Introduction
The extraordinarily rapid development of China has caused extremely high
aerosol loading and gaseous pollutant emissions that have enveloped most
regions across China in recent decades. The increased pollutant emissions,
particularly for the particulate matter finer than 2.5 µm in
aerodynamic diameter (PM2.5), generally result in severe haze events and
present a major threat to public health (Gao et al., 2017; Tang et al., 2017;
Wang, 2018), crop production (Tie et al., 2016), and regional climates (Cao
et al., 2016). For example, the annual averaged PM2.5 in Beijing
exceeded 75 µg m-3 during 2009–2016 (Fig. 1b), which is more
than 3 times the recommended 24 h standard (25 µg m-3) of
the World Health Organization (WHO). This degeneration of the air pollution
across China, which is similar to that in Beijing, is primarily caused by the
integrated effects of high emissions and poor ventilation (Chen and Wang,
2015; Y. Zhang et al., 2016). Many efforts are thus underway to reduce
emissions that cause severe haze pollution. However, the question remains as
to whether climate change will offset or facilitate these efforts.
Recent studies have documented that the exacerbation of air quality over
eastern China was partly modulated by meteorological conditions and climate
variability that are generally conducive to the severe haze occurrences (Li
et al., 2018; Liao and Chang, 2014; Wang and Chen, 2016; Yang et al., 2016;
Zhang et al., 2014; Z. Zhang et al., 2016). Specifically, Wang et al. (2015)
revealed that the shrinking Arctic sea ice favours less cyclone activity and a
more stable atmosphere conducive to haze formation, which can explain
approximately 45 %–67 % of the interannual to interdecadal
variability of winter haze days over eastern China. Besides Arctic sea ice,
other decadal variability and changes, including a weak East Asian winter
monsoon (Jeong et al., 2017; Li et al., 2016; Yin et al., 2015), strong El
Niño–Southern Oscillation (Gao and Li, 2015; Zhao et al., 2018), high
Pacific decadal oscillation (Zhao et al., 2016), and high Arctic oscillation
(Cai et al., 2017), may have contributed. In addition, the increasing winter
haze days over eastern China may also be linked to the low boundary layer
height (Huang et al., 2018; Wang et al., 2018), weakened northerly winds
(Yang et al., 2017a), decreased relative humidity (Ding and Liu, 2014), and
increased sea surface temperature (Xiao et al., 2015; Yin and Wang, 2016; Yin
et al., 2017).
Global warming generally presents an adverse impact on the haze pollution
across China. Simulations of the dynamic downscaling by the regional climate
model RegCM4 under the RCP4.5 (Representative Concentration Pathway)
scenario have shown that the air
environment carrying capacity tends to decrease, and the weak ventilation
days tend to increase, in the 21st century across China, suggesting an
increase in the haze pollution potential compared to the current state (Han
et al., 2017). Furthermore, Cai et al. (2017) projected that the days
conducive to severe haze pollution in Beijing would increase by 50 % at
the end of the 21st century (2050–2099) under the RCP8.5
scenario compared to the
historical period.
These qualitative estimations of the haze pollution response to climate
changes are generally derived from the potential changes in the corresponding
meteorological conditions indirectly. No studies to date quantitatively
assessed the simulated PM directly. How the fine particulate matter
pollution changes in response to the global warming in China has not been
thoroughly elucidated to date. This study focuses in particular on the
anthropogenic PM2.5 loading and its response to the future warming. In
this study, the large ensemble simulations from the Community Earth System
Model Version 1 (CESM1) throughout the 21st century that are induced by
increasing greenhouse gas (GHG) emissions along the trajectory RCP8.5 but
retaining the emissions of aerosols and/or their precursors fixed at the
year of 2005 level (RCP8.5_FixAerosol2005; Xu and Lamarque,
2018) will be utilized.
Data and methods
PM2.5 observational datasets
Surface hourly PM2.5 concentration data released since 2013 are taken
from the website of the Ministry of Environmental Protection
(http://beijingair.sinaapp.com, last access: 3 January 2019), which
covers 1602 sites across China. The duration of available datasets varies
across sites because of the gradual development of the monitoring network in
recent years. In our study region of eastern China (east to 100∘ E),
there are 1263 sites remaining after the sites with missing values were
removed during 2015–2017. Additionally, surface daily PM2.5
concentrations for the Beijing, Shanghai, Guangzhou, and Chengdu cities that
had relatively longer monitoring times are also collected from the US Embassy
Beijing (http://www.stateair.net/web/historical/1/1.html, last access:
3 January 2019).
Observed PM2.5 pollution conditions over eastern China during
the past years. (a) Annual averaged PM2.5 concentration
(µg m-3) for the years of 2015–2017. (b) Variations
in annual averaged PM2.5 concentration (green bars) in Beijing city and
the corresponding number of the severe PM2.5 pollution days (red bars).
The severe pollution days are defined as the daily averaged PM2.5
concentration exceeding 75 µg m-3. Panels (c),
(d), and (e) are similar to (b), but for the
results of Shanghai, Guangzhou, and Chengdu city, respectively.
CESM1 simulations
The CESM1 is an Earth system model involving the atmosphere, land, ocean, and
sea-ice components with a nominal 1∘ by 1∘ horizontal
resolution (Hurrell et al., 2013). The RCP8.5_FixAerosol2005 simulations are
forced by the RCP8.5 scenario, but all emissions of sulfate (SO4),
black carbon (BC), primary organic matter (POM), secondary organic aerosols
(SOA; or their precursors), and atmospheric oxidants are fixed at the
present-day level (2005). These simulations include 16 ensemble members,
differing solely in their atmospheric initial conditions with a tiny random
temperature difference (order of 10-14 ∘C; Kay et al., 2015).
For comparison, the CESM1 large ensemble consists of 35-member simulations,
forced by the RCP8.5 scenario, that are also employed here. Using these relatively large ensembles can
substantially reduce the contribution of the natural variability of the
climate system to the result estimation (Xu and Lamarque, 2018).
For the aerosol emission in the RCP scenarios database, just its decadal
change is considered rather than the emission in a single year (Lamarque et
al., 2011). Here, the years of 2006–2015 are considered as the reference
period in the RCP8.5_FixAerosol2005 simulations. The differences of the mean
climates from the reference period are largely due to the increase in GHG
emissions and are not attributed to the decline in aerosol emissions, as
specified in RCP8.5. The changes in anthropogenic PM2.5 loadings and
anthropogenic air pollution days in our study are thus only a result of the
GHG-induced climate change, rather than changes in aerosol emission. Note
that just four species of PM2.5 components that show a substantial
threat to public health are considered here for analysis, including
SO4, BC, POM, and SOA from the CESM1 simulations.
Definition of the fraction of attributable risk
The influences of the GHG-induced climate changes on the anthropogenic air
pollution in China are investigated using the metric of the fraction of
attributable risk (FAR), which has been widely used for attribute analyses of
extreme climate changes (Chen and Sun, 2017; Stott et al., 2004). FAR is
defined as 1-P0/P1, where P0 is the probability of exceeding
a certain threshold during the reference period and P1 is the
probability of exceeding the same threshold during a given period. FAR thus
presents the quantitative estimations of effects of the GHG-induced climate
changes on the anthropogenic air pollution.
Definition of stagnation days
The changes in the stagnation days that were induced by the increase in GHG
emissions are also evaluated in our study to explore the possible impact of
climate change on the anthropogenic air pollution. The day is considered to
be stagnant when the daily mean near-surface wind speed is less than
3.2 m s-1, the daily mean 500 hPa wind speed is less than
13 m s-1, and the daily accumulated precipitation is less than 1 mm
(Horton et al., 2012). Earlier studies suggested that this air stagnation
definition might not be applicable for China to represent the air pollution
condition under the seasonal scales (Feng et al., 2018; Wang et al., 2018).
However, the annual mean stagnation generally presents good agreement with
that of air pollution across China (Huang et al., 2017, 2018). The changes in
the annual mean states of air stagnation over China at the end of 21st
century will thus be discussed in the following.
Plots of future changes in the total PM2.5 as well as its
associated species averaged over eastern China in terms of the surface
concentration (µg m-3, right axis in red) and column burden
(mg m-2, left axis in blue) from the simulations of the
RCP8.5_FixAerosol2005 experiment. (a) PM2.5, (b) BC,
(c) SO4, (d) POM, and (e) SOA. Ensemble
variance (1σ) for surface concentration is shown in red shading.
Results
Observational changes in PM2.5 pollution
The days of severe haze pollution increased over the past several decades
across eastern China, particularly for the episodes of January 2013,
December 2015, and December 2016, when several severe haze alerts were
reached. High PM2.5 loading was centralized over the Jing–Jin–Ji (JJJ)
region, Shangdong, and Henan provinces, as well as the Sichuan Basin (SCB,
Fig. 1a). The annual mean PM2.5 mass concentrations for most sites over
these regions exceed 75 µg m-3. According to the statistics,
at approximately 95 % of sites the annual mean PM2.5 concentration
exceeded the WHO recommended 24 h standard (25 µg m-3)
across eastern China, and there are 65 sites centralized by Beijing where the
annual mean PM2.5 concentration was larger than
75 µg m-3, which would present the possibility of exposing
people to serious health hazards (World Health
Organization, 2014).
Regarding the four economic zones of Beijing, Shanghai, Guangzhou, and
Chengdu cities in China, serious PM2.5 pollution was
reported for recent years,
especially for the Beijing and Chengdu regions (Fig. 1). Taking Beijing as an example, the annual mean PM2.5
concentration was stably exceeding 100 µg m-3, and before
2013 severe air pollution was experienced for more than half the year (>75 µg m-3). Since 2013, China's State
Council released its Air Pollution Prevention and Control Action Plan, which
requires the key regions, including the JJJ, the Yangtze River Delta (YRD),
and the Pearl River Delta (PRD) to reduce their atmospheric levels of
PM2.5 by 25 %, 20 %, and 15 %, respectively, by the end of
year 2017 (State Council, 2013). Considerable effort was made , and the PM2.5 loading and the air pollution days presented
sharp decreases in recent years. However, the strict emission policies have
substantial costs for the economic development, which cannot meet the current
requirement of the rapid development of China. Thus, scientifically
quantifying the roles of anthropogenic emissions and climate changes holds
great importance for seeking the balance between socioeconomic development
and emission reduction.
Changes in the anthropogenic PM2.5 pollution days across
eastern China from the RCP8.5_FixAerosol2005 experiment. The top
panels (a, b) show the changes in light air pollution days (>25 µg m-3) and the bottom panels (c, d) show the
results of severe air pollution days (>75 µg m-3). The
left panels (a, c) illustrate the annual averaged severe pollution
days in 2006–2015 and the right panels (b, d) show changes in the
pollution days at the end of the 21st century compared to 2006–2015. Dots
in (b) and (d) mean the changes are significant at the
95 % confidence level using Student t test for all years and ensembles.
Units: days.
Simulated changes in anthropogenic PM2.5 pollution
A strong spatial correlation (0.69) is found for the annual mean PM2.5
concentration between the site observation and median ensemble of CESM1
simulations over eastern China (Fig. S1). The high concentrations across
eastern China, including the regions centralized by Beijing and Chengdu, are
reasonably reproduced. However, a negative bias is obvious.
Earlier studies (Li et al., 2016;
Yang et al., 2017b, c) have documented that this low bias of aerosol
concentration simulated by models is much more complicated in China and the
causes mainly involve the uncertainties from aerosol emission amount,
emission injection height, lack of nitrate, and aerosol treatment in the
model as well as the coarse model resolution.
The median ensemble-mean change in the PM2.5 surface concentration
presents strong regional dependence across China, with significantly
decreasing trends over the southeastern part of eastern China and
significantly increasing trends over the other regions throughout the 21st
century (Fig. S2), even though the emissions are constant throughout the
experiment. The regional differences in the total PM2.5 changes are
mainly due to SO4, which can account for approximately 50 % of
the total PM2.5 mass (Xu and Lamarque, 2018). The species of BC and POM
are reported to significantly increase in the 21st century across eastern
China, although the aerosol emissions were fixed at the level in 2005. Figure
2 presents the simulated PM2.5 loadings from the CESM1, in terms
of column burden and surface concentration, that are significantly increasing
throughout the 21st century. The increase in the total PM2.5 is
approximately 8 % for the column burden and 2 % for the surface
concentration at the end of the 21st century (2090–2099) compared to the
current state (2006–2015). These increasing trends of PM2.5 loadings
are mainly due to the significant increases in the major PM2.5 species,
except for SOA, in which the surface concentration presents a slight
decrease. Furthermore, the increases in all major PM2.5 species in terms
of column burden (BC: 11 %, SO4: 6 %, SOA: 11 %, and POM:
11 %) show stronger than the surface concentration (BC: 4 %,
SO4: 2 %, SOA: -1 %, and POM: 4 %).
Attributable changes in anthropogenic air pollution days to the
increased greenhouse gases emissions. (a) Spatial distribution of
FAR for the changes in severe PM2.5 pollution (>75 µg m-3) at the end of the 21st century over eastern
China. (b) Regional averaged relative changes in air pollution days
(left axis in red; >25 µg m-3) and the corresponding
variation in FAR (right axis in blue). Ensemble variance (1σ) for the
relative changes in pollution days is shown in red shading. (c) is
similar to (b), but for the severe PM2.5 pollution days. Units: percentage (%).
Simulated changes in weather conditions of the air pollution across
eastern China due to the GHG-induced warming. (a) Changes in the
planetary boundary layer height (PBLH) at the end of the 21st century compared to the years of 2006–2015 from the RCP8.5_FixAerosol2005
experiment. Panels (b) and (c) are similar to (a) but for
the wind speed at near-surface and 500 hPa levels, respectively.
(d) Changes in the light precipitation days (daily accumulated
precipitation < 10 mm) at the end of the 21st century compared to
the current state. Panel (e) is similar to (d) but for the heavy
precipitation days (>10 mm). Dots in the figure mean the changes are
significant at the 95 % confidence level using Student t test for all
years and ensembles. Units: percentage (%).
Changes in the stagnant conditions across China due to the
GHG-induced warming. (a) Distribution of the relative changes in the
stagnation days at the end of the 21st century against the current state
(2006–2015). Dots mean the changes are significant at the 95 %
confidence level using Student t test for all years and ensembles.
(b) Variations in the regional averaged stagnation days over eastern
China. Ensemble variance (1σ) is shown in red shading. Panels (c),
(d), (e), and (f) are similar to (b), but
for the results of four Chinese economic zones, i.e. JJJ, YRD, PRD, and SCB.
Units: percentage (%).
For comparison, we also evaluated the future changes in PM2.5
concentrations and the associated species along the RCP8.5 forcing trajectory
from the large ensemble simulations of CESM1 (figure not shown). Different
from changes in aerosol concentrations under the fixed aerosol simulations,
the PM2.5 concentrations and the associated species present uniformly
decreasing trends across eastern China from the simulations along the RCP8.5
forcing. The decreasing trends in the RCP8.5 simulations are mainly
attributed to the prescribed decrease in aerosol forcing in the future in the RCP
database (Xu and Lin, 2017). The climate change induced by the GHG-warming
might exacerbate the air pollution, but the impacts cannot compensate the
prescribed decreasing trend of aerosol concentration.
As mentioned above, the PM2.5 surface concentration in the two economic
zones of YRD and PRD present a negative response to the GHG-induced warming,
while the corresponding column burden shows significantly increasing trends
(Fig. S3). The decreases in the surface concentration over these two zones
are primarily contributed by the changes in SO4 and SOA, while there
are no obvious trends for BC and POM (Figs. S4–S7). The robust response of
the increased surface wind speed and decreased upper-level wind speed to GHG
warming can be partly responsible for the changes in the major PM2.5
species in these two zones, which will be further discussed. Over the zones
of JJJ and SCB, both the PM2.5 concentrations and the associated major
PM2.5 species present the significantly rising trends throughout the
21st century. For the surface concentration, PM2.5 is reported to
increase by 3 % and 4 % in the regions of JJJ and SCB, respectively,
at the end of the 21st century. The BC is reported to increase by 4 % and
8 % for JJJ and SCB, respectively. The other species, such as SO4
and POM, increase by 4 % and 4 %, respectively, in the JJJ regions
and by 2 % and 9 %, respectively, in SCB regions. Relatively stronger
responses can be seen in changes in the column burden for all major species
(Figs. S4–S7). The increased concentrations of PM2.5 species finally
result in significantly increasing trends of the total PM2.5 loading
over these two regions, which will present a more direct effect on human
health.
The increase in PM2.5 surface concentration throughout the 21st century
substantially leads to the significant increase in the light anthropogenic
PM2.5 pollution days (PM2.5 > 25 µg m-3) across
the northwestern part of eastern China (Fig. 3). Due to the decrease in
PM2.5 concentration over the southeastern part of eastern China, the
light anthropogenic air pollution days can be expected to decrease in this
region. Estimation shows that the number of the light air pollution days
would be decreased by approximately 10 days at the end of the 21st century
compared to the early period of this century in the region. However, the
annual mean light air pollution days are reported to increase on average over
eastern China at the end of this century despite the aerosol emission being
constant throughout the experiment. In contrast to the light air pollution
days, the severe anthropogenic air pollution days
(PM2.5 > 75 µg m-3) show a positive response to the
GHG-induced warming across eastern China, particularly for the regions around
JJJ in which the high PM2.5 concentration was localized (Fig. 3). The
severe air pollution days are estimated to increase over this region by more than 2 days at the
end of this century when compared to the early period.
Considering the underestimation in aerosol concentration by CESM1 in
China, the percentile threshold metric is also applied here to estimate the
future changes in light (90th) and severe (99th) air pollution days. Similar
results can be obtained (Fig. S8).
Attributable changes due to the GHG warming
Although the aerosol emission was constant throughout the experiment, our
study reveals that the PM2.5 loadings and their associated pollution
days still present increases throughout the 21st century, primarily resulting
from the impact of climate change induced by the GHG
warming. One may ask how large a
contribution the climate change exerts on the changes in anthropogenic air
pollution. To quantitatively address this issue, the framework of the
“fraction of attributable risk” that has been widely used for attribute
analyses of climate extreme changes (Chen and Sun, 2017; Stott et al., 2004)
is employed in this study.
Figure 4 shows the percentage changes in the anthropogenic air pollution days
throughout the 21st century over eastern China and their associated FAR
variations. The regional averaged anthropogenic air pollution days present an
obvious increase in the 21st century as addressed above. Correspondingly,
synchronous increasing trends can be found in FAR for both light and severe
anthropogenic air pollution days. For the light pollution days, FAR is
estimated to be 28 % at the end of the 21st century, implying that
approximately 28 % of the pollution days are contributed by the climate
change that was induced by the GHG warming. For the severe pollution days, FAR shows a relatively smaller
value of approximately 11 %. Furthermore, the high FAR values are mainly
located over the regions of high PM2.5 loadings concentrated over
eastern China, suggesting considerably stronger effects of climate change in
these regions. Note that the FAR values estimated in this research may be
underestimated because the GHG-induced warming impact was involved in the
selected reference period that resulted in the overestimation of the
probability of anthropogenic air pollution days.
Effects of the changes in meteorological conditions
We further examined the changes in meteorological conditions induced by the
GHG warming that exerted effects on air pollution. Our results show that the local boundary
layer height presents as higher under the warming scenario (Fig. 5a), which
benefits the vertical transport of the air pollutant.
However, a robust negative response of the horizontal advection to the
GHG-induced warming across eastern China can be found in the troposphere
(Fig. 5b, c), facilitating air pollutant accumulation. The change in surface
wind speed in response to the GHG warming is highly similar with the
variation in PM2.5 surface concentration, with wind speed increasing in
the southeastern part of eastern China and decreasing in the northwestern
part. Variations in surface wind speeds are thus mainly responsible for the
changes in PM2.5 surface concentration over eastern China. Different
responses can be found for the tropospheric upper-level wind speeds, which
are reported to substantially decrease. These decreases would directly result
in significant increases in the stagnation days over eastern China,
particularly over the northern region and SCB (Fig. 6). The decreasing trend
of wind speed in the 21st century across China not only exists in CESM1, but also happens in the other global climate models that participated
in Coupled Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 (Jiang et
al., 2010a; Mclnnes et al., 2011), as well as in regional climate models
(Jiang et al., 2010b).
In response to the GHG-induced warming, the stagnation days over eastern
China are estimated to increase by 6 % at the end of 21st century
compared to the current period. For the specific economic zones, the
stagnation days over the SCB and JJJ regions show considerably stronger
rising trends, while relatively weaker increases are observed over the YRD
and PRD regions. The number of stagnation days is estimated to increase by
13 % and 6 % at the end of the 21st century for the SCB and JJJ
regions, respectively. Briefly, though the atmospheric stratification
appears to be considerably more unstable in response to the GHG warming, the
weakened horizontal advection would substantially increase the stagnation
days over eastern China, which provides a beneficial background for the air
pollutant accumulation and further increases the occurrence probability of
the anthropogenic air pollution events.
Earlier studies have documented a
significant increase in total precipitation across China due to the
GHG-induced warming (Chen, 2013; Li et al., 2018; Wang et al., 2012), which
seems to represent a conflict with the increase in the anthropogenic air
pollution days. To resolve this issue, the precipitation changes in terms of
light precipitation days (daily accumulated precipitation < 10 mm) and
heavy precipitation days (>10 mm) are further examined (Fig. 5d, e).
Clearly, the heavy precipitation days present an increase, while the light
precipitation days show a decrease, across eastern China in response to the
warming. Though the precipitation shifts toward heavy precipitation events,
its cleansing impact on air pollutants has not increased because an increase
in heavy precipitation days appears to be insufficient to further enhance the
wet removal ability (Xu and Lamarque, 2018). In contrast, the decrease in
light precipitation days substantially weakens the wet deposition of air
pollutants, leading to the increase in the PM2.5 loading, as well as
anthropogenic air pollution days. The future changes in precipitation days
presented here are robust. Both the increasing trends of heavy precipitation days and the
decreasing trends of light precipitation days are also obvious across China,
simulated by the CMIP5 models (Chen and Sun, 2013, 2018) as well as the
regional climate models (Gao et al., 2012).
Conclusions
The world is projected to
experience increased disasters, such as heat waves, flash floods, and storms,
due to the continuous global warming induced by the GHG increase. The
research question we aim to address in this study is how the GHG warming
would affect the anthropogenic PM2.5 pollution across China. Our
evaluations show that the anthropogenic PM2.5 loadings, as well as the
anthropogenic PM2.5 pollution days, would increase under the global
warming conditions, even the aerosol emissions fixed at current levels. More
stringent regulations are thus suggested for regional aerosol emissions to
maintain the air quality standard as the current state.
The climate changes induced by the GHG warming exert their effects on the anthropogenic air pollution across
eastern China via two ways that are of interest in this study. First, the
weakened tropospheric wind speed induced by the GHG warming would result in a
decrease in the horizontal advection and lead to an increase in the number of
stagnation days, facilitating the local accumulation of air pollutants.
Second, the number of light precipitation days would decrease due to
the GHG-induced warming, although the
total precipitation would clearly increase across China. This shift toward
more no-rainfall days would further weaken the wet deposition of PM2.5
pollutants. Thus, the increased stagnation days and decreased light
precipitation days provide a beneficial background for the occurrence of
anthropogenic air pollution. Of course, under the warming scenarios, a large
discrepancy exists among the different meteorological processes that benefit
the air pollution at the current state, leading to the fuzzy recognition of
air pollution change. For example, the boundary layer height shows an
increase in response to the GHG warming that may strengthen the vertical
dissipation of air pollutants. Thus, more studies are suggested in the future
to further understand the mechanisms governing air quality across China.