In his study, we use a combination of multivariate statistical methods to
understand the relationships of PM2.5 with local meteorology and
synoptic weather patterns in different regions of China across various
timescales. Using June 2014 to May 2017 daily total PM2.5 observations
from ∼ 1500 monitors, all deseasonalized and detrended to focus
on synoptic-scale variations, we find strong correlations of daily PM2.5
with all selected meteorological variables (e.g., positive correlation with
temperature but negative correlation with sea-level pressure throughout
China; positive and negative correlation with relative humidity in northern
and southern China, respectively). The spatial patterns suggest that the
apparent correlations with individual meteorological variables may arise from
common association with synoptic systems. Based on a principal component
analysis of 1998–2017 meteorological data to diagnose distinct
meteorological modes that dominate synoptic weather in four major regions of
China, we find strong correlations of PM2.5 with several synoptic modes
that explain 10 to 40 % of daily PM2.5 variability. These modes
include monsoonal flows and cold frontal passages in northern and central
China associated with the Siberian High, onshore flows in eastern China, and
frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH)
region as a case study, we further find strong interannual correlations of
regionally averaged satellite-derived annual mean PM2.5 with annual mean
relative humidity (RH; positive) and springtime fluctuation frequency of the
Siberian High (negative). We apply the resulting PM2.5-to-climate
sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled
Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict
future PM2.5 by the 2050s due to climate change, and find a modest
decrease of ∼ 0.5 µg m-3 in annual mean
PM2.5 in the BTH region due to more frequent cold frontal ventilation
under the RCP8.5 future, representing a small “climate benefit”, but the
RH-induced PM2.5 change is inconclusive due to the large inter-model
differences in RH projections.
Introduction
Air pollution caused by high surface concentrations of particulate matter
(PM) and ozone in megacities are of utmost public health concern in China
currently (Xu et al., 2013). China has experienced deteriorating air quality
since the 1990s due to rapid industrial and economic development. Episodes of
haze and smog pollution with dangerous levels of fine particulate matter
(PM2.5, particles with an aerodynamic diameter of or less than
2.5 µm) are becoming more common in the most developed and highly
populated city clusters in China (Chan et al., 2008; Zhang et al., 2007; Q.
Zhang et al., 2014). For example, annual mean PM2.5 concentration in
Beijing increased dramatically from 12 µg m-3 in 1973 to
66 µg m-3 in 2013 (Han et al., 2016), with an average growth
rate of +0.7 µg m-3 yr-1 for the past 4 decades.
Outdoor air pollution in China alone has been shown to cause over 1 million
premature deaths every year (Cohen et al., 2017). Many epidemiological
studies have documented the harmful effects of PM2.5 on cardiovascular
and respiratory health (Cao et al., 2012a; Krewski et al., 2009; Madaniyazi
et al., 2015; Pope and Dockery, 2006). Urban PM2.5 originates from many
sources including power plants, industry, vehicular emissions, road and soil
dust, biomass burning, and agricultural activities (Zhang et al., 2015), but
the regional concentrations are also strongly dependent on pan-regional
transport (e.g., Jiang et al., 2013) and ventilation by atmospheric
circulation (e.g., Chen et al., 2008; Zhang et al., 2012, 2016).
The severity of PM2.5 pollution is known to be strongly dependent not
only on emissions but also on weather conditions. For example, Zhang et
al. (2016) showed using GEOS-Chem that cold surge occurrences over northern
China explain about half of the variability of total PM2.5. Several
modeling studies have examined the effects of historical (Fu et al., 2016)
and future (Jiang et al., 2013) changes in emissions and climate (i.e.,
long-term changes in weather statistics) on PM2.5 air quality in East
Asia, but large uncertainty remains due to the complexity of
PM2.5–meteorology interactions (Jiang et al., 2013; Shen et al., 2017;
Tai et al., 2012b). Such poor understanding stems mainly from the diverse
sensitivities of different PM2.5 chemical components to meteorological
changes, and from the strong coupling of PM2.5 with synoptic circulation
and the hydrological cycle. In this study, we apply a combination of
multivariate statistical techniques to identify important local-scale
meteorological variables and synoptic-scale meteorological modes that
dominantly control the daily and interannual variability of PM2.5 in
China, and illustrate how these modes enable effective diagnosis of the
effects of future synoptic circulation changes on China PM2.5 air
quality.
Local meteorological conditions are known to strongly influence the levels of
all air pollutants including PM2.5. PM2.5–meteorology interactions
are complex due to the varying responses of PM2.5 species to different
meteorological variables. Higher temperature favors the formation of sulfate
and secondary organic aerosols due to the faster oxidation of sulfur dioxide
(SO2) and volatile organic compounds (VOCs; Jacob and Winner, 2009).
Higher temperature also increases the emissions of biogenic VOCs from
vegetation, especially in southern China where high-emitting broadleaf
evergreen trees are prevalent (Ding et al., 2012; Zhang and Cao, 2015).
Higher temperature favors the volatilization of nitrate, ammonium, and
semivolatile organics by shifting the gas–aerosol phase equilibria toward the
gas phase (Jiang et al., 2013; Shen et al., 2017), thereby decreasing these
components. Depending on the region, an increase in relative humidity (RH)
may enhance the production of hydroxyl (OH) radical and hydrogen peroxide
(H2O2), which promotes SO2 oxidization and increases the
uptake of semivolatile components including nitrate and organics (Seinfeld
and Pandis, 2016). Precipitation, via its direct scavenging effect, is a
principal sink for all PM2.5 components (Koch et al., 2003; Tai et al.,
2010). Meanwhile, both strong wind and boundary layer mixing also tend to
ventilate or dilute PM2.5 (Chen et al., 2008; Jacob and Winner, 2009;
Wang et al., 2012; Zhang and Cao, 2015). For instance, Han et al. (2016)
found that annual mean PM2.5 and wind speed in Beijing on stable
meteorological days were negatively correlated over 1973–2013, illustrating
the importance of ventilation on interannual PM2.5 variability.
Average (a) site and (b) gridded
2.5∘× 2.5∘ total PM2.5 concentrations
(µg m-3) of China during the years 2015–2016 obtained from
the Chinese Ministry of Environmental Protection (MEP, http://pm25.in;
last access: 2 July 2017). Gridded data are obtained by spatially
interpolating site data using an inverse weighting method as in Tai et
al. (2010). The four main regions of our study are indicated in panel
(b): Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD),
the Pearl River Delta (PRD), and the Sichuan Basin (SCB).
In addition to local meteorological conditions, synoptic-scale circulation
patterns also play important roles in driving PM2.5 variability.
Different classification schemes for a wide range of synoptic circulation
patterns have been researched extensively (Huth et al., 2008) and used
worldwide to evaluate pollution–meteorology interactions (e.g., McGregor and
Bamzelis, 1995; Shahgedanva et al., 1998; Shen et al., 2017; Tai et al.,
2012a; Zhang et al., 2012). Tai et al. (2012a) showed that cold fronts
associated with midlatitude cyclone passages and maritime inflows were the
major ventilation mechanisms of PM2.5 in the US. Shen et al. (2017)
further showed that the variability of PM2.5 over the USA explained by
both local meteorology and synoptic factors (43 %) are on average about
10 % higher than solely using local meteorology (34 %). In Asia, Chen
et al. (2008) demonstrated that synoptic high-pressure systems in northern
Mongolia associated with cold fronts facilitate the dispersion of air
pollutants over northern China, whereas a surface high centered on Beijing–Tianjin–Hebei (BTH) favors
accumulation. Zhang et al. (2013) showed similar results by extracting nine
distinct synoptic pressure patterns over the North China Plain (NCP), and
discovered that weak pressure tendency in NCP favors pollutant accumulation.
Zhang et al. (2016) found that a cold surge associated with the East Asian
winter monsoon significantly reduced PM2.5 concentration in Beijing by
110 µg m-3 within a few days. Moreover, the effects of local
meteorology and synoptic circulation are not independent of each other. For
instance, Tai et al. (2012a) found that much of the apparent observed
correlation of PM2.5 with temperature and pressure in the eastern USA are
attributable to common association with cold frontal passages. To understand
how meteorological changes may affect future PM2.5 air quality,
therefore, requires keen consideration of the co-variation of meteorological
variables with synoptic-scale phenomena in an integrated framework (Jiang et
al., 2005).
Meteorological variables considered in this studya.
VariableMeteorological parameter (abbreviation, unit)X1Surface air temperature (T or SAT, K)bX2Surface air relative humidity (RH, %)bX3Surface precipitation rate (prec., mm d-1)bX4Sea level pressure (SLP, hPa)X5Sea level pressure tendency (dP/dt, hPa d-1)X6Surface wind speed (wind, m s-1)b,cX7West–east direction indicator (cosθ, dimensionless)X8South–north direction indicator (sinθ, dimensionless)
a From the National Center for Environmental Prediction/National Center
for Atmospheric Research (NCEP/NCAR) Reanalysis 1 for 1998–2017. All data
are 24 h averages and are deseasonalized as described in the text.
b Surface data are from 0.995 sigma level. c
Calculated from the horizontal wind vectors (u, v).
dθ is the angle of the horizontal wind vector
counterclockwise from the east. Positive values of X7 and X8
indicate westerly and southerly winds, respectively.
In this study, we perform correlation analysis to estimate the sensitivities
of observed daily total PM2.5 to a suite of meteorological variables
from June 2014 to May 2017. As discussed in Sect. 3, however, correlations
between local meteorology and PM2.5 are complicated by co-variations
among individual meteorological variables, which are at least partially
driven by synoptic systems. We therefore apply principal component analysis
to construct different meteorological modes that differentiate between unique
synoptic-scale meteorological regimes, and we apply principal component regression (PCR) of
daily PM2.5 on these modes to not only interpret the observed
correlations of daily PM2.5 with individual meteorological variables,
but also to determine the dominant meteorological modes of daily PM2.5
variability, in four major city clusters of China: BTH, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the
Sichuan Basin (SCB; Fig. 1). Furthermore, using BTH as a case study, we
undertake spectral analysis of the time series of dominant meteorological modes
over the past decade to examine the interannual correlations between synoptic
frequencies and annual mean PM2.5. We finally construct a statistical
model using annual median synoptic frequency and annual mean local
meteorology to project 2000–2050 PM2.5 changes given present-day and
future climate simulations by an ensemble of climate models. This study
represents an advancement over that of Tai et al. (2012a, b) in terms of
methodology by considering the joint effects of synoptic frequency and local
meteorology, on par with Shen et al. (2017), which, however, focused only on
the US. Our work represents the first attempt to apply these methods to
Chinese air quality in an effort to derive a statistical projection of future
PM2.5 concentrations based on historical PM2.5–meteorology
relationships. These historical relationships can also be used to compare
results from process-based models (e.g., Jiang et al., 2013).
Data and methods
Daily assimilated meteorological fields for 1998–2017 over China are
obtained from National Centers for Environmental Prediction/National Center
for Atmospheric Research (NCEP/NCAR) Reanalysis 1 provided by the National
Oceanic and Atmospheric Administration (NOAA) of the USA (Kalney et al.,
1996). The dataset has a horizontal resolution of
2.5∘× 2.5∘. Following Tai et al. (2012a, b), eight
meteorological variables are considered here (Table 1), including surface air
temperature (X1), relative humidity (X2), precipitation rate
(X3), sea-level pressure (X4), pressure tendency (X5), wind
speed (X6), and two wind direction indicators (X7, X8). To
conduct correlation analysis and PC regression, meteorological data, except
from variables X5, X7 and X8, are deseasonalized and detrended
by subtracting the corresponding centered 31-day moving averages from the
original data to focus on day-to-day, synoptic-scale variability.
Specifically, for a meteorological variable Xk in any grid, the
deseasonalized meteorology X̃k is calculated as follows:
X̃k(t)=Xk(t)-131∑n=t-15t+15Xk(n).
The deseasonalized and detrended data are also normalized to their standard
deviations to yield zero means and unit variances:
X^kt=X̃kt-X̃k‾sX̃k,
where X^k(t) represents the normalized meteorological time series,
X̃k‾ and sX̃k are the mean
and standard deviation of the deseasonalized time series, respectively.
PM2.5 monitoring has been introduced in the national air quality
monitoring network in China since 2012 with the published third revision of
the National Ambient Air Quality Standards (NAAQS; Zhang and Cao, 2015).
Before that, observational spatial distribution of PM2.5 was mostly
estimated by satellite retrievals (Ma et al., 2015; van Donkelaar et al.,
2010; Xue et al., 2017; Zheng et al., 2016). One of the disadvantages of
PM2.5 monitoring at present is that there are very few sites with
detailed speciation data in China, although short-term studies of PM2.5
speciation have been conducted (Cao et al., 2012b; Huang et al., 2014; Yang
et al., 2005, 2011; J. K. Zhang et al., 2014). In this study, hourly mean
data of total PM2.5 from 1 June 2014 to 30 May 2017 are obtained from
the Chinese Ministry of Environmental Protection (MEP). Data are archived
from 1497 monitors across China (Fig. 1a), most of which are concentrated in
eastern, northeastern, and southern China, and are made available through a
repository website (http://pm25.in; last access: 2 July 2017). We
cross-check and correct the locations of the different monitoring sites,
removing unrealistic values and instrumental errors. PM2.5 data are then
deseasonalized and detrended in the same way as for the meteorological
variables.
To conduct the statistical analysis, MEP observations are interpolated using
inverse distance weighting onto the same 2.5∘× 2.5∘
resolution as that for the NCEP/NCAR data to produce daily mean PM2.5
fields for 2014–2017. Sampled values (zj) from sites within a search
distance (dmax) are weighted inversely by their distances
(di) from the cell centroid to produce an average (zj) for each
grid cell j:
zj=∑i=1nj(1/di)kzi∑i=1nj(1/di)k,
where nj is the number of sampled sites for grid cell j and k is the
power parameter. We choose k=2 and dmax=500 km as
recommended by Tai et al. (2010). Figure 1 shows the averaged site and
interpolated PM2.5 values for 2015 and 2016. As shown in Fig. 1, sites
in much of southwestern China (e.g., in the provinces of Tibet and Qinghai)
are relatively sparse, leading to likely unrepresentative interpolated values
in the corresponding grid cells. These regions are excluded from our
analysis.
For the purpose of examining long-term interannual PM2.5 variability, we
also make use of the annual mean concentrations of surface total PM2.5
for 1998–2015 derived from satellite measurements (van Donkelaar et al.,
2016). Total column aerosol optical depth (AOD) retrievals from multiple
satellite instruments and model simulations, such as the MODerate resolution
Imaging Spectroradiometer (MODIS), the Multiangle Imaging SpectroRadiometer
(MISR), and the GEOS-Chem chemical transport model, were weighted by the
ground-based AOD observations from the Aerosol Robotic Network (AERONET) sun
photometers. The daily AOD and near-surface PM2.5 were simulated by
GEOS-Chem to obtain the AOD-PM2.5 relationship, which were applied to
the satellite AOD retrievals to yield weighted PM2.5 concentrations.
Annual mean values of PM2.5 were computed and then calibrated to
ground-based PM2.5 observations using the global geographically weighted
regression (GWR) method (Brunsdon et al., 1996). Figure S1 in the Supplement
shows the spatial variation of the satellite-derived PM2.5 over China
from van Donkelaar et al. (2016), which has a spatial correlation of r=0.79 with MEP total PM2.5 for year 2015.
Correlation coefficients of daily PM2.5 with different
meteorological variables in Table 1, including (a) surface air
temperature (X1, K), (b) relative humidity (X2, %),
(c) precipitation (X3, mm d-1), (d) sea level
pressure (X4, hPa), (e) pressure tendency (X5,
hPa d-1), (f) wind speed (X6, m s-1), and
(g) wind direction (X7 and X8, unitless), for China from
June 2014 to May 2017. PM2.5 data are from MEP. Meteorological data are
deseasonalized by subtracting 31-day moving averages and normalized, and
daily total PM2.5 are also deseasonalized the same way to focus on
day-to-day variability. Only values with significant correlations at
p value ≤ 0.05 are shown. Panel (g) is plotted by finding the vector
sums of the correlation coefficients for X7 and X8, with positive
correlations pointing eastward and northward, respectively. The direction of
the vector sum indicates the prevalent wind direction when PM2.5 has a
positive anomaly.
To project the 2000–2050 effect of climate change on future PM2.5, we
use the meteorological variables in Table 1 archived from an ensemble of 15
climate models participating in the Coupled Model Intercomparison Project
Phase 5 (CMIP5) under the representative concentration pathway 8.5 (RCP8.5).
We regrid the data from different models into the same
2.5∘× 2.5∘ resolution. The details of the models
can be found in Table S1 in the Supplement.
Correlations between daily PM2.5 and meteorological variables
Here we first discuss the general correlation patterns between PM2.5 and
individual meteorological variables in China, and highlight what we can and
cannot conclude from them. The Pearson's correlation coefficients between
each meteorological variable in Table 1 and interpolated daily total
PM2.5 are computed for each grid cell from June 2014 to May 2017.
Figure 2 shows the correlation maps for the whole period. Temperature is
found to have an overall significant positive correlation with deseasonalized
PM2.5 in most regions of China (Fig. 2a), with the highest values
appearing in BTH and SCB (r=0.6). The correlation map of SLP (Fig. 2d),
which is often an indicator of the passages of synoptic systems, has a
similar spatial pattern to that with temperature but with an opposite sign
and smaller magnitudes, suggesting that PM2.5 tends to be low when SLP
is high. The anticorrelation pattern is relatively weaker in southern China.
Temperature and SLP are themselves found to be significantly negatively
correlated throughout most of China (Fig. S2), and thus it is difficult to
conclude whether they are the direct physical drivers of PM2.5
variability, or the correlations simply reflect common association with
larger meteorological regimes that control PM2.5 variability.
Correlation between RH and PM2.5 shows different patterns in northern
vs. southern China (Fig. 2b). A positive correlation (r=0.4) is seen in
BTH, likely reflecting higher PM water content in ambient air which can
enhance the uptake of semivolatile components (Dawson et al., 2007b),
consistent with previous findings (Wang et al., 2014). In southern China,
however, RH is negatively correlated with PM2.5, with larger
correlations in SCB and PRD (r=-0.4) than in YRD (r=-0.2). As can be
seen in Fig. 2c, negative correlation of precipitation with PM2.5 in
southern China is very similar to that of RH in Fig. 2b, likely reflecting
the association of high RH with precipitation (Fig. 2c) and onshore wind
(Fig. 2f), which can facilitate PM2.5 deposition or ventilation (Zhu et
al., 2012). Such a strong association between RH and precipitation may also
explain the apparently positive correlation between precipitation and
PM2.5 in northern China, where RH-promoted aerosol formation is likely
more important than wet deposition in the overall relationship.
Pressure tendency and wind speed exhibit similar correlation patterns
(Fig. 2e–f). Pressure tendency, another indicator of synoptic-scale motions,
is negatively correlated with PM2.5 in southern China, including PRD (r=-0.3) and in northeastern China, suggesting that PM2.5 tends
to be low when SLP is increasing. Wind speed is also negatively correlated
with PM2.5 in similar regions. These patterns are consistent with
advecting cold fronts with strong winds helping to ventilate PM2.5 in
heavily polluted regions (Tai et al., 2012a). Pressure tendency and wind
speed have a positive correlation with PM2.5 in northern China and some
parts of western China, which may be due to the co-varying strong winds and
frontal passages promoting the mobilization of mineral dust from the semiarid
regions and deserts there.
Figure 2g shows the correlation of wind direction with PM2.5, in which
arrow directions indicate wind directions associated with increasing
PM2.5. The corresponding mass divergence map together with its
calculation is shown in the Supplement (Fig. S3). For instance, PM2.5
increases with southeasterly wind for all of eastern and northeastern
China with a correlation of r=0.3 on average. This relationship suggests
that northwesterly wind tends to ventilate PM2.5 in most of China. Two
divergent wind patterns are seen, one in central China and one in Taklamakan
Desert, and their positions mirror regions with the highest PM2.5
concentrations in Fig. 1b. This result implies that wind transports
pollutants from source regions to the peripheries.
A generally consistent correlation among neighboring grid cells may be
associated with synoptic effects because the correlation pattern extends to a
synoptic regional length scale. The correlation maps for most of the
meteorological variables in Fig. 2 show such an effect. The commonality among
the correlation patterns of PM2.5 with different meteorological
variables, which among themselves have various degrees of correlation,
renders the interpretation of individual PM2.5–meteorology relationships
more difficult because the true driver of PM2.5 variability may be
masked by the collinearity among meteorological variables (as is pointed out
above for the case of temperature and SLP). Whenever a strong correlation
between PM2.5 and a given meteorological variable (e.g., temperature,
RH, precipitation, wind speed) is found, there can be three interpretations:
(1) this variable is truly the physical driver for PM2.5 variability;
(2) at least part of the correlation may arise from the correlation of this
variable with another local variable that is the true physical driver; and
(3) at least part of the correlation may reflect common association with a
larger, synoptic-scale phenomenon that drives PM2.5 variability. To
quantitatively differentiate between these possibilities and to ascertain the
roles of local meteorology vs. synoptic-scale circulation on PM2.5
variability, we conduct principal component analysis (PCA) on the eight
meteorological variables to capture their common co-variations in an ensemble
of independent meteorological modes. We follow Tai et al. (2012a), and
regress daily PM2.5 on the resulting principal component (PC) time
series to identify the dominant synoptic drivers of PM2.5 variability.
Their approach is particularly useful in that it enables the quantification
of the fraction of PM2.5 variability that can be explained by synoptic
meteorological regimes.
Dominant meteorological modes for daily PM2.5 variability based
on principal component regression
We perform PCA on the eight meteorological variables for 1998–2017 in
Table 1 to extract synoptic circulation patterns, focusing on the four major
metropolitan regions in China (BTH, YRD, PRD, and SCB). We use this longer
period of meteorological data for the PCA despite the relatively short time
history of PM2.5 data from MEP (2014–2017) because we aim to
characterize the climatologically important synoptic systems in China. The
longer period also overlaps with the annual mean PM2.5 data available
for quantifying interannual variability (see Sect. 5), so that a unified set
of meteorological modes can be used to explain both daily and interannual
PM2.5 variability. We conduct PCA for individual seasons and for the
whole period. All gridded daily meteorological data are spatially averaged
over the grid cells covering each of the four regions, deseasonalized, and
normalized to yield zero means and unit variances, as described above. The
resulting time series for each region are then decomposed to produce the PC
time series (Uj=U1, …, U8):
Ujt=∑k=18αkjX̃k(t)=∑k=18αkjX̃kt-X̃k‾sX̃k,
where X̃k represents the deseasonalized regionally averaged
meteorological fields in Table 1, X̃k‾ and
sX̃k are the temporal mean and standard deviation of
X̃k, X^k is the normalized value of
X̃k, and αkj is the elements of the
transformation matrix (i.e., eigenvector or empirical orthogonal function,
EOF) of PCA. The PC time series are ranked by their variances λ,
with the leading three to four PCs capturing most of the meteorological
variability (Wilks, 2011). For example, the first four PCs for the BTH region
explain 76 % of the total meteorological variability. The last few PCs
with variances λ < 1 are truncated using Kaiser's rule
since they likely represent noises (Wilks, 2011). Each PC represents a
distinct meteorological mode, the physical meaning of which is reflected by
the values of αkj in Eq. (2) and verified by cross-examination of
synoptic weather maps.
Annually dominant meteorological mode for observed PM2.5
variability in Beijing–Tianjin–Hebei (BTH). (a) Time series of
deseasonalized observed total PM2.5 concentrations and the principal
component (PC) time series in the sample month of December 2014.
(b) Composition of this mode as determined by the coefficients
αkj, with error bars showing 2 standard deviations of the
eigenvector coefficients. Meteorological variables are listed in Table 1.
(c) Synoptic weather map on 30 December 2014 with temperature (K) as
shaded colors, wind speed (m s-1) as vectors, and sea level pressure
(hPa) as contours. The rectangle indicates BTH. The weather map, which shows
an example of positive influence of the mode, is plotted using NCEP/NCAR
reanalysis I data.
For each region, we then extract the PCs for 2014–2017 only, and construct a
PCR model for deseasonalized, regionally averaged daily PM2.5
(ỹ, µg m-3) on the daily PC values (Uj) for
2014–2017, both for the whole period and for individual seasons:
ỹt=∑j=1NβjUj(t),
where βj is the regression coefficient (µg m-3), and
N the number of PCs retained after truncation (mostly 3 to 4).
We define a dominant meteorological mode seasonally or annually by computing
the ratio of the resulting regression sum of squares (SSRj) to total
sum of squares (SST) for each PC:
Rsynoptic,j2=SSRjSST=∑t[βjUj(t)]2∑t[y(t)]2.
This ratio characterizes the fraction of variance of daily PM2.5 that
can be explained by the jth PC in the PCR model. The PC with the largest
SSR/SST is deemed the dominant meteorological mode for that region. Any PC
which has an SSR/SST more than half of that of the dominant PC in a given
season is also recognized as an important PC for that region. The total
percentage of PM2.5 variability explained by the K dominant synoptic
modes in a region can be written as
Rsynoptic2=∑jKRsynoptic,j2.
The PCR model also allows us to separate between synoptically driven and
locally driven PM2.5 variability from the total meteorologically driven
PM2.5 variability. Regressing PM2.5 using all eight individual
meteorological variables yields a total R2 value, which entails both
synoptically and locally driven PM2.5 variability, as discussed in
Sect. 3. Using R2 and Rsynoptic2 from the PCR model, we
can infer the variability explained by local meteorology alone unrelated to
synoptic modes, using
Rlocal2=R2-Rsynoptic2,
where Rlocal2 indicates the overall locally driven PM2.5
variability.
Here we discuss the synoptic meteorological systems that dominate PM2.5
variability on annual timescales for each region. Discussion of regimes that
control PM2.5 on seasonal timescales, as well as information on the
values of SSR/SST and β, is included in the Supplement. We also note
that in our interpretation, we focus only on the physical effects of
meteorological phenomena. Non-physical drivers such as anthropogenic
emissions can be correlated with meteorology to some extent (e.g., cold
weather leading to higher emissions from heating); such effects, if any,
would be encapsulated in the statistical model, but are difficult to diagnose
explicitly due to a lack of corresponding data.
Same as Fig. 3 but for the Yangtze River Delta (YRD).
(a) Deseasonalized total PM2.5 concentrations and the PC time
series in the sample month of March 2015. (b) Composition of this
dominant mode as determined by the coefficients αkj.
(c–d) Synoptic weather charts on 25 and 18 March 2015, with
precipitation (mm d-1) shown as shaded colors, wind speed (m s-1)
as vectors, and sea level pressure (hPa) as contours. Panel (c) shows
the positive influence characterized by onshore wind with rainfall that
corresponds to decreasing PM2.5, while panel (d) shows the
negative influence with little wind on YRD. The rectangles indicate YRD.
Figure 3 shows the dominant meteorological mode in BTH, which explains nearly
36 % of PM2.5 variability throughout the year. Figure 3a shows a
strong anticorrelation between the time series of this mode and
deseasonalized observed total PM2.5 for the sample month of
December 2014. Figure 3b shows the meteorological composition of the EOF of
this annually dominant mode, with a positive phase consisting of low
temperature, high SLP, and strong northwesterly winds. The error bars
represent two standard errors of the meteorological composition, computed by
the formula shown in Sect. S1 in the Supplement. Similar loadings are seen
for winter, spring, and fall. We choose 30 December 2014 as a representative
day with PC changing from negative to positive phase to explain the physical
meaning of this PC. As seen in the weather map (Fig. 3c), the positive phase
of the PC represents a high-pressure system associated with the Siberian High
with dry cold fronts sweeping across BTH from northwest to southeast. The
Siberian High is the driver of the winter monsoon in East Asia, and such
northwesterly flow efficiently advects PM2.5 across BTH. Figure 3c shows
a strongly decreasing temperature gradient and increasing pressure tendency
originating from the Siberian High. PM2.5 concentration decreases by
nearly 240 µg m-3 over 29 to 31 December (Fig. 3a).
Regressing PM2.5 on all eight individual meteorological variables yields
an R2 value of 43 %, indicating that local meteorology only
contributes to an extra 7 % of the PM2.5 variability in addition to
that already explained by synoptic circulation. In addition to cold fronts
from the Siberian High, easterly onshore flow with high humidity and
southerly monsoon also controls daily PM2.5 variability in spring and
summer, explaining 18 and 17 % springtime and summertime variability of
PM2.5, respectively (see Sect. S2).
Same as Fig. 3 but for fall in the Pearl River Delta (PRD).
(a) Deseasonalized total PM2.5 concentrations and the PC time
series in the sample month of October 2014. (b) Composition of this
dominant mode as measured by the coefficients αkj.
(c) Synoptic weather map on 21 October 2014, corresponding to the
positive influence from the mode, with precipitation (mm d-1) as shaded
colors, wind speed (m s-1) as vectors, and sea level pressure (hPa) as
contours. The rectangle indicates PRD.
Figure 4 shows the dominant mode in YRD. This mode is important in spring,
fall, and winter, and contributes up to 14 % of the PM2.5
variability for the whole year. The two time series of the PC and PM2.5
demonstrate anticorrelation with each other in March 2015 (Fig. 4a). The
positive phase of this mode consists of low temperature, high RH and
rainfall, high and decreasing pressure, and strong easterly winds (Fig. 4b).
This set of meteorological phenomena is characteristic of onshore flow with
rainfall, as demonstrated by the weather map on 25 March 2015, which shows
cold and moist easterly winds originated from the high pressure centered over
the East China Sea.
Such winds sweep away pollutants and decrease PM2.5 concentration by
30 µg m-3 (Fig. 4c), and the associated rainfall also wash
out PM2.5. The negative phase of this mode, as represented on
18 March 2015, shows anticyclonic flow leading to accumulation of PM2.5
(Fig. 4d). Local meteorology is found to contribute to an additional 11 %
of the PM2.5 variability on top of that explained by synoptic effects.
In addition to onshore flow, PCA for summer alone indicates that summertime
low-pressure systems also deplete PM2.5, likely due to the associated
precipitation, explaining 24 % of summertime PM2.5 variability. This
PC is also sometimes characterized by northward-propagating tropical
cyclones, with strong wind and rainfall (see Sect. S3).
Same as Fig. 3 but for winter in the Sichuan Basin (SCB).
(a) Deseasonalized total PM2.5 concentrations and the PC time
series in the sample month of January 2015. (b) Composition of this
dominant mode as measured by the coefficients αkj.
(c–d) Synoptic weather maps on 29 and 24 January 2015. Panel
(c) shows the positive influence characterized by a cold front from
the Siberian High that advects PM2.5 away, while panel (d)
shows the negative influence characterized by stagnation over SCB.
Temperature (K) is shown as shaded colors, wind speed (m s-1) as
vectors, and sea level pressure (hPa) as contours. The rectangles indicate
SCB.
Figure 5 shows the dominant mode for explaining PM2.5 variability in
PRD. This mode is dominant in spring, fall, and winter, and in total
contributes 22 % of variability in PM2.5 throughout the year.
Figure 5a reveals a negative correlation between the PC for this mode and
PM2.5 in October 2014. The positive phase of this mode consists of high
RH, precipitation, increasing pressure, and strong northerly winds (Fig. 5b).
This set of meteorological phenomena represents a cold frontal rainstorm, as
demonstrated by the weather map in Fig. 5c, which shows a frontal rain belt
coinciding with the positive phase of the PC on 21 October 2014. Pressure
contours were advected southward by northerly winds, and a regional rain belt
brought maximum rainfall of up to 15 mm d-1 to southern China. In
general for this mode, advancing cold air sweeps from north to south and
lifts the warmer and moister air, leading to precipitation and sometimes
thunderstorms. Annually, regressing PM2.5 on individual meteorological
variables yields an R2 value of 33 %; thus local meteorology
contributes to an extra 11 % of PM2.5 variability unexplained by
synoptic circulation. In addition to cold frontal rainstorms, summertime PCA
also shows that the air quality in summer PRD is also influenced by rainfall
from low-pressure troughs as well as by landfalls of tropical cyclones (see
Figs. S11, S12). These two modes explain 18 and 15 % of summertime
PM2.5 variability, respectively. The troughs cause rainfall that
scavenges pollutants; tropical cyclones making landfall to the east of PRD
cause inversion layers that trap pollutants and degrade air quality (see
Sect. S4).
Figure 6 shows the dominant mode in SCB in winter, which has a negative
correlation with PM2.5, as shown for the sample month of January 2015
(Fig. 6a). This mode dominates PM2.5 variability year-round,
explaining 25 % of its day-to-day variability. PCA shows that its
positive phase is characterized by low temperature, high SLP, and weak
northwesterly winds (Fig. 6b), which resembles the dominant EOF in BTH. This
mode is characterized by a northwesterly flow also associated with the
Siberian High. On 29 January 2015, the Siberian High was situated southeast
of Lake Baikal (Fig. 6c), advecting a clean, northwesterly cold front toward
SCB and ventilating PM2.5 by 150 µg m-3 over 25 to
29 January. On 24 January, this mode was in its negative phase and SCB was
under a relatively mild weather (Fig. 6d), while PM2.5 was at a local
maximum (Fig. 6a). Annually, local meteorology contributes to another
20 % of the total PM2.5 variability. In addition to cold frontal
passages, rainfall also drives PM2.5 variability especially in winter
and spring, explaining 18 and 16 % of wintertime and springtime
PM2.5 variability, respectively. This mode represents a cold frontal
rain system that promotes wet deposition of pollutants (see Sect. S5).
Synoptic frequency and local meteorology as metrics for climate change
impact on PM2.5
Future climate change can significantly affect synoptic-scale circulation
patterns and local meteorology, modifying the transport and deposition of
PM2.5 (Fiore et al., 2015; Jiang et al., 2013; Mickley et al., 2004).
Based on the demonstrated strong relationships of synoptic circulation and
local meteorology on daily PM2.5, we build a regression model to infer
how interannual variations of local and synoptic meteorology affect
interannual PM2.5 variability, which we then apply to future climate
projections. This approach allows us to evaluate the potential impacts of
climate change on PM2.5 air quality. Here we adopt the PCA spectral
analysis approach, namely, to apply a fast Fourier transform (FFT) to the
daily time series of the dominant PCs for all seasons to extract the median
frequencies from the resulting spectra. We use the same PCs generated using
the 1998–2017 NCEP/NCAR meteorological data (Sect. 4), and smooth the resulting
FFT spectra with a second-order autoregressive filter (Wilks, 2011). We focus
on BTH as a case study. For example, spectral analysis shows that the
Siberian High fluctuates between 58 and 67 times per year on average, and has
a climatological frequency of 63 times per year averaged over 1998–2015.
Detrended annual mean total PM2.5 concentration and climate
variables chosen by the forward selection model of 1998–2015, including
(a) annual mean frequency of springtime Siberian High (r=-0.51)
and (b) relative humidity (r=0.49). Annual mean surface
PM2.5 concentrations are derived from satellite AOD by
van Donkelaar et al. (2016). All variables are detrended by subtracting the
7-year moving averages from the annual mean values.
Regression model that explains interannual variability of
satellite-derived PM2.5 in Beijing–Tianjin–Hebei (BTH).
Frequency of springtimeRelativeSiberian HighhumidityPM2.5 sensitivity-0.31 µg m-3 yr1.00 µg m-3 %-1Standard error±0.16µg m-3 yr±0.57µg m-3 %-1p value for each predictor0.07760.0977Adjusted R2 value0.309 F statistic4.81 Total p value0.0244
Satellite-retrieved PM2.5 has large uncertainties in seasonal mean
values, and thus we make use of only the annual mean PM2.5 values for
building our regression model. We construct a multiple linear regression
(MLR) model for the 1998–2015 satellite-retrieved annual mean PM2.5
over BTH by spatially averaging the grid boxes covering the region. In
selecting predictor variables, we consider the annual mean local
meteorological variables in Table 1 (except SLP tendency, X5, and the
two wind direction indicators, X7 and X8, whose averages are often
nearly zero) and the annual median frequencies of synoptic
circulation patterns from all individual seasons diagnosed from spectral
analysis. The predictand (annual mean PM2.5) and potential predictors
are detrended by subtracting from them the respective 7-year moving averages
in order to remove long-term trends driven by emission changes. We adopt a
forward selection approach (Wilks, 2011) to identify which climatic variables
explain the greatest amount of interannual PM2.5 variability, starting
from the one explaining the largest percentage of PM2.5 variability
(having the largest adjusted R2 value), and adding predictor variables
until the enhancement in adjusted R2 given by an additional predictor is
less than 0.05. Variables that lead to a large variance inflation factor
(> 2) are also excluded to avoid the issue of multicollinearity,
which often leads to higher imprecision of regression estimates. Typically
the forward selection algorithm does not yield more than three predictor
variables for interannual PM2.5 variability.
Projected changes in PM2.5 from 2000 to 2050, as calculated from
meteorological outputs from the CMIP5 model ensemble. (a) Future
projections of mean relative humidity (RH, %) and median synoptic
frequency of springtime Siberian High (yr-1) as computed by 15 CMIP5
models. (b) Statistical distributions of CMIP5-projected RH and
synoptic frequency as computed by model weighting algorithm of Tebaldi et
al. (2005). (c) Changes in PM2.5 (µg m-3) from
2000 to 2050 based on climate projections from 15 models and statistical
sensitivities from our multiple linear regression model. (d)
Statistical distributions of projected PM2.5 based on Monte Carlo
sampling of all possible uncertainty spaces. Dashed lines indicates the
simple ensemble mean of the changes, red dots indicate positive changes, and
blue dots indicate negative changes. The label “RH” indicates changes
associated with relative humidity, “freq” indicates changes associated with
frequency of cold fronts from the Siberian High, and “total” denotes the
sum of the two.
Table 2 shows the interannual PM2.5 variability explained by the
predictors, the corresponding regression coefficients and the p values for
the BTH region. The two predictors selected by the forward selection
algorithm are the frequency of the first PC in spring (i.e., the springtime
Siberian High, Fig. S5) and annual mean RH. Figure 7 shows the correlation of
detrended annual mean PM2.5 with detrended annual mean RH and the
frequency of fluctuation of the springtime Siberian High. The negative
correlation (r=-0.51) between springtime PC frequency and annual
PM2.5 indicates that more frequent occurrences of cold advection from
the high-pressure systems further north, especially during spring, help
ventilate PM2.5 in BTH and influence annual mean PM2.5 here. This
is consistent with the relationship we found between PM2.5 and Siberian High on the daily timescale (Sect. 4). Annual mean RH has a positive
correlation with PM2.5 (r=0.49), which is consistent with Sect. 3
where we found higher RH coinciding with higher PM2.5 on the daily
timescale. Adding RH helps explain an additional 9 % of interannual
PM2.5 variability, and the two predictors in total give an adjusted
R2 value of 31 %, which represents a reasonably high value for a
linear model, given that nonlinear PM2.5–meteorology interactions and
emission-driven PM2.5 variability are not included in the model.
Although temperature has a strong daily correlation of r=0.6 with
PM2.5 in the correlation analysis in Sect. 3, annual mean temperature
does not appear to correlate significantly with annual mean PM2.5 (r=0.18) and was not selected by the forward selection algorithm.
Annual mean temperature also has a weak correlation with springtime Siberian
High fluctuation frequency (r=-0.25), which indicates that more frequent
synoptic fluctuations have only little bearing on annual mean temperature,
and that the strong daily PM2.5–temperature co-variation is mostly a
manifestation of synoptic influence. All other annual mean local
meteorological variables have insignificant correlations with annual mean
PM2.5.
Our findings show that meteorological effects on daily PM2.5 at least in
part contribute to interannual variability PM2.5, a finding which we can
utilize to estimate future changes in PM2.5. To this end, we extract the
meteorological variables in Table 1 from the results of 15 models in the
Climate Model Intercomparison Project Phase 5 (CMIP5) for 1996–2005 and
2046–2055 under the RCP8.5 scenario (Table S1). This scenario represents a
business-as-usual future. We diagnose the 2000–2050 changes in the decadal
averages of these variables and the median frequencies of the constructed PCs
(Fig. 8a). To obtain an ensemble mean and distribution of the meteorological
changes (Fig. 8b), we apply the weighting algorithm of Tebaldi et al. (2005)
to the CMIP5 model outputs, which can discount any poorly performing models
yielding meteorology that diverges from the present-day observations (using
NCEP/NCAR reanalysis data in this study) or that diverges too much from the
weighted ensemble mean, by giving those models a lower weight in the
calculation of the ensemble mean and distribution.
We combine the meteorological changes with the PM2.5-to-climate
sensitivities (i.e., regression coefficients in Table 2) to obtain an
estimate for the 2000–2050 change in annual mean PM2.5 due to climate
change alone (Fig. 8c), according to the following formula:
ΔPM2.5=∑iN∂PM2.5∂xiΔxi,
where ΔPM2.5 is the total PM2.5 change due to climate
change, N is the total number of predictors selected by the forward
selection algorithm, and Δxi is the change of the ith predictor
selected by the algorithm. Here we make the “stationarity” assumption that
the PM2.5-to-climate sensitivities, ∂PM2.5/∂xi, remain unchanged in the near future, such that ΔPM2.5 is
totally due to changes in future meteorology. We then use a Monte Carlo
approach to characterize the probability distribution and statistical
significance of the changes in PM2.5 concentration arising from the
uncertainties of the regression coefficients in the MLR model and
from the differences in model physics among CMIP5 models. Our approach
involves repeated (> 5000 times) sampling of regression
coefficients of the MLR model from their distributions as parameterized by
the means and standard errors in Table 2, along with the sampling of the
performance-weighted ensemble distributions of meteorological changes from
the Tebaldi et al. (2005) algorithm. The sampling distributions are
aggregated in accordance with Eq. (9) to obtain the final distributions of
PM2.5 changes for each predictor and the sum of the two (Fig. 8d).
Figure 8 shows the future changes of PM2.5 concentrations with the
corresponding changes in future meteorology. Changes in RH among CMIP5 models
show high inconsistency, with values ranging from -2.01 to +3.19 %
(Fig. 8a). The ensemble mean of CMIP5 models shows a statistically
insignificant increase (p value = 0.32) of RH of 0.23 ± 1.24
percentage points by 2050s in BTH (Fig. 8b), consistent with a future
prediction of a change within < 1 % over BTH in the Fifth
Assessment Report of Intergovernmental Panel on Climate Change (IPCC AR5; Fig. 12.21 in Collins et al., 2013). Past modeling studies show that RH
remains nearly constant on climatological timescales and continental spatial
scales (Randall et al., 2007), while recent investigation shows that
near-surface RH decreases over most land areas globally (O'Gorman and Muller,
2010). IPCC AR5 (2013) shows that the regional mean RH in BTH changes by less
than 1 standard deviation of interannual variability by year 2065, and the
variability is dominated more by naturally occurring processes than by human
activities.
We find that 10 of the 15 models project an increase in this synoptic
frequency (Fig. 8a). Based on the weighting algorithm for discounting poorly
performing models, we project an overall very likely (i.e.,
90–100 % likelihood according to IPCC guideline in Stocker et al.,
2013) statistically significant increase (p value = 0.0008) in the
frequency of synoptic-scale fluctuation of the Siberian High by
1.46 ± 0.39 yr-1 by the 2050s (Fig. 8b). The generally increasing
frequency is possibly driven by the future reduction in meridional
temperature gradient, which decreases the intensity of the midlatitude jets
and favors the amplification and persistence of surface anticyclones (e.g.,
Francis and Vavrus, 2012; Zhang et al., 2012). Francis and Vavrus (2012)
showed that the upper tropospheric midlatitude jet (in the form of Rossby
wave) exhibited reduced zonal velocity and augmented wave amplitude under
warming over 1979–2010, which may have led to an increase in atmospheric
blocking events (Barriopedro et al., 2006) and an enhancement in the
likelihood of cold surges from the Siberian High. In another multi-model
study, Park et al. (2011), however, found no significant correlation between
cold surge occurrences and surface air temperature over East Asia, and
thereby concluded that cold surge occurrences would remain constant in
frequency under a warming climate. Our results based on PCA spectral analysis
show a modest increase instead of unchanging frequency in synoptic-scale
fluctuation of the Siberian High in the future.
Figure 8c and d show the corresponding future PM2.5 changes from the
baseline value of 57.2 µg m-3 in the 2000s. Across the model
results, we find an overall PM2.5 change of 0.21 to
+1.79 µg m-3 due to changing RH, and of -0.29 to
0.63 µg m-3 due to changing synoptic frequency (Fig. 8c).
From the Monte Carlo sampling of the performance-weighted distribution of
meteorological changes and uncertainties of statistical parameters, the
RH-induced PM2.5 change is 0.21 ± 1.44 µg m-3
(p value = 0.58), and the frequency-induced PM2.5 change is
-0.46 ± 0.28 µg m-3 (p value = 0.028, 97 %
likelihood; Fig. 8d). While the RH-induced PM2.5 change is
statistically insignificant and its sign inconclusive, we show that the
higher frequency of fluctuation in the Siberian High alone, through enhancing
cold frontal frequency, could lead to a very likely reduction in
annual mean PM2.5 and thus constitute a slight climate “benefit” for
PM2.5 air quality over BTH of China. We find that the greatest
uncertainty stems from large inter-model differences in the future projections
of RH and, which are much larger than those in the synoptic frequency
projections. The regression coefficients have relatively moderate standard
errors (Table 2) and contribute only little to the overall projection
uncertainty.
Conclusions and discussion
In this study we use a combination of multivariate statistical methods to
investigate the local and synoptic meteorological effects on daily and
interannual variability of PM2.5 in China. Based on the resulting
statistical relationships between PM2.5 with annual mean meteorological
variables and synoptic frequencies, we also project future PM2.5
changes in the Beijing–Tianjin–Hebei (BTH) region. First, we find strong
correlations between daily observed PM2.5 and individual meteorological
variables in China over 2014–2017, and the spatial patterns of correlations
suggest common association of these variables with synoptic circulation and
transport. We therefore apply PCA on spatially averaged meteorological
variables for four major metropolitan regions (BTH, YRD, PRD, SCB) for
1998–2017 (for all seasons and for the whole period) to diagnose the
dominant synoptic meteorological modes, and the time series of these modes
are used as predictor variables in an MLR model to explain day-to-day
PM2.5 variability for each region. We find that, in BTH, the presence
of the Siberian High strongly controls PM2.5 levels. Northerly
monsoonal flows and advecting cold fronts from the Siberian High play key
roles in ventilating PM2.5 in BTH for all seasons except JJA. In YRD,
onshore wind with precipitation from the East China Sea is the dominant
meteorological mode, effectively scavenging PM2.5 for all seasons
except JJA. In PRD, frontal rain is a key driver reducing PM2.5 by wet
deposition for all seasons except JJA. In SCB, the Siberian High plays a key
role in bringing clean air from the north that effectively dilutes pollution
for all seasons. Different synoptic meteorological regimes in different
seasons explain about 16–37 % of PM2.5 variability in 2014–2017.
We further show that the long-term fluctuations in the frequencies of the
dominant synoptic modes also shape interannual variability of PM2.5.
Using the BTH region as a case study, we use regionally averaged annual mean
local meteorological variables and annual median frequencies of the dominant
synoptic modes of all individual seasons as potential predictors in a
forward-selection MLR model to explain the interannual variability of
satellite-derived annual mean PM2.5 over 1998–2015. The forward
selection model finds two significant predictors, namely, the frequency of
springtime frontal passages (which indicates the interannual fluctuation in
the strength of the Siberian High) and annual mean RH, with observed
PM2.5-to-climate sensitivities of
-0.31 ± 0.16 µg m-3 yr and
1.00 ± 0.57 µg m-3 %-1, which together explain
31 % of the variability of annual mean PM2.5. The signs of
correlations between PM2.5 and the two predictors are also consistent
with that from the daily PC regression analysis, showing a broad consistency
in PM2.5–meteorology relationships across different timescales.
We further address the effect of 1996–2055 climate change on future
PM2.5 air quality, using an ensemble of 15 CMIP5 climate model outputs
under the RCP8.5 scenario. Ten out of 15 models show an increase in the
frequency of strength fluctuation of the Siberian High with an ensemble mean
of 1.46 yr-1. Nine out of 15 models show a statistically insignificant
change in future RH. Inter-model differences in the projected changes in RH
are much larger than that in synoptic frequency of fluctuation in the
Siberian High, owing to the high inconsistency in future projections of
atmospheric humidity, especially on a regional scale (IPCC, 2013). We combine
the ensemble projection of RH and synoptic frequency with the
PM2.5-to-climate sensitivity from our statistical model to project
future PM2.5 changes, with uncertainties quantified using a Monte Carlo
approach. While the RH-induced PM2.5 change is insignificant and
inconclusive, we project for the 2050s a statistically significant and
very likely (∼ 97 % likelihood) decrease in PM2.5 of
-0.46 ± 0.28 µg m-3 due to increasing frequency in
the fluctuation of the Siberian High. The overall projection is inconclusive
mostly due to the highly uncertain RH projections. Our prediction is
comparable in magnitude with other studies (e.g., Jiang et al., 2013) and predictions for the USA (Shen et al., 2017; Tai et al.,
2012b; Pye et al., 2009; Avise et al., 2009) and Europe (Juda-Rezler et al.,
2012), but much smaller in magnitude compared with the baseline value of
57.2 µg m-3 in the 2000s, suggesting that the “climate
benefit” from higher synoptic frequency is rather small especially in
comparison with what emission control efforts could do to curb PM2.5
concentrations in China. Jiang et al. (2013) projected changes of PM2.5
over China due to climate change alone under IPCC A1B scenario, and the
resulting change over BTH is about +1 µg m-3 averaged
annually. They attributed their predictions to (1) changing precipitation
that leads to a change in wet deposition and (2) increasing temperature that
results in more volatilization of nitrate and ammonium, which differs from
our conclusion that cold frontal ventilation dominates the
PM2.5–temperature correlation and total PM2.5 response. Our
statistical results (for BTH only) do not show significant relationships
between temperature and PM2.5 (r=0.18) nor between rainfall and
PM2.5 (r=0.20) on an interannual timescale, despite strong
correlations on a daily timescale. This discrepancy between empirical results
and process-based model results may stem from the inadequacy of
satellite-derived PM2.5 in capturing the variability caused by
volatilization effect, an inadequate process-based model representation of
the PM2.5–temperature relationship (Shen et al., 2017), and from the
uncertainty in emissions of PM precursors in the process-based model.
There are two major limitations of the statistical approach developed in this
study. First, due to accuracy constraints of the satellite-derived PM2.5
concentrations, we could only use annual mean PM2.5 instead of seasonal
mean PM2.5 as the basis for interannual regression and future
projections. Shen et al. (2017) showed that PM2.5 responds to
meteorological conditions differently in different seasons in the US. Due to
the short period of surface monitoring data (see Sect. 2), we rely on the
annual mean satellite-derived PM2.5 with no seasonality in this study,
and thus no seasonal predictions of PM2.5 are possible. Another
limitation is that the statistical projections rely on the stationarity
assumption that the PM2.5-to-climate sensitivities will be more or less
constant in the future (see Eq. 7). This assumption may be acceptable for
near-future projections (Fiore et al., 2012; IPCC, 2013), but it is less reliable for multidecadal
projections especially as significant changes in emission levels may alter
the chemical nature of total PM2.5 and thus the interactions with
meteorology. While process-based modeling studies of the future evolution of
PM2.5–meteorology relationships under varying levels of emissions in
China are highly warranted, the empirical relationships as diagnosed from
investigation of historical data in this study are valuable in providing a
basis for testing and validating the process-based model sensitivities of
PM2.5 air quality to climate change.
Data used in this study, including
site-interpolated daily mean PM2.5, NCEP/NCAR Reanlaysis I
meteorological data, and satellite-derived annual mean PM2.5, are
deposited in the publicly available institutional repository, accessible via
this link:
http://www.cuhk.edu.hk/sci/essc/tgabi/data.html (Leung, 2018). Request for
raw data or the complete set of data, or any questions regarding the data,
can be directed to the principal investigator, Amos P. K. Tai
(amostai@cuhk.edu.hk).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-6733-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by a faculty start-up allowance from the Croucher
Foundation and The Chinese University of Hong Kong (CUHK; project
ID: 6903601, 4930041) given to the principal investigator, Amos P. K. Tai, as
well as a Vice-Chancellor Discretionary Fund (project ID: 4930744) from CUHK
given to the Institute of Environment, Energy and Sustainability.
Edited by: Qiang Zhang
Reviewed by: two anonymous referees
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