As an important issue in atmospheric environment, the
contributions of anthropogenic emissions and meteorological conditions to
air pollution have been little assessed over receptor regions in regional
transport of air pollutants. In the present study of 5-year observations and
modeling, we targeted the Twain-Hu Basin (THB), a large region of heavy PM2.5 pollution in central China, to assess the
effects of meteorology on PM2.5 change over a receptor region in the regional transport of
air pollutants. Based on observations of environment and meteorology over
2015–2019, the Kolmogorov–Zurbenko (KZ) filter was performed to decompose
the PM2.5 variations into multiple timescale components over the THB,
where the short-term, seasonal and long-term components accounted for,
respectively, 47.5 %, 41.4 % and 3.7 % of daily PM2.5 changes.
The short-term and seasonal components dominated the day-to-day PM2.5
variations with long-term component determining the change trend of PM2.5 concentrations over recent years. As the emission- and
meteorology-related long-term PM2.5 components over the THB were
identified, the meteorological contribution to the declining PM2.5 trend
presented a distinct spatial pattern over the THB with northern positive
rates up to 61.92 % and southern negative rates down to -24.93 %. The
opposite effects of meteorology on PM2.5 pollution could accelerate and
offset the effects of emission reductions in the northern and southern THB,
which is attributed to the upwind diffusion and downward accumulation of air
pollutants over receptor regions in regional PM2.5 transport. It is
noteworthy that the increasing conversion efficiencies of SO2 and
NO2 to sulfate and nitrate for secondary PM2.5 could have offset the
effect of PM2.5 emission reduction on air pollution in the THB during
recent years, revealing the enhancing contribution of gaseous precursor
emissions to PM2.5 concentrations under control of anthropogenic
emissions of PM2.5 and the gaseous precursors over
receptor regions in the regional transport of air pollutants. Our results
highlight the effects of emission mitigation and meteorological changes on
the source–receptor relationship of the regional transport of air pollutants with the
implication of long-range transport of air pollutants for regional and
global environment changes.
Introduction
Haze pollution with high levels of PM2.5 (fine particulate matters with
aerodynamic diameters equal to or less than 2.5 µm) has been a
serious problem in the atmospheric environment (Peng et al., 2016; Wang et
al., 2016) with adverse influences on air quality and human health (Cao
et al., 2012; Crouse et al., 2012). In recent years, large areas in
central and eastern China (CEC) have experienced haze pollution with
unprecedentedly high PM2.5 levels in the regions covering the North China
Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan
Basin (SB) (Zhang et al., 2012; C. Q. Lin et al., 2018; Guo et al., 2017). In
order to improve air quality by reducing air pollutant emissions, the Chinese
government has implemented an action plan for controlling anthropogenic
emissions since September 2013 (http://www.gov.cn/xinwen/2018-02/01/content_5262720.htm,
last access: 17 January 2022). Surface PM2.5 concentrations have exhibited
30 %–40 % decreases in CEC over recent years (Xue et al., 2019;
Zhang et al., 2019). However, the changes of air pollution are generally
co-determined by air pollutant emissions and meteorological conditions. The
contributions of changes in meteorology and anthropogenic emissions to the
improvement of air quality need to be comprehensively investigated.
PM2.5 includes primary particles emitted directly from various sources
and secondary particles generated by homogeneous and heterogeneous chemical
reactions of gaseous precursor in the atmosphere, depending on the emissions
of the primary PM2.5 and its gaseous precursors (Y. Lin et
al., 2018; Du et al., 2020). In addition to the emissions of air
pollutants, meteorological conditions can alter the local accumulation,
regional transport, chemical conversion and wet and dry depositions of air
pollutants (Lu et al., 2017; Li et al., 2018). Severe haze pollution
always occurs in the wintertime under stagnant meteorological conditions
with weak near-surface winds, strong temperature inversion and high relative
humidity in the atmospheric boundary layer, which are favorable for the
accumulation of air pollutants to form air pollution (Li et al., 2018;
Miao et al., 2015; Tang et al., 2016). Meteorological conditions are closely
governed by synoptic circulation by modulating the atmospheric physical and
chemical processes, including regional transport of air pollutants (Miao
et al., 2017; Ning et al., 2019). By regulating synoptic circulation
patterns, the climate changes of East Asian monsoons greatly influence the
seasonal and interannual variations of aerosol concentrations for air
pollution over China (Zhu et al., 2012; Jeong and Park, 2017).
Assessments of contributions of anthropogenic emissions and meteorological
changes to air quality improvement are an important issue in environmental
changes (Pearce et al., 2011; Zhang et al., 2018; Chen et al., 2019). Chemical transport models have been widely used to quantify meteorology's effect on PM2.5 variations by using a linear additive
relationship between sensitivity and base simulations (Mueller and
Mallard, 2011; X. Li et al., 2015; Zhang et al., 2020). The contribution of
meteorological changes to PM2.5 decreases was estimated at the averages
of 10 %–20 % with interannual fluctuations of about 5 % in CEC
from 2015 to 2019 through a model-based environmental meteorology index (Gong et al., 2021). The accuracy of modeling assessments can be
influenced by the uncertainties in emission inventories and the incomplete
chemical and physical mechanisms in air pollution simulation (Li et al.,
2011). Based on the statistical analysis of the long-term observational data, it was
quantified that the emission controls could explain more of the variances in PM2.5 than meteorology (Gui et al., 2019), and 12 % of the
observed PM2.5 decrease was attributed to meteorological drivers in
China since 2013 (Zhai et al., 2019). However, modeling and
observational studies have mostly assessed the contribution of emissions and
meteorology to regional PM2.5 variations in the emission source regions
with high anthropogenic emissions of air pollutants, and there have been few
assessments of multi-scale changes of atmospheric environment over
receptor regions in the regional transport of air pollutants.
The Twain-Hu Basin (THB), featuring the lowlands (mainly less than 200 m a.s.l.) of two central Chinese provinces, Hubei and Hunan (Fig. 1), is
surrounded by the high air pollution regions of NCP, YRD, PRD and SB. As such,
it is a key receptor region in the regional transport of air pollutants from the
upstream region driven by East Asian monsoonal winds over CEC (Shen et
al., 2020). Heavy air pollution in the THB with a unique “non-stagnation”
atmospheric boundary layer is aggravated by the regional PM2.5 transport
over CEC (Zhong et al., 2019; Yu et al., 2020). In combination with the
heavy pollution region of NCP through distinct transport channels, the
regional transport from northern China to central China contributed 70.5 % PM2.5 concentrations to a wintertime heavy pollution episode in
the THB (Hu et al., 2021). Thus, the contributions of air pollutant
emissions and meteorological conditions to air quality change over this
region in central China need to be specifically assessed with the long-term
observations over recent years.
Topographical height (color contours, m a.s.l.) over
the THB (outlined with black dashed line) with the locations of 14 sites (red dots) and the surrounding regions in central China.
In this study, we investigated the multi-scale changes of PM2.5
concentrations over the THB, a key receptor region of regional PM2.5
transport in China, from 2015 to 2019 by establishing a statistic model
with a Kolmogorov–Zurbenko (KZ) filter. We then evaluated the contributions
of anthropogenic emissions and meteorological changes to the declining
trends in PM2.5 concentrations over this receptor region in regional PM2.5 transport over CEC during the past 5 years of emission controls. The
analysis of THB's multi-scale air quality changes can improve the
understanding of the effects of emission mitigation and meteorological
changes on environmental change with regional transport of air pollutants.
Data and methodsData
In order to analyze air quality changes in the THB, the observational data
of hourly NO2, SO2 and PM2.5 concentrations from 2015 to 2019
were collected from the national air quality monitoring network (http://www.mee.gov.cn/, last access: 17 January 2022). The air quality
observation data are under quality control, based on China's national
standard of air quality observation.
The data of meteorological observations in the THB were sourced from the
weather monitoring network of China Meteorological Administration
(http://data.cma.cn/, last access: 17 January 2022), including air
temperature (T), relative humidity (RH), sea level pressure (SLP), wind
speed (WS) and precipitation (Pre), with temporal resolutions of 3 h.
KZ filter
To better understand the multiple timescale variations of PM2.5 and the
relations to air pollutant emissions and meteorological drivers, a KZ filter (Rao and Zurbenko, 1994; Seo et al., 2018) is used to separate the daily
data into multi-scale components, based on an iterative moving average that
removes high-frequency variations in the data with applications in the study
of air pollutants, especially O3 and PM2.5 variations (Chen et
al., 2019; Ma et al., 2016; Seo et al., 2014; Zheng et al., 2020).
The KZ filter KZm,p with a length of moving average window m and
number of iterations p can remove the high-frequency component of periods
smaller than the effective filter width N (≥m×p1/2). The KZ filter is applicable to the time series with missing data owing to the
iterative moving average process, which provides high accuracy when compared with the wavelet transform method (Eskridge et al., 1997). By
comparing different sets of moving average m and number of iterations p, it
was found that the decomposed time series using the KZ15,5 filter
exhibited no white noise (short-term component), and the trend of the long-term
component derived with the KZ365,3 filter corresponded approximately to
the interannual trend of the original data (Rao and Zurbenko, 1994;
Eskridge et al., 1997). Based on the spectral decompositions of the daily
observational data and three components, the power spectra of daily
observational data in periods of less than 33 d and longer than 632 d (1.7 years) have been well reproduced by short-term and long-term
components, and the seasonal component well represents the seasonal variations,
i.e., periods between 33 d and 1.7 years (Seo et al., 2018). Thus
we applied KZ15,5 and KZ365,3 filters to remove the variations
with periods shorter than 33 d and 1.7 years in this study.
A meteorological or environmental variable Xt observed in
time series t can be decomposed into the short-term component XSTt and the baseline component XBLt presenting as
Xt=XSTt+XBLt.
The baseline component XBL(t) is obtained by applying the
KZ(15,5) filter to Xt, removing the short-term
component XSTt with the temporal period shorter
than 33 d from the observed data, expressed as
XBLt=KZ15,5Xt.
The baseline component XBL(t) also can be separated into the daily
climatic averages XBLclm over the study period, occupying most of the
seasonality in XBL(t) and the residual ε(t):
εt=XBLt-XBLclm.
To obtain the long-term component XLT(t) by removing the variations
with the temporal period shorter than 1.7 years, the KZ365,3
filter is applied to ε(t) expressed as follows:
XLTt=KZ365,3εt,
with the short-term component
XSTt=Xt-XBLt
and the seasonal component
XSNt=XBLt-XLTt.
The KZ filter was used to separate the daily surface PM2.5, NO2
and SO2 concentrations into short-term, seasonal and long-term
components in this study. The short-term component presents a synoptic-scale
variation of meteorological influences, which could control local
accumulation and regional transport of air pollutants (Seo et al.,
2017), partly associated with short-term fluctuations in air pollutant
emissions (Russell et al., 2010). The seasonal and long-term components
are attributable to the variations in air pollutant emissions related to
human activities as well as the seasonal and interannual changes in
meteorological conditions (Kim et al., 2018).
Multiple linear regression of air pollutant changes with
meteorological variables
By altering the local accumulation, regional transport, chemical conversion and
wet and dry depositions of air pollutants, the meteorological factors such
as wind, RH, T, air pressure and Pre could exert significant impacts on PM2.5 changes (Sun et al., 2013; Li et al., 2018; Z. Y. Chen et al.,
2020). Therefore, with the multiple factors of the baseline components of
10 m WS, 2 m RH, 2 m T, SLP and Pre calculated by Eq. (2), a multiple linear
regression equation was stepwise established for the baseline component of PM2.5 as follows:
PM2.5BLMLRt=a0+∑iaiMETBLit,
where METBLit(iϵ1,5) is the baseline component of the
meteorological variable i with i=1,2,3,4,5, respectively, for WSBLt, RHBLt, TBLt, SLPBLt, PreBL(t). We fit the regression
coefficient ai for each meteorological variable and the intercept
a0. The residual εPM2.5 between PM2.5BL and
PM2.5BLMLR regressed with the multiple linear Eq. (7) is
given as
εPM2.5t=PM2.5BLt-PM2.5BLMLRt.εPM2.5 contains not only the variability of PM2.5
related to long-term changes in air pollutant emissions but also the minor
seasonal change of PM2.5 attributable to unconsidered meteorological
influences in the multiple linear regression. By removing the minor seasonal
change from εPM2.5 with the KZ365,3
filter, the emissions-related long-term component
PM2.5LTemiss(t) can be isolated as follows:
PM2.5LTemisst=KZ365,3εPM2.5t.
Here the long-term component of surface PM2.5 concentrations can be
further separated into the emission- and meteorology-related long-term
components with Eqs. (9) and (4) (Seo et al., 2018). Similarly, the
multiple timescale variations in SO2 and NO2 with long-term
variations related to changes in air pollutant emissions and meteorological
drivers are decomposed by the KZ filter with multiple linear regression. Seo et
al. (2018) described the details of this method.
Results and discussionVerification of PM2.5 decompositions in
multi-scale variations
The daily PM2.5 concentrations observed at 14 sites over the
THB (Fig. 1) were decomposed into short-term, seasonal and long-term
components with Eqs. (4), (5) and (6) of the KZ filter. To verify the
decomposition results, the spatial distribution of the total contributions of
the short-term, seasonal and long-term PM2.5 components to the total
variances of observed daily changes in PM2.5 concentrations over
2015–2019 are shown in Fig. 2a. The larger the total variance, the more
independent the three components are of each other (Chen et al., 2019).
The sum of the long-term, seasonal and short-term components contributed
91.4 %–94.4 % of the total variance, with regional averages of
92.7 % (Fig. 2), reflecting a satisfactory verification of the KZ filtering results.
Spatial distributions of the (a) short-term, (b) seasonal and (c) long-term components, and (d) total contribution to the total variances of daily PM2.5 changes observed at 14 sites in the THB with regional averages of 47.5 %, 41.4 %, 3.7 % and 92.7 %, respectively.
Based on the PM2.5 decomposition results of the KZ filter, the
short-term, seasonal and long-term components, respectively, accounted for
34.8 %–53.8 %, 29.2 %–56.3 % and 0.2 %–9.8 % of the
total variances of daily PM2.5 changes in the THB over recent years
(Fig. 2b, c and d), reflecting the different patterns of multiple timescale
variations of PM2.5 over this region in central China with diverse
effects of emissions and meteorology. The regional contributions of the averaged
short-term, seasonal and long-term components were, respectively,
with 47.5 %, 41.4 % and 3.7 % of the daily PM2.5 changes over the
THB (Fig. 2), and thus it could be reasonably verified that the daily variation in
atmospheric pollutant was generally dominated by the short-term and seasonal
components, with the long-term component determining the change trend (Ma et
al., 2016; Yin et al., 2019a).
The short-term, seasonal and long-term PM2.5 components were averaged
for 14 sites of the THB to characterize the temporal variations of three
components in the THB for 2015–2019 (Fig. 3). The correlation coefficients
of 0.05, 0.01 and 0.04 among the decomposed short-term, seasonal and
long-term components were near zero, indicating the orthogonal decomposition
of multiple timescale components (Eskridge et al., 1997). According to the
decomposed long-term, seasonal and short-term components demonstrated in
Fig. 3, the notable peaks of decomposed seasonal and short-term components
were highly consistent with the peaks of PM2.5 concentrations in the
original observed data, which further proved the reasonable decomposition of
the multi-scale components of PM2.5 change over 2015–2019.
The relations of regional averages of (a) short-term (PM2.5-ST), (b) seasonal (PM2.5-SN) and (c) long-term (PM2.5-LT) components with the observed daily PM2.5 concentrations (PM2.5) over the THB from 2015 to 2019.
The observed daily PM2.5 exhibited a distinct daily variation, with an
overlapping of high-frequency variations, which could be caused by mesoscale
and synoptic-scale meteorological processes (Ma et al., 2016). The
short-term component of PM2.5 fluctuated frequently with a
significantly positive correlation to the daily change of PM2.5 (r=0.68, p<0.05), indicating the important role of the short-term
component with the temporal period <33 d in the day-to-day
variations of PM2.5 concentrations in the THB (Fig. 3a).
The notable peaks of PM2.5 seasonal component that emerged in winters were
highly consistent with the peaks of observed daily PM2.5 concentrations (Fig. 3b). A close linkage with the significant correlation coefficient of
0.75 (p<0.05) was found between the changes of PM2.5 seasonal component and daily PM2.5 concentrations, which could
reflect a significant modulation of the PM2.5 seasonal oscillations in
the day-to-day variations of PM2.5, driven by the seasonal shift of
East Asian summer and winter monsoons, as well as the seasonal change of
anthropogenic emissions (Zhu et al., 2012; Jeong and Park, 2017). The
change of the long-term component of PM2.5 exhibited a steadily declining
trend over 2015–2019 (Fig. 3c), which was consistent with the interannual
trend of observed PM2.5 concentrations under the sustained impact of
emission controls (Zhang et al., 2019; Xu et al., 2020). The correlation
coefficient (r=0.24, p<0.05) of the long-term PM2.5 component
with the observed daily PM2.5 change was much smaller than those of
the short-term and seasonal PM2.5 components, implying less influence of
emission reduction on the daily PM2.5 change and air pollution
frequency, although the declining trend in PM2.5 was determined by
anthropogenic emission reduction.
In previous studies, chemical transport models and statistical methods were
both used to assess the changes in air pollution attributable to emissions
and meteorology (Xiao et al., 2021). Significant declines in
emissions-related PM2.5 concentrations occurred in central China (Wang et al., 2019; L. Chen et al., 2020), and the meteorology offset the
impact of emission reduction in typical years of unfavorable meteorological
conditions (Xu et al., 2020; Gong et al., 2021). The regional averaged
emission- and meteorology-related long-term components and the
long-term component over the THB are displayed in Fig. S1a in the Supplement, and suggest the
steadily declining trend of PM2.5 and the dominant impact of emission
reduction on long-term PM2.5 changes, which is consistent with
previous studies using multiple linear regression models for central China (Fig. S1b). The meteorology-related long-term component has a positive value in
certain periods, implying the significant modulating effect of meteorology
on PM2.5 decline in the THB.
Multiple linear regressions of PM2.5,
SO2 and NO2 with
meteorological drivers
Since the short-term variations in meteorological variables were excluded,
the correlations between baseline components of PM2.5 and
meteorological variables were only related to their seasonal and long-term
components, affected by the regional climate of East Asian monsoons rather than
synoptic-scale meteorological processes. Based on our understanding of
chemical and physical processes of diffusive transport, chemical
transformation, emissions and depositions of PM2.5 in the atmosphere,
the dominant meteorological factors for changing PM2.5 concentrations
over china are wind speed, relative humidity, air temperature, atmospheric
pressure and precipitation (Z. Y. Chen et al., 2020). We examined the
significant correlations between baseline components of air pollutant concentrations and selected a set of meteorological factors, including air
temperature, wind speed, precipitation, relative humidity and air pressure (Tables S1–S3 in the Supplement). The meteorological parameters selected in this study are
consistent with previous studies (Z. Y. Chen et al., 2020).
Generally, the baseline components of air pollutants were negatively
correlated with the baseline components of wind speed (WSBL) and positively
correlated with the baseline components of sea level pressure (SLPBL; Tables S1–S3), which could be attributed to the ventilation effect of wind
and the stagnant conditions of meteorology in high-pressure systems, restraining
the horizontal and vertical dispersions of air pollutants (Hsu and Cheng,
2016; Wang et al., 2016; Miao et al., 2017). Although wind speed exerts a
negative influence of on PM2.5 concentrations over the emission source
region, increasing wind speed might cause the accumulation of PM2.5
concentrations over the downwind region of emission sources (Z. Y. Chen et
al., 2020), which led to the inconsistent influence of WSBL in the
region of central China (Tables S1–S3). Under high surface air temperature
conditions, there are strong thermal activities such as turbulence, making
an accelerated dispersion of air pollutants (Y. Yang et al., 2016). The
negative influence of RHBL and TBL on PM2.5BL, SO2BL and
NO2BL mainly reflected the effect of the seasonal cycle in East Asian
winter and summer monsoons, whereas the influence of precipitation on air
pollutants was more straightforward than other meteorological parameters,
negatively influencing surface pollutant concentrations through the
precipitation washout of air pollutants (Tables S1–S3).
To isolate emissions-related long-term components from long-term components
of PM2.5, NO2 and SO2, the stepwise multiple linear
regressions of PM2.5BL, SO2BL and NO2BL, respectively, with
the baseline components of the meteorological parameters (TBL, WSBL,
RHBL, SLPBL and PreBL) were conducted with Eq. (7) for 14 sites, by adding and deleting meteorological variables based on their
independent statistical significance to obtain the best model fit (Draper, 1998). We evaluated PM2.5BL, SO2BL and NO2BL
fitted by multiple linear regression models with KZ decomposition (Table 1). The multiple linear regressions explained PM2.5BL, SO2BL and
NO2BL with the adjusted determination coefficients (Adj. R2) of
0.5695–0.8093, 0.0630–0.4592 and 0.6304–0.8669, passing the confidence
level of 99 % in all the THB sites, confirming the reasonable construction of the multiple linear regressions. The Adj. R2 of multiple linear
regression for SO2BL were lower than those of PM2.5BL and
NO2BL, which might be attributed to the larger impact of SO2
emission controls on the seasonal and long-term SO2 variations. In
general, the variations of meteorological drivers can well reproduce the
meteorology-related seasonal and long-term variations of PM2.5,
SO2 and NO2 in the THB (Table 1).
Adjusted determination coefficients (Adj. R2) between
the baseline components decomposed by the KZ filter and fitted with multiple
linear regressions, respectively, for PM2.5BL, SO2BL and NO2BL
in 14 sites over the THB. All Adj. R2 passing the confidence level of
99 %.
SitesAdj. R2 of multiple linear regressions PM2.5BLSO2BLNO2BLJZ0.67760.41660.8358XN0.68990.06300.7408XY0.79710.67410.8181JM0.78720.36120.6480YC0.71680.29800.6304SZ0.71750.36120.8669WH0.72890.27180.6653EZ0.71620.45920.7523HG0.69370.19010.7220HS0.56950.27870.6952CS0.73070.12550.7012YY0.75010.10470.7592XG0.67550.43890.7692CD0.70170.17300.6937Interannual variations in air pollutants observed over the THB
PM2.5 consists of chemical components generated in the complex physical
and chemical processes (S. Li et al., 2015). Primary particles are
emitted directly from anthropogenic (e.g., industry, power plants and
vehicles) and natural (e.g., outdoor biomass burning and dust storms)
sources. Secondary particles (e.g., sulfate and nitrate) are converted in the
chemical reactions of the precursor gases (e.g., SO2 and NOx), which
are mainly produced by human activities (S. Li et al., 2015; H. Yang et al.,
2016). Therefore, in addition to the reductions in primary particulate
emissions, control of the secondary aerosol precursor emissions is of great
importance in mitigating air pollution.
The interannual variations of the ratios in annual mean PM2.5, SO2
and NO2 concentrations relative to the annual averages in 2015 over the THB are displayed in Fig. 4. The declines of PM2.5 and SO2 in
2019 averaged over the THB were -26 % and -68 % relative to 2015,
while the decrease ratio in NO2 was only -8 % over this region. The
observed SO2 concentrations had a steeper decrease than PM2.5 and NO2, possibly because the dominant source sectors (i.e., power and
industry) of SO2 significantly reduced their emissions (Zheng et
al., 2018). The power sector was the major contributor to emission reduction
but only accounted for one-third of NOx emissions and the contribution
of transportation to NOx emissions was estimated to have increased over
recent years (Zheng et al., 2018). The interannual variations in
emissions for China were calculated from MEIC (Zheng et al., 2018), the annual total emissions of SO2 and NOx and the PM in the THB
region reported by the National Bureau of Statistic of China
(http://www.stats.gov.cn/tjsj/ndsj/, last access: 17 January 2022),
presenting a more rapid decline of SO2 emissions in the THB than the changes
of PM2.5 and NOx emissions (Fig. S2 in the Supplement). The declining trend of
anthropogenic emissions estimated from emission inventories can support the
explanation of the changes in air pollutant concentrations.
Interannual variations in the ratios of observed annual
mean concentrations of SO2, NO2 and PM2.5 relative to those
in 2015 averaged over the THB.
Figure 5 shows the spatial distributions of 5-year averaged concentrations,
the linear trends and the change rates in interannual variations of PM2.5, SO2 and NO2 observed in the THB over 2015–2019. The
change rates (% yr-1) were calculated with the linear trends by
dividing with temporal-mean concentrations of air pollutants at the
observation sites for the analysis period in Fig. 5. The 5-year averaged PM2.5 concentrations over the THB exceeded the Chinese National
secondary air quality standard of 35 µg m-3 for annual mean PM2.5 concentrations (Fig. 5a), while SO2 and NO2
concentrations reached the secondary standards of 60 and 40 µg m-3 in annual mean SO2 and NO2 concentrations at
most sites over the THB (Fig. 5d and g). Specifically, the 5-year averaged NO2 concentrations exceeded 40 µg m-3 in WH (Wuhan), a megacity in central China, which might be attributable to the large amounts
of traffic transportation. From 2015 to 2019, both PM2.5 and SO2
decreased at all sites over the THB (Fig. 5b and e), whereas NO2
trends changed from mostly negative to positive in some sites (Fig. 5h), possibly due to the spatial disparity of NOx emissions in traffic
sectors (Zheng et al., 2018). The comparison among the change rates of PM2.5, SO2 and NO2 in the THB presented the largest decreases
of SO2 with -40 %–-20 % yr-1 over the 5 years (Fig. 5c, f and i), reflecting the effective control of SO2 emissions in
terms of primary gaseous pollutants.
Spatial distributions of (a, d, g) 5-year averages of (a)PM2.5, (d)SO2 and (g)NO2 concentrations (A,
unit: µg m-3), (b, e, h) the linear trends in interannual
variations of (b)PM2.5, (e)SO2 and (h)NO2 (k,
unit: µg m-3 yr-1), as well as (c, f, i) the change
rates (Rt =k/A, unit: % yr-1) of (c)PM2.5, (f)SO2 and (i)NO2 in the THB over 2015–2019.
There were obvious decreases in regional mean PM2.5, SO2 and NO2 concentrations over the THB (Fig. 4), while the declining degree of PM2.5 and SO2 varied from site to site over the THB and the change
trends in NO2 were weak negative and even positive in certain sites
(Fig. 5c, f and i). These interannual changes of air pollutants in the
THB over recent years were investigated using the emission- and
meteorology-related long-term components of the air pollutants in the next
sections.
Effects of NO2 and SO2 emission reductions on PM2.5 change trends
The declining trend of PM2.5 in China could be partly attributed to the
reduced NOx and SO2 concentrations for producing secondary
aerosols (Zhang et al., 2018). The reduction rates of anthropogenic
emissions markedly accelerated after 2013, decreasing by 59 % for SO2, 21 % for NOx and 33 % for PM2.5 during 2013–2017 over
China (Zheng et al., 2018). In order to assess the effect of changing
precursor pollutant emissions on PM2.5 reductions, we compared the linear
trends of emissions-related long-term components of PM2.5, NO2 and
SO2 decomposed based on Eq. (9) over the THB for 2015–2019 (Fig. 6).
The distinct declining trends of emissions-related long-term PM2.5 and SO2 components, as well as the variable trends of emissions-related
long-term NO2 components were distributed basically consistently with the
positive and negative trends in the interannual variations of air pollutant
concentrations in the THB (middle column of Fig. 5; Fig. 6), demonstrating
that the local emissions of air pollutants could spatially dominate the
long-term variations of air pollutants in central China, especially the
increasing trends in NO2 at some THB sites.
Spatial distributions of the linear trends in
emissions-related long-term components of (a)PM2.5, (b)SO2 and
(c)NO2 (unit: µg m-3 d-1) over 2015–2019 in the THB.
PM2.5 concentrations are changed by emissions of both primary PM2.5 and its gaseous precursors. As major gaseous precursors,
SO2 and NO2 can be oxidized to convert nitrate and sulfate for
secondary PM2.5 (S. Li et al., 2015). To investigate the effects of
emission reductions on the interannual variations of PM2.5, NO2
and SO2 over recent years, the ratios of change trends in long-term
(kLT) and emissions-related long-term (kemiss)
components of PM2.5, SO2 and NO2, in the THB over
2015–2019 were demonstrated in Fig. 7, where the long-term and
emissions-related long-term components of PM2.5, SO2 and NO2
were calculated with Eqs. (4) and (9). The trend ratios
kLT/kemiss<1 indicated the more obvious downward trend of emissions-related
long-term variations than the long-term trend of air pollutant
concentrations, which might be attributed to the offsetting effect of
meteorological conditions on emission reduction in the air quality change,
whereas the long-term trend of air pollutant concentrations was more
significant than the emissions-related long-term trend as kLT/kemiss>1, reflecting the synchronous impacts of anthropogenic
emissions and meteorology on the long-term trend in air pollutant change. In
addition, the trend ratios kLT/kemiss>1 and kLT/kemiss<1 of the gaseous precursors of PM2.5, SO2 and NO2, could
reflect the high and weak efficiencies of SO2 and NO2, converting
to sulfate and nitrate in the production of secondary PM2.5 during air
pollutant emission reduction. The notable differences in Fig. 7 were
spatially distributed with the trend ratios kLT/kemiss>1 and kLT/kemiss<1 in PM2.5, SO2 and NO2 concentrations under the
same meteorological conditions, indicating the different influences of
emissions on the long-term variations of PM2.5, SO2 and NO2
in the THB during recent years. The reduction in PM2.5 emissions was a
primary cause of the long-term declines in PM2.5 concentrations in the
THB, even though the meteorological changes might offset the effects of
emission reduction on air quality improvement over the southern THB (Figs. 6
and 7). It is noteworthy that the trend ratios
kLT/kemiss<1 of PM2.5 were accompanied with kLT/kemiss>1 of SO2 and NO2 at the downwind southern THB sites
with both negative kLT and kemiss (Fig. 7, Table S4 in the Supplement), which could imply the increasing conversion efficiency of SO2 and
NO2 to sulfate and nitrate for secondary PM2.5 during the
reductions of air pollutant emissions over recent years. In the upwind
northern THB sites, the kLT/kemiss>1 of PM2.5 were accompanied with
kLT/kemiss>1 of SO2 and NO2 with an obvious facilitating effect
of meteorology on the PM2.5 decline (Fig. 7, Table S4), revealing the
underlying effect of regional transport of air pollutants on the spatial
distribution of conversion efficiency of gaseous precursor to secondary PM2.5.
Spatial distributions of the ratios of linear trends in
long-term components (kLT) and emissions-related long-term
components (kemiss) of (a)PM2.5, (b)SO2 and (c)NO2 at 14 sites in the THB over 2015–2019.
In order to further assess the effect of gaseous precursor emissions on PM2.5 declines during recent 5-year air pollution mitigation, we
selected seven and nine sites in the THB with decreasing trends of
emissions-related long-term SO2 and NO2 components below -0.5 and
0.0 µg m-3 100 d-1, respectively, (Table S4) to compare the
trend ratios kLT/kemiss
of PM2.5, NO2 and SO2 for 2015–2019 (Fig. 8). The
significantly negative linear correlations between the changes in kLT/kemiss
of gaseous precursors (SO2, NO2) and PM2.5 could present
the connection of kLT/kemiss>1 of NO2 and SO2 with kLT/kemiss<1 of PM2.5, which confirms the fact that the high
conversion efficiency of SO2 and NO2 to sulfate and nitrate could
have have offset the role of PM2.5 emission reduction in controlling PM2.5
pollution. The decreasing emissions of gaseous precursors drove faster
oxidation of NO2 and SO2 to nitrate and sulfate components of
PM2.5 in the source regions of air pollution in China (Zhai et al.,
2021; Huang et al., 2021). This study identified the enhancing contribution
of gaseous precursors to PM2.5 concentrations with reduced
anthropogenic emissions of air pollutants over receptor regions in
regional PM2.5 transport.
Scatter plots of the ratios between kLT and
kemiss of (a)SO2, (b)NO2 and PM2.5 in the THB from
2015 to 2019 with red lines for the linear fitting equations.
There are a few sources of uncertainty in the discussion of chemical
transformation, for example in the separation of emission- and
meteorology-related long-term components and in the selection of
observational sites. Our results point to a better understanding of the offsetting
effect of SO2 and NO2 oxidized to secondary particles in PM2.5 mitigation during the emission reduction in the THB. The
long-term changes in PM2.5 are also caused by the emission variations
of primary components like black and organic carbon, in addition to the
chemical transformation of gaseous precursors. The difference in the
emission of different primary pollutants may also lead to modifications in
kLTkLTkemisskemiss
of PM2.5. However, due to the current lack of long-term observations of
PM2.5 components in the THB, the influence of emission variations of
primary components on long-term changes in PM2.5 concentrations is not
assessed in our study. Further work with long-term observational data of PM2.5 components like black and organic carbon could be conducted to
quantify the influence of emissions of primary components and chemical
transformation of gaseous precursors on PM2.5 changes.
Meteorological contribution to PM2.5 change
trends
As the air pollutant change trend is assumed to generally consist of
emission- and meteorology-related changes (Seo et al., 2018; Yin et al.,
2019b), the meteorological contribution rate Conmet to the long-term PM2.5 change trend is calculated with the following equation:
Conmet=kLT-kemisskLT×100%.
Here, Conmet (in %) is estimated with the linear trends kLT
of the long-term component PM2.5LT(t) and kemiss of the emissions-related
long-term component PM2.5LTemiss(t). PM2.5LT(t)
and PM2.5LTemiss(t) are, respectively, calculated with
Eqs. (4) and (9).
To quantitatively assess the meteorological contribution to the declining PM2.5 trends, the linear trends kLT and
kemiss with the meteorological contribution rate Conmet
in Eq. (10) were presented in Table S5 in the Supplement for 14 sites over the THB during
2015–2019. All the trends kLT and kemiss,
respectively, in PM2.5LT(t) and PM2.5LTemiss(t)
were negative over the THB (Table S5), indicating the significant effect of
emission reductions on declining PM2.5 trends for improving regional
air quality in central China. By comparing the declining PM2.5 trends
kemiss and kLT from site to site (Table S5),
the positive and negative contributions of meteorological variations to PM2.5 change trends over recent years were determined with the positive
and negative differences between kemiss and kLT with the distinct meteorological influences on the change of the THB regional
environment.
The spatial distribution of meteorological contribution rates Conmet
regarding the long-term declining PM2.5 trend presented a unique pattern of
northern positive and southern negative values over the THB (Fig. 9), with
high positive contributions in the northern sites XY (61.92 %) and EZ (37.31 %), and as low negative contributions in the southern sites CD (-24.93 %) and CS (-23.03 %). It is worth mentioning that the contribution
rates of meteorological variations show great spatial disparities at a small
scale, i.e., EZ, HG and HS, which seems not be induced by the variation in
synoptic weather or meteorological conditions. The underlying surface
conditions dominate the near-surface meteorological conditions in the
atmospheric boundary layer at a small scale (Wang et al.,
2017). The topography and land use of HG, HS, EZ and the surrounding regions
vary distinctly with the underlying surface conditions of plains, lakes and hilly
areas. The underlying surface of observational sites with different
near-surface meteorology effectively influences the local accumulation,
chemical transformation, and dry and wet depositions of air pollutants (Bai et al., 2022). Therefore, the heterogeneity of meteorological
contributions to PM2.5 at such a small spatial scale might be attributed
to the local meteorological conditions in the atmospheric boundary layer,
which are largely affected by the underlying surface changes.
Spatial distribution of contribution rates (colored dots,
unit: %) of meteorological variations regarding PM2.5 reductions with
topographical height (color contours, m a.s.l.) in the THB (outlined
with orange dashed line) and surrounding regions from 2015 to 2019.
Compared with the statistical studies using synthetic data of
meteorological influence on regional PM2.5 changes in central China
and the meteorological contribution from -45.5 % to 29.0 % over
recent years (Gong et al., 2021; L. Chen et al., 2020), the PM2.5
pollution over the THB was affected contrarily by meteorological drivers
with the northern positive and southern negative contributions from 2015 to
2019 (Fig. 9). The meteorological change could accelerate and offset the
effects of emission reductions on declining PM2.5 trends in the
northern and southern THB, which might be attributed to the regional transport
of air pollutants conducive to the upwind diffusion and downward
accumulation of air pollutants, respectively, over the northern and southern
THB under the declining wind of East Asian monsoons over recent years (Hu
et al., 2020; Zhong et al., 2019).
Meteorological contribution to PM2.5 changes
validated with WRF-Chem modeling
The above observational study investigated the meteorological influence on
the changes in PM2.5 concentrations in the THB using the KZ filter, concluding on the large impact of meteorology on the PM2.5 changes over
2015–2019. To validate this conclusion of analyses with the KZ filter, we
designed three sets of modeling experiments CTRL, SENS-MET and SENS-EMI
(Table S6 in the Supplement) for December of 2015–2019, respectively, driven with the changing
meteorology and anthropogenic emissions over 2015–2019, fixed
meteorological conditions and anthropogenic emissions of 2015 with
atmospheric chemical model WRF-Chem (Weather Research and Forecasting model
with Chemistry). Air pollutant emission inventories, modeling configuration,
experiment design and modeling verification are described in the
Supplement. The modeling verification of experiments CTRL indicated that PM2.5 and meteorology were reasonably reproduced by the WRF-Chem
simulation (Figs. S4–S5, Table S7 in the Supplement), and the three designed sets of modeling
experiments CTRL, SENS-MET and SENS-EMI can be used in the further
analyses of emission and meteorological impact on PM2.5 change over
2015–2019 to confirm the results of the KZ filter.
We derived the impact of meteorology by comparing the simulated PM2.5
concentrations in three sets of experiments CTRL, SENS-MET and SENS-EMI
(Table S6). The relative contribution of meteorology to the interannual
changes of PM2.5 concentrations was calculated with a linear additive
relationship of contributions of meteorology and emission in the following
equations:
11ConMET=kMETkCTRL,12ConEMI=kEMIkCTRL,13RConMET=ConMETConMET+ConEMI×100%.kCTRL,kMET and kEMI represent the trends in interannual changes of PM2.5 concentrations simulated by the experiments CTRL, SENS-MET and
SENS-EMI, respectively. ConMET and ConEMI are the contributions
of meteorology and emission, and RConMET is the contribution rate
(%) of meteorology to interannual changes of PM2.5 concentrations (Zhang et al., 2020).
Based on WRF-Chem modeling experiments, we assessed the impact of
meteorological changes on interannual PM2.5 variations from 2015 to
2019 with Eqs. (11)–(13). The relative contribution of meteorology to interannual PM2.5 variations displayed a regional pattern of northern positive
and southern negative values over the THB (Fig. 10), confirming the impact
of meteorological changes by accelerating and offsetting the effects of
emission reductions on declining PM2.5 trends in the northern and
southern THB, respectively. The general spatial distribution of
meteorological contribution rates regarding declining PM2.5 trends from the WRF-Chem simulation was consistent with the results using the KZ filter (Figs. 9 and 10), validating these results and confirming that meteorological drivers exerted a contrary impact between the northern positive and southern negative contributions to long-term changes of PM2.5 concentrations in the THB.
Spatial distribution of contribution rates of
meteorological variations regarding PM2.5 reductions based on WRF-Chem
modeling experiments (contour, unit: %) in the THB outlined with black
dashed line and surrounding regions for December of 2015–2019.
Conclusions
The meteorological effect on multi-scale changes of the atmospheric environment
has been rarely assessed for the receptor region in the regional transport of air pollutants. In this study of observations and modeling, we targeted the THB,
a large region of heavy PM2.5 pollution in central China, to assess
the effects of meteorology on PM2.5 changes over a receptor region in the
regional transport of air pollutants during recent years. The study
results provide insights into the effects of emission mitigation and
meteorological changes on the source–receptor relationship of the long-range
transport of air pollutants for regional and global environment changes.
This study decomposed the observed PM2.5 concentrations into multiple timescale components with a modified KZ filter to better understand the PM2.5 variations, with the short-term, seasonal and long-term components accounting for, respectively, 47.5 %, 41.4 % and 3.7 % of observed PM2.5 changes. The short-term and seasonal PM2.5 components
dominated the daily PM2.5 changes and long-term component determined
the trend of PM2.5 change over recent years. The long-term components
of PM2.5, SO2 and NO2 were further isolated into emission-
and meteorology-related long-term components with multiple linear
regressions in order to determine the contributions of emission and meteorology to
PM2.5 decline in the THB over 2015–2019. The reduction in
anthropogenic emissions was the primary cause of the long-term decline in PM2.5 concentrations, and the meteorological changes moderated the PM2.5 variations in the THB. As the receptor region of regional
PM2.5 transport, the impact of diverse meteorological conditions on
long-term trend of PM2.5 changes displayed a unique regional pattern of
northern positive rates up to 61.92 % and southern negative rates down to -24.93 %. The change of meteorological conditions could accelerate and
offset the effects of the emission reductions on declining PM2.5 trends in
the northern and southern THB, which can be attributed to the upwind
diffusing and downward accumulating roles of regional transport pathways in
air pollutants in the THB. In terms of gaseous precursor emissions, the
increasing conversion efficiency of SO2 and NO2 to sulfate and
nitrate for secondary PM2.5 could have offset the role of emission reduction
in controlling air pollution, and the contribution of gaseous precursors to
secondary PM2.5 was enhanced with the reducing anthropogenic emissions of
air pollutants over this receptor region.
This study revealed the impact of anthropogenic emissions and meteorological
conditions on PM2.5 decline over a receptor region in the regional
transport of air pollutants in central China. The effect of regional
transport on PM2.5 pollution over receptor regions is found
to differ from that over source regions with high anthropogenic
emissions. We considered the uncertainties induced by statistical
methods as systematic biases and explained the offsetting effect of
enhancing oxidation of gaseous precursors to secondary particles on PM2.5 decline during stringent emission controls. The changes in
data coverage and meteorological parameter selection largely
influence the quantitative estimation of the contributions of meteorology and
emissions. Further work should be undertaken regarding climate analysis of the long-term data of fine meteorological and environmental observations and more comprehensive modeling of chemical and physical processes in the
atmosphere to generalize the assessment of the effects of emission
mitigation and meteorological changes on the source–receptor relationship of
the regional transport of air pollutants.
Data availability
Data used in this paper can be provided upon request from Xiaoyun Sun (sunxy6362@126.com) or Tianliang Zhao (tlzhao@nuist.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-3579-2022-supplement.
Author contributions
TZ and XS conceived the study. YB provided the observation data and
analysis. XS designed the graphics and wrote the manuscript with help from
TZ, YB and SK. HZ, WH, XM and JX were involved in the scientific discussion.
All authors commented on the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This research was financially funded by grants from National Natural
Science Foundation of China (grant nos. 41830965, 42075186 and 91744209) and the National
Key R & D Program Pilot Projects of China (grant no. 2016YFC0203304).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41830965, 42075186 and 91744209) and the National Key Research and Development Program of China (grant no. 2016YFC0203304).
Review statement
This paper was edited by Leiming Zhang and reviewed by three anonymous referees.
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