 2000; Thompson and Cicerone, 1986;
Novelli et al., 1998). The lifetime of CO in the atmosphere
ranges from weeks to months, which makes it an ideal tracer for atmospheric
transport processes (Seinfeld and Pandis, 2006; Worden et al., 2013).
Because CH4 and CO can react with OH radicals (Thompson, 1992;
Daniel and Solomon, 1998), certain CO mixing ratios can indirectly cause a
decrease in CH4 and an increase in CO2. Therefore, CO is
recognized as an important indirect greenhouse gas. Moreover, CO can be an
important precursor for the photochemical generation of ozone in rural
areas (Demerjian et al., 1972).
Continuous long-term observation is a method for studying large-scale CO
sources, sinks, and long-distance transport. This method allows the CO
balance to be determined on a regional or global scale (Fang et al., 2014).
In the past decades, many studies have explored the long-term change in CO
levels through ground-, aircraft-, or satellite-based observations (Yurganov
et al., 2010; Worden et al., 2013; Ahmed et al., 2015; Cohen et al., 2018;
Wang et al., 2018). Most of these studies have revealed downward trends for
CO concentration. For example, Worden et al. (2013) reported that the CO
total column over China decreased by 1.6 % ± 0.5 % yr-1 from 2002 to
2012. Ahmed et al. (2015) analyzed long-term CO observations at two urban
sites in Seoul and reported a downward trend of CO from 2004 to 2013. Wang
et al. (2018) found that from 1998 to 2014, the total column amount of CO
over Beijing and Moscow decreased at 1.14 % ± 0.87 % yr-1 and
3.73 % ± 0.39 % yr-1, respectively. Cohen et al. (2018) analyzed the
trends of CO in the upper troposphere from 2001 to 2013. In their study,
almost all observed trends were negative, with the estimated slopes ranging
from -1.37 to -0.59 ppb yr-1. The CO data recorded in the Arctic ice core
indicated that the CO mixing ratios in this region decreased after the 1970s
(Petrenko et al., 2013).
Ground-based background measurements are crucial for verifying the accuracy
of satellite observation data, reflecting the impact of human activities on
air quality and climate change, and evaluating the effectiveness of
pollution control measures. In China, many air pollutants have been emitted
in very large quantities. For example, the emission of CO was estimated to
be about 171 Tg in 2010 (Li et al., 2017). To fight against the air
pollution, the country has implemented a series of emission control measures
in the last decade. The effectiveness of these measures needs to be
verified by observational data, in particular the data from background
sites. Long-term background observations over a decade are relatively scarce
in China. Reports of long-term background observations of CO are very
limited in the literature (Meng et al., 2009; Liu et al., 2019; Zhou et al.,
2004; Zhang et al., 2011) and none of them present an analysis of CO
variations over a decade. The Yangtze River Delta (YRD) is one of the most
developed regions in China. The long-term observation of atmospheric
background CO allows for a scientific understanding of the CO source and
sink cycle in this region. In this study, we present 12-year (from 2006 to
2017) ground-based observations of CO at a background station in the YRD
region. We analyze the long-term CO variations and their determinants in the
background areas of eastern China. The results of this study function as
scientific evidence for evaluating the effectiveness of pollution control
policies and as a reference for formulating practicable air pollution
management and emission control measures.
Monitoring site and data collection
The CO mixing ratios analyzed in this study were collected from January 2006
to December 2017 at Lin'an (LAN) station (30∘18′ N,
119∘44′ E, 138.6 m a.s.l), a regional atmospheric background
monitoring site in China's Zhejiang Province. The LAN station is one of the
seven atmospheric background stations operated by the China Meteorological
Administration and also a member station of the World Meteorological
Organization (WMO) Global Atmosphere Watch (GAW) program. The measurements
at this station reflect the changes in the YRD region's atmospheric
background composition (Qi et al., 2012). The LAN station is located
approximately 50 km west of Hangzhou (the capital city of Zhejiang Province)
and 150 km southwest of Shanghai. It is influenced by a typical subtropical
monsoon climate. Figure 1 displays the seasonal variations in temperature (T),
air pressure (P), wind speed (WS), and relative humidity (RH) as well as the
wind direction (WD) frequency at the LAN station from 2006 to 2017. These
data were obtained from regular meteorological observations at the LAN
station. As displayed in Fig. 1, the seasonal temperature trend at the LAN
station was of a convex shape. The highest and lowest temperatures occurred
in July (28.4 ± 1.5 ∘C) and January (4.1 ± 1.8 ∘C), respectively. In opposition to the seasonal change in
temperature, the seasonal change in atmospheric pressure at the LAN station
showed a concave shape, with the lowest and highest pressures occurring in
July (989.51 ± 0.77 hPa) and January (1010.81 ± 1.54 hPa),
respectively. The seasonal patterns of the WS and RH at the LAN station were
not as clear as those of air temperature and pressure. The seasonal average
WS was lowest in winter (1.9 ± 0.1 m/s) and highest in spring
(2.1 ± 0.1 m/s). The RH was highest in summer (77 ± 3 %) and
lowest in spring (68 ± 2 %). The winds at the LAN station mostly
originated from the northeast and southwest, as shown in Fig. 1d. On
average, the northeast and southwest winds accounted for 29.2 % and
22.6 % of the winds, respectively. The calm wind frequency was 4 %.
Seasonal variations in (a) temperature, air pressure, (b) WS, (c)
RH, and (d) WD frequency distribution (the static wind frequency was 4 %)
at the LAN station from 2006 to 2017 (an error bar represents 1 standard
deviation).
A gas-filter correlation infrared absorption analyzer (48C trace level,
Thermo Fisher, USA) was used to measure the surface CO mixing ratios. The
analyzer has a limit of detection of 0.04 ppm. Infrared radiation is chopped
and passed through a rotating gas-filter lens, half of which is filled with
CO and half with nitrogen. Thus, reference and measurement beams are
produced in alternation. The beams then pass through a narrow-band
interference filter and sample cell. Because the CO in the sample cell can
only absorb the measurement beam, and the other gases can absorb both beams,
the measurement signal of CO could be obtained by comparing the attenuation
intensity between the reference and measurement beams.
The measurement signal from the CO analyzer was recorded every 5 min. Zero
check and span check were conducted every 6 and 24 h, respectively.
Multipoint (>5) calibration was performed once a month using
a standard CO gas mixture (CO in nitrogen). Because the zero point of the
instrument drifted with time, we performed linear interpolation between two
adjacent zero checks to obtain the zero signals for a given time point between
the zero checks. These zero signals were used in the corrections of the CO
data. We performed response correction according to the results of
multipoint calibrations as well as the zero and span checks (Lin et al.,
2009). Finally, we corrected the data according to the quantity transfer and
traceability results (Lin et al., 2011a). Valid 5 min data were used to
calculate the hourly mean mixing ratios. At least 10 data points were
required for any given hour to calculate that hour's mixing ratio. Missing
data were caused by the malfunction of the instrument from 1 to 13 February 2007 and from abnormal measurement fluctuations from 30 May to 17 July 2009.
Results and discussionObserved levels and comparisons with other sites
Figure 2 displays the time series of hourly mean CO levels at the LAN station
from 1 January 2006 to 31 December 2017 and the linear fitting results of
the hourly mean CO mixing ratios. The overall mean (±1 standard
deviation) and median values of the CO mixing ratios in the 12 years were
0.62 (±0.23) ppm and 0.57 ppm, respectively. The highest (2.98 ppm)
and lowest (0.08 ppm) hourly mean mixing ratios occurred at 17:00 local time (LT) on 10 January 2008 and 18:00 LT on 4 October 2007, respectively. The highest hourly
mean CO mixing ratio was considerably lower than the second-level hourly
limit (approximately 8 ppm) of the ambient air quality standard in China (GB
3095-2012). The highest (2.38 ppm) and lowest (0.23 ppm) daily mean mixing
ratios occurred on 10 January 2008 and 31 August 2011, respectively. The
highest daily mean value was also below the daily limit for air quality
standard (3.2 ppm). The lowest monthly average CO concentration was 0.39 ppm
on August 2011, and the highest concentration was 1.00 ppm on January 2010.
The median of daily mean CO levels from January 2006 to December 2017 was
0.58 ppm. The overall CO concentrations at the LAN station were much higher than
those observed at the Waliguan global baseline station from 2006–2017 and
some regional background stations outside China (Table 1), indicating that
east China has been one of the regions with high CO levels. Table 1 also
presents a comparison of the seasonal average CO mixing ratios at the LAN
station and other background stations in the world from 2006 to 2017. The
seasonal CO mixing ratios at the LAN station were marginally lower than
those at the Shangdianzi station in northern China (Meng et al., 2009) but
were almost 3 times higher than those at many other regional atmospheric
background stations outside China, such as the Tae-ahn Peninsula station in
South Korea, Yonagunijima station in Japan, Park Falls (WI) station in the US,
and Payerne station in Switzerland from 2006 to 2017 (Table 1). Moreover,
the CO mixing values observed at the LAN station were nearly 5 times higher
than those observed at the Waliguan station, a global baseline station in
China. In conclusion, the CO levels at the LAN station were relatively high
compared to other regional atmospheric background stations outside China
because of more intense anthropogenic emissions in the YRD region.
Time series of the CO variations at the LAN station from 2006 to
2017.
Comparison of seasonal average CO variations at the LAN
station and other similar background stations around the world.
SiteLocationYearsPeriod TrendsRef.(yyyy/mm)Spring (ppm)Summer (ppm)Autumn (ppm)Winter (ppm)(ppb yr-1)Lin'an, China30∘18′ N, 119∘44′ E, 138 m2006–20090.65±0.040.59±0.040.65±0.080.75±0.05-11.3This study2010–20150.59±0.040.54±0.060.62±0.070.70±0.072016–20170.57±0.080.46±0.040.49±0.030.56±0.01Lin'an, China30∘18′ N, 119∘44′ E, 189 m2010/9–2012/20.47±0.010.30±0.010.41±0.000.52±0.01–Fang et al. (2014)Lin'an, China30∘18′ N, 119∘44′ E, 189 m2010/9–2017/50.38±0.000.28±0.000.37±0.000.45±0.00-16.3Liu et al. (2019)Shangdianzi, China40∘39′ N, 117∘07′ E, 293 m2006/1–2006/120.75±0.160.64±0.140.80±0.120.76±0.13–Meng et al. (2009)Shangdianzi, China40∘39′ N, 117∘07′ E, 293 m2011/12–2017/50.16±0.000.18±0.000.14±0.000.16±0.00-1.3Liu et al. (2019)Longfengshan, China44∘44′ N, 127∘36′ E, 311 m20060.210.200.270.38–Wu et al. (2008)Jinsha, China29∘38′ N, 114∘12′ E, 750 m2006/6–2007/70.440.390.660.60–Lin et al. (2011b)Waliguan, China36∘28′ N, 100∘89′ E, 3810 m2006/1–2017/120.13±0.010.13±0.010.12±0.010.12±0.01-0.67WDCGG (2020)Tae-ahn Peninsula,36.73∘ N, 126.13∘ E, 20 m2006/1–2017/120.27±0.030.19±0.040.21±0.030.23±0.02-0.43WDCGG (2020)South KoreaYonagunijima, Japan24.47∘ N, 123.01∘ E, 30 m2006/1–2017/120.18±0.030.09±0.010.13±0.020.19±0.02-0.98WDCGG (2020)Park Falls (WI), the US45.93∘ N, 90.27∘ W, 868 m2006/1–2017/120.17±0.020.16±0.030.14±0.020.16±0.02-0.96WDCGG (2020)Payerne, Switzerland46.81∘ N, 6.94∘ W, 490 m2006/1–2017/120.20±0.040.14±0.010.20±0.040.28±0.05-5.20WDCGG (2020)Seasonal variation
Figure 3 shows the seasonal variations in CO mixing ratios at the LAN station
and the number of fire emissions (retrieved from the Global Fire Emissions
Database version 4 described in van der Werf et al. (2017) in the YRD region
(22–40∘ N, 112–123∘ E) from 2006 to 2017.
Seasonal variations in CO mixing ratios at the LAN station and the
number of fire spots in the YRD region from 2006 to 2017. The lines and dots
in the box are the median and mean concentrations, respectively; the box's
lower and upper limits represent 25th and 75th percentile
concentration ranges, respectively; and the lower and upper whiskers
correspond to the 10th and 90th percentile values.
As can be seen in Fig. 3a, the average CO mixing ratios were the highest
in the winter (0.69 ± 0.08 ppm), followed by the spring (0.61 ± 0.05 ppm), autumn (0.61 ± 0.09 ppm), and summer (0.54 ± 0.06 ppm). In the winter, because of the weak radiation, the photochemical
consumption of CO in the atmosphere decreased. Also, the atmospheric
stability was high and the diffusion conditions were unfavorable. Therefore,
atmospheric CO accumulated easily and reached its maximum concentration in
the winter. In comparison, the photochemical reaction was strong in the
summer, which resulted in an increase in the mixing ratios of OH radicals
and the chemical consumption of atmospheric CO. Moreover, the boundary layer
height was relatively high in the summertime, which promoted the vertical
diffusion and dilution of CO in the atmosphere. Therefore, the CO mixing
ratios were the lowest in the summer. By contrast, the seasonal variations
in the number of fire emissions in the YRD region (Fig. 3b) were opposite to
the trend of the CO mixing ratios in different months, which indicated that
open fire burning was not a main factor affecting the atmospheric CO
concentrations at the LAN station from 2006 to 2017.
Diurnal variation
The daily variations in the CO mixing ratios were influenced by emission
sources, atmospheric transport (horizontal and vertical), and the evolution
of the atmospheric boundary layer (Xue et al., 2006). Figure 4 displays the
average daily variations in the CO mixing ratios at the LAN station, along
with the cities Shanghai (Gao et al., 2017), Nanjing (Huang et al., 2013a),
and Hangzhou (Zhang et al., 2018). As displayed in Fig. 4, the CO mixing
ratios exhibited double peaks, with higher CO levels in the morning and
evening but lower CO levels in the afternoon. The peak of the CO mixing
ratios at the LAN station mostly occurred in the morning (07:00–10:00 LT) and
at night (19:00–24:00 LT). The lowest CO mixing ratios were observed between
12:00 and 16:00 LT. The hourly CO mixing ratios usually reached their minimum
value in the afternoon due to the high atmospheric boundary layer, intense
vertical diffusion mixing, and sufficient OH radicals at that time (Fang et
al., 2014). The planetary boundary layer height (PBLH) is a key indicator of
atmospheric mixing state. As shown in Figs. S1 and S2 in the Supplement, the PBLH was
rather high during the daytime and usually reached its highest around 14:00 LT,
which indicated that the pollutants in the atmosphere were well mixed in the
afternoon and corresponded to the time when the lowest CO mixing ratios were
observed (Fig. 4). Since the diurnal variations in the PBLHs at four sites
were almost similar according to the hourly resolution (Figs. S1 and S2), the little phase shift in the CO mixing ratio peak between different
sites was likely attributed to the difference in local emissions. The peak
CO mixing ratios at the LAN station occurred during the morning and evening
rush hours. This is consistent with those observed in the urban areas of
Shanghai (Gao et al., 2017), Nanjing (Huang et al., 2013a), and Hangzhou
(Zhang et al., 2018) (Fig. 4). Thus, the CO mixing ratios at the LAN station
were affected by the pollutant emissions related to transportation in the
surroundings. However, the peak–valley difference of CO at LAN was much
smaller than those found in the cities, reflecting reduced impacts from
direct emissions on this background site.
Average diurnal variations in CO mixing ratios from 2006 to 2015 in
Shanghai, from January to December 2011 in Nanjing, from January to December 2013 in Hangzhou, and from 2006 to 2017 at the LAN station. The
lines and red dots in the box are the median and mean CO concentrations at
the LAN station, respectively; the box's lower and upper limits represent
25th and 75th percentile concentrations, respectively; and the
lower and upper whiskers correspond to the 10th and 90th
percentile values.
Long-term trendsTrends of annual means
Figure 5 shows the change in the annual mean CO mixing ratios at the LAN
station from 2006 to 2017. The CO levels varied across the years. The World
Expo was held in Shanghai from May to October 2010, when air pollution
prevention and control measures were strengthened in Shanghai and its
surrounding areas. Because of these strengthened measures, the number of
days with good air quality reached its highest value since 2001 (Huang et
al., 2013b). Figure 5 also indicates that the average CO mixing ratio in 2010
was lower than those from 2006 to 2009 (1.5 months of data were missing for
the summer of 2009). The CO level continued to decline in 2011 but increased
in 2012, after which the CO level decreased steadily. China officially
implemented the Action Plan for The Prevention and Control of Air Pollution
in 2013 (State Council of the People's Republic of China, 2013), which comprehensively intensified air pollution control efforts and
reduced multi-pollutant emissions. The plan called for 5-year efforts to
improve overall air quality and significantly reduce heavy pollution. As
illustrated in Fig. 5, the effects of the aforementioned action plan began
to be observed in 2014, and the CO mixing ratios started to decline
significantly. Overall, the annual average of CO at LAN showed a decrease
trend of 11.3 ppb yr-1 (p<0.01) during 2006–2017. For the period
2010–2017, we obtained a trend of -14 ppb yr-1. This rate of decline in the CO
mixing ratio was slightly lower than that (-16.3 ppb yr-1) reported by Liu
et al. (2019) for the same station for 2010–2017. The measurements of Liu et
al. (2019) were performed using a cavity ring-down spectrometer, their air
samples were drawn from a tower (intake height: 50 m a.g.l.), and their trend
was based on non-linear fitting on CO values after removing those impacted
by local events. The CO decreasing trend obtained in this study is smaller
than those reported by Ahmed et al. (2015) with values of -20 and
-13 ppb yr-1, respectively, for two urban sites in South Korea during
2004–2013, larger than that reported by Liu et al. (2019) with a value of
-1.3 ppb yr-1 for a regional atmospheric background station in northern
China during 2011–2017 and about a factor of 2–26 of those found in
regional atmospheric background stations in South Korea, Japan, and Switzerland
(Table 1).
Variation in the annual mean CO mixing ratios at the LAN station
from 2006 to 2017 (the error bars represent 1 standard deviation
calculated from monthly means).
Considering the variation trend in Fig. 5 and the major air pollution
control policies adopted during the study period, we divided the study data
into three subsets of data (collected during 2006–2009, 2010–2015, and
2016–2017, respectively). The frequency distributions of average daily CO
mixing ratios in the three data subsets and the Lorentz curve fitting
results are displayed in Fig. 6. Approximately, a unimodal structure of CO
frequency distribution was observed for all the datasets. The peak values of
the Lorentz curves can be used to characterize the background concentration
levels of atmospheric pollutants for a specific time and region (Lin et al.,
2011b). The peak of the CO Lorentz curve shifted towards lower mixing ratios
over time, and the trailing phenomenon of the fitting curve diminished
gradually. The peak concentration of the fitting curve was 0.59 ± 0.01 ppm from 2006 to 2009. During 2010–2015 and 2016–2017, the peak CO
concentrations were 0.56 ± 0.01 and 0.49 ± 0.01 ppm,
respectively. The peak frequency of the Lorentz curve was higher in
2016–2017 than in 2006–2015. Moreover, the peak width was significantly
narrower in 2016–2017 than in 2006–2015. These result from a
decrease over time in the regional background mixing ratios of CO.
Frequency distribution of the CO mixing ratios and Lorentz curve
fitting results for different time intervals.
Trends of seasonal means
The time series of seasonal average levels of CO at the LAN station from
2006 to 2017 are displayed in Fig. 7. Linear trends were calculated from the
seasonal data, with the standard deviation of monthly mean values being used as a
weighting factor. From 2006 to 2017, the seasonal CO mixing ratios
exhibited larger fluctuations; nevertheless, an overall significant (p<0.05) decreasing trend was observed in seasons except for the
winter. The largest decrease (the slope of linear fitting) in the seasonal
CO levels occurred in autumn (-15.7 ppb yr-1), followed by summer (-11.1 ppb yr-1), spring (-10.8 ppb yr-1), and winter (-9.7 ppb yr-1). As indicated
in Table 1, the CO mixing ratios at the LAN station in the four seasons
between 2016 and 2017 were lower than those between 2006 and 2015, with the
largest average decrease of 0.19 ppm occurring in winter.
Seasonal time series and linear fitting of CO mixing ratios at the
LAN station (spring: March to May, summer: June to August, autumn: September to
November, and winter: December to February).
Trends of CO levels under clean and polluted conditions
In the annual statistics, the 95th and 5th percentiles of the CO mixing
ratios can be viewed as the CO levels in the most polluted and clean
(background) air masses, respectively. Here, we use these two quantities to
study CO trends under polluted and clean conditions, respectively, at the
LAN station. As illustrated in Fig. 8a, the CO concentration under the
polluted conditions experienced a significant decreasing trend of -22.4 ppb yr-1 (r=-0.68, p<0.05) from 2006 (0.91 ppm) to 2017 (0.58 ppm), and under the clean condition it descended as well but not
statistically significantly (r=-0.41, p=0.19) throughout the years.
This suggests that the CO levels in pollution plumes, which are highly
impacted by anthropogenic emissions in the YRD region, have been reduced
greatly, and the background levels of CO at the LAN station showed a
decreasing trend at the same time. Figure 8b shows the average CO
concentrations from prevailing (N, NNE, NE, S, SSW, and SW) and other wind
directions. As can be seen in Fig. 8b, the annual CO levels from
different wind directions generally presented similar patterns, and all of
them exhibited a significant (p<0.01) downward trend, suggesting
that the CO concentrations in the provinces and cities surrounding the LAN
station have all decreased.
Trends of CO mixing ratios at the 95th and 5th percentiles and from
different wind directions.
Causes and implications of the long-term variationsImpacts of Shanghai Expo and G20 in Hangzhou
During the Shanghai Expo in 2010 (from 1 May to 31 October) and Hangzhou G20
in 2016 (from 24 July to 6 September), the Chinese government
implemented a series of joint pollution control measures in the cities of
the YRD region to ensure good air quality during these mega-events. A
satellite-based study (Hao et al., 2011) reported that a 12 % reduction of
CO concentration was observed over Shanghai city during the Expo compared to
the past 3 years. Zhao et al. (2017) found that the ground CO levels in
Hangzhou city decreased by 56 % during G20 as opposed to those in 2015. In
order to further evaluate the effect of these control strategies, we
compared the annual trends of CO concentrations at the LAN station during
the same period as the Shanghai Expo and Hangzhou G20, which are shown in Fig. 9a and b, respectively. The concentration of CO at the LAN station was
0.54 ppm during the Expo and 0.41 ppm during the G20, and the values were
lower than those observed in Shanghai city (0.86 ppm) and Hangzhou city
(0.53 ppm) in the same period. Sharp decreases (reductions of 18 % during
the Expo in 2010 and 35 % during the G20 in 2016) of the CO mixing ratios
were observed at the LAN station compared to those during the same periods
in the previous years. Since the meteorological conditions (the average
values and standard deviations of temperature, air pressure, wind speed,
relative humidity, and the wind direction frequency; see Table S1 and Fig. S3) between the Shanghai Expo and Hangzhou G20
and the same periods in the previous year were quite close, the results
indicated that the pollution control measures worked well to reduce
atmospheric CO concentrations in the YRD region.
Average CO levels for the periods corresponding to (a) the 2010 Shanghai
Expo (from 1 May to 31 October) and (b) 2016 Hangzhou G20 (from 24 July to 6 September).
Relationships with meteorological conditions
Atmospheric CO mixing ratios are not only affected by local emission sources
and the mixing ratios of atmospheric OH radicals but also by meteorological
conditions. Temperature, WS, WD, and other meteorological conditions
directly affect atmospheric stability and photochemical reaction intensity,
which influence the diffusion, generation, consumption, and lifetime of
atmospheric CO (Seinfeld and Pandis, 2006). Meteorological conditions
varied across the years of our study period. Such variations affected the
comparison of the atmospheric CO mixing ratios between different time
intervals, especially when analyzing or evaluating the effectiveness of
pollution control policies. To minimize the effects of meteorological
conditions on the analysis results, we took temperature, WS, and WD as
classification variables and analyzed the variation in the CO mixing ratios
under similar meteorological conditions during the three periods. The
results are displayed in Fig. 10.
Variations in CO mixing ratios in different periods with respect to
temperature (T), wind speed (WS), relative humidity (RH), and wind direction
(WD). The intervals are 5∘, 0.5 m/s, 10 %, and 22.5∘
for T, WS, RH, and WD, respectively.
As displayed in Fig. 10a, the plot of the CO mixing ratios versus the
temperature showed a convex shape, with relatively low concentrations
occurring at both high and low temperatures. Generally, because the
photochemical reaction of CO intensifies at extremely high temperatures, and
strong winds occur at extremely low temperatures, both high temperatures and
strong winds can cause low CO mixing ratios. The decrease in the CO mixing
ratios in a relatively high-temperature range during 2016–2017 was lower
than the corresponding decreases in previous years. This result might be
attributable to the summertime increase in energy consumption from the
widespread use of air conditioners in China. Compared with 2006–2015, the
stable area with high CO mixing ratios started to appear at lower
temperatures during 2016–2017, which reflected the effectiveness of
pollution control measures on the large emission sources. As displayed in
Fig. 10b, as the WS increased within a given range, the CO mixing ratios
gradually decreased because of the strengthened diffusion and dilution of
the atmosphere. When WS increased to a given level, where this level
differed between the time intervals and continually decreased overtime, the
CO mixing ratios increased with WS. This may be attributable to the
pollution sources being increasingly close to the LAN station because of
increased urbanization over time. At a WS of 6–7 m/s, the CO mixing ratios
in the different time intervals tended to be consistent. As the WS continued
to increase to approximately 8 m/s, the atmospheric CO mixing ratios
significantly decreased with the WS. As displayed in Fig. 10c, the CO
mixing ratios correlated positively with RH, which is consistent with the
results reported by Turkoglu et al. (2004) and Ye et al. (2008). The main
sink of CO is the oxidation reaction with OH radicals (Seinfeld and Pandis, 2006). Because water vapor is a precursor of clouds, at higher
levels of RH, the atmosphere is more likely to be oversaturated with water
and form clouds. Furthermore, because clouds can reflect sunlight and reduce the
ultraviolet radiation reaching the ground, the photochemical reaction
between CO and OH radicals is weakened (Ye et al., 2008). Figure 10d
displayed the change in CO mixing ratios with respect to WD. The figure
indicates that CO levels were the highest in the south sector of the LAN
station.
Table 2 summarized the average percentage decrease in the CO mixing ratios
during 2010–2015 and 2016–2017 relative to CO mixing ratios in the
previous time intervals under the same meteorological conditions
(temperature, WS, RH, and WD). As indicated in Fig. 10 and Table 2, the CO
mixing ratios during 2016–2017 were generally lower than those during
2006–2009 and 2010–2015. Therefore, the meteorology was not the main
factor contributing to the descending trend of CO.
Comparison of the average percentage decline in CO mixing ratios
during 2010–2015 and 2016–2017 relative to CO mixing ratios in previous
time intervals under the same meteorological factors.
a Compared with 2006–2009. b Compared with 2010–2015.
Changes in emissions in neighboring provinces
China has implemented a comprehensive energy conservation and emission
reduction policy since 2006 (Zhao et al., 2008; Lei et al., 2011). Small and
old factories and boilers have been gradually replaced by larger and more
energy-efficient alternatives. Although the focus of these measures was to
control sulfur dioxide emissions, these measures also greatly improved
combustion efficiency and thus decreased CO emissions (Zhao et al., 2012).
Figure 11 displays the change in the CO emissions in six provinces and cities
around the LAN station from 2006 to 2017. The emission data were obtained
from the Multiresolution Emission Inventory for China (Li et al., 2017). As
indicated in Fig. 11, the average annual CO emissions of the provinces and
cities surrounding the LAN station declined significantly (r=-0.95, p<0.01), with an average decline of 170 000 t yr-1. The percentages
of CO emission decreased during 2016–2017 in Shanghai city, and in
Jiangsu, Zhejiang, Anhui, Fujian, and Jiangxi provinces the percentages were -59.3 %,
-25.5 %, -18.6 %, -27.2 %, -40.1 %, and -19.3 %,
respectively, relative to CO emission values during 2006–2009.
There was a strong positive correlation (r=0.83, p<0.01)
between the annual mean CO concentrations and the anthropogenic emissions of
CO in the neighboring provinces. Also, compared with the base year of 2006,
the CO concentration in 2017 declined by 18.7 %, which is close to the
decline value of 31.3 % for the average anthropogenic emissions of CO in
the neighboring provinces. The decreasing percentage of the CO
concentrations and the emissions was overall consistent when considering
larger uncertainty existing in emission. Therefore, the declined trend of CO
at the LAN station might be mainly attributed to the decrease in
anthropogenic emissions in the YRD region.
CO emissions from 2006 to 2017 in the provinces and cities
surrounding LAN station and linear fitting of the average annual CO
emissions of the six provinces and cities. Data source: http://meicmodel.org/dataset-mix.html (last access: 1 October 2020).
Implications on regional atmospheric chemistry
The tropospheric CO has been measured on a global scale from the
Measurements Of Pollution In The Troposphere (MOPITT) instrument on the
spacecraft since 2000 (Deeter et al., 2017). Monthly CO mixing ratios at the
surface layer and the CO total column concentrations over the YRD region
from 2006 to 2017 were retrieved from MOPITT (MOPITT Algorithm Development Team, 2018;
http://www.satdatafresh.com/CO_MOPITT.html, last access: 1 October 2020). We found
significant correlations (p<0.05) between surface CO and MOPITT CO
(r=0.75 and 0.61 for the MOPITT CO mixing ratio and total column,
respectively) data (see Fig. S4), which indicate the good regional
representativeness of Lin'an measurements. From 2006 to 2017, the average CO
mixing ratio from MOPITT over the YRD region (22.5–39.5∘ N, 112.5–123.5∘ E) in 2006 (0.11 ± 0.02 ppm) was higher than that in
2017 (0.10 ± 0.02 ppm), with a significant declining trend of -0.5 ppb yr-1 (r=-0.82, p<0.01). As for the average CO total column,
the value in 2006 (1.91 × 1018± 0.23 × 1018 molecules/cm2) was also higher than that in 2017 (1.76 × 1018± 0.21 × 1018 molecules/cm2), with a
significant declining trend of -1.07×1016 molecules/(cm2⋅ yr) (r=-0.70, p<0.05) from 2006
to 2017. They are consistent with the negative trends of the ground CO
levels measured in the sites of the WDCGG network (Table 1) and at the LAN
station. Although negative trends in both surface and MOPITT CO data
were found, their relative decline percent was different. Compared with the
base year of 2006, the surface CO declined by 1.6 % annually and MOPITT CO
declined by 0.4 % (in mixing ratio) and 0.6 % (in total column),
respectively.
The major sink for CO is reaction with OH radical (Seinfeld and Pandis,
2006), so a decrease in the CO concentrations may lead to an increase in the
lifetime of OH radical and thus affect the atmospheric OH photochemistry
(i.e., ozone production). The lifetime of OH is defined as the inverse of
the OH reactivity (i.e., OH loss rates), and the total OH reactivity is
calculated by summing over all the products of the OH reactant (CO,
volatile organic compounds, nitrogen oxides, etc.) concentrations times
their respective rate coefficients with OH (kOH) (Kovacs and Brune,
2001; Di Carlo et al., 2004). The lowest average total OH reactivity (5–6 s-1) was observed in the rural areas around the
world (Ren et al., 2005; Ingham et al., 2009). The kOH of CO is 350/(ppm ⋅ min) at the standard temperature of 298 K (Vukovich, 2000), and
CO generally contributed 10 %–20 % to the total OH
reactivity at the rural sites of China (Lou et al., 2010). From 2006 to
2017, the average OH reactivity of CO at the LAN station exhibited a
significant downward trend of -0.07 s-1 yr-1 (r=-0.80, p<0.01) and the average monthly OH reactivity of CO dropped from 4.1 ± 0.7 s-1 in 2006 to 3.0 ± 0.3 s-1 in 2017.
Conclusion
The average annual levels of CO at the LAN station during 2006–2009,
2010–2015, and 2016–2017 were 0.66 ± 0.03 ppm, 0.62 ± 0.03
ppm, and 0.52 ± 0.01 ppm, respectively. From a seasonal perspective,
the highest seasonal average CO mixing ratio occurred in winter (0.69 ± 0.08 ppm), followed by spring (0.61 ± 0.05 ppm), autumn (0.61 ± 0.09 ppm), and summer (0.54 ± 0.06 ppm). The average daily
variations in the CO concentration exhibited a double-peaked pattern, with
high CO concentrations in the morning and evening and low CO concentrations
in the afternoon. Such diurnal variations suggest that the CO mixing ratios
at the LAN station were affected by traffic pollutant emissions in its
surrounding area.
The average annual atmospheric CO mixing ratios at the LAN station exhibited
a significant decreasing trend (-11.3 ppb yr-1, p<0.01) from 2006
to 2017, which was consistent with the negative trends of the average CO
mixing ratios and total column retrieved from MOPITT over the YRD region.
The measurements at the LAN station reflected regional changes in
atmospheric background CO mixing ratios in the YRD region well. The largest
decrease in the CO level was observed in autumn (-15.7 ppb yr-1), followed by
summer (-11.1 ppb yr-1), spring (-10.8 ppb yr-1), and winter (-9.7 ppb yr-1). The
significant downward trend of the CO mixing ratios at the LAN station was
not caused by meteorological conditions but by strengthened pollution
control measures, which indicated that the adopted measures were effective.
In spite of the nearly a quarter of reduction during 2006–2017, the CO
levels at the LAN station were still much higher than those at other
regional atmospheric background stations around the world so that further
reductions in CO emissions in the YRD region are needed. The significant
decrease in regional CO level has an implication for atmospheric chemistry,
considering the role of CO in OH reactivity. From 2006 to 2017, the average
OH reactivity of CO at the LAN station exhibited a significant downward
trend of -0.07 s-1 yr-1 (r=-0.80, p<0.01) and dropped from
4.1 ± 0.7 s-1 in 2006 to 3.0 ± 0.3 s-1 in 2017.
Data availability
Our measurement data are deposited to an accessible
repository. The data sources of number of fire emissions (https://www.geo.vu.nl/~gwerf/GFED/GFED4) (van der Werf et al., 2017), the annual CO
emissions (http://meicmodel.org/dataset-mix.html) (Li et al., 2017), and the CO concentrations retrieved from MOPITT (http://www.satdatafresh.com/CO_MOPITT.html) (MOPITT Algorithm Development Team, 2018) results over the YRD
region are all listed in the reference, and the CO concentrations and the
meteorological data at the LAN station can be inquired about by contacting
the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-15969-2020-supplement.
Author contributions
YC, WL, and XX developed the idea for this paper
and formulated the research goals. QM and JY carried out the CO field
observations at the LAN station. WG provided the CO data in Shanghai. YC
and WL wrote and revised the manuscript with contributions from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was funded by the National Key R&D Program of
China (2016YFC0201900), National Natural Science Foundation of China
(91744206), and Beijing Science and Technology program (Z181100005418016).
We thank the personnel on duty at the LAN station for their assistance. A previous version of this
paper was edited by Wallace Academic Editing.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2016YFC0201900), the National Natural Science Foundation of China (grant no. 91744206), and the Beijing Science and Technology program (grant no. Z181100005418016).
Review statement
This paper was edited by Steven Brown and reviewed by two anonymous referees.
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