Introduction
As a consequence of rapid industrialization and urbanization, China has been
suffering from air quality degradation in recent years (Zhao et al., 2011; Fu
et al., 2014; Han et al., 2014; Gao et al., 2015; Hao et al., 2017).
Frequently occurring severe haze is characterized by long duration, extensive
coverage, and sharply increasing particulate concentration (Tao et al., 2014;
X. Y. Zhang et al., 2015; G. Wang et al., 2016; Jiang and Xia, 2017). It has
been suggested that severe haze pollution increases the risk of respiratory
and cardiovascular diseases (Chen et al., 2013; Pan et al., 2014; Z. L. Zhang
et al., 2014; Gao et al., 2015; Zhou et al., 2015). On the basis of previous
statistics, there are four haze-prone city clusters in China, including the
Beijing–Tianjin–Hebei region, Yangtze River Delta, Pearl River Delta, and
Sichuan Basin (Wu et al., 2008; Tao et al., 2013a; Bi et al., 2014; Fu et
al., 2014; Q. Z. Wang et al., 2015; L. Zhang et al., 2015; X. Chen et al.,
2016; H. M. Li et al., 2016; Fu and Chen, 2017). In recent years, the role of
particulates in hazy events has been becoming more and more prominent. The
particulates can be discharged from a variety of sources or formed by
physicochemical or aqueous-oxidation reactions among gaseous precursors,
which have significant negative effects on climate, atmospheric visibility,
and public health (Quinn and Bates, 2003; Tai et al., 2010; Zhang et al.,
2010; Chen et al., 2015; Lee et al., 2015; Shen et al., 2015; Fu and Chen,
2017). The high observed concentrations of fine particles and prolonged haze
events have occurred frequently during autumn and winter and covered large
regions in China. In some cases, the instantaneous mass concentration of
PM2.5 had reached up to 1000 µg m-3 (J. K. Zhang et al.,
2014; Qin et al., 2016), which caused extensive concern from citizens and
government agencies.
Confronted with severe air pollution and degradation of air quality, the
government has implemented a variety of control measures in recent years,
including the odd-and-even license plate rule
(http://www.sjz.gov.cn/col/1496488850551/2016/11/17/1502101082513.html, last access: 5 August 2017), the
mandatory installation of desulfurization, denitration, and other
pollution-controlling facilities in factories (Ma et al.,
2015; Liu et al., 2017a; Peng et al., 2017), and the on-line monitoring system structure plan
on
construction sites, for example. The atmospheric quality in China has been notably
improved so far. From 2013 to 2016, the concentrations of atmospheric
pollutants in China showed a decreasing trend, and the annual mean
concentrations of PM2.5, PM10, SO2, and NO2 in 2016
reached up to 50, 85, 21, and
39 µg m-3, respectively, and significantly lower than those in 2013
(http://www.zhb.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/, last access: 5 August 2017). However, the annual mean
concentrations of PM2.5 and PM10 in 2016 were still 1.4 and 1.2
times higher than the National Ambient Air Quality Standards (NAAQS)
(GB3095-2012 guideline value (annual) of Grade II, PM2.5: 35 µg m-3; PM10 : 70 µg m-3). Note that the concentrations of PM2.5 and PM10
in the
Beijing–Tianjin–Hebei region were up to 71 and 119 µg m-3 in 2016 and 2.0 and 1.7 times higher than the NAAQS
(GB3095-2012 guideline value (annual) of Grade II),
respectively. Therefore, China still has a lot of work to do to improve the
national air quality.
Over the last decade, the Chinese government has implemented stricter control
measures for emission sources during multiple international events held in
China than normal times (T. Wang et al., 2010; Guo et al., 2013; Liu et al.,
2013; P. L. Chen et al., 2016; Sun et al., 2016; Wang et al., 2017). For
instance, the first attempt took place during the Beijing 2008 Olympic Games
(Guo et al., 2013). Drastic control actions were executed to cut down the
emissions of atmospheric pollutants from motor vehicles, industries, and
building construction activities
(UNEP, 2009; M. Wang et al., 2009; T. Wang et al., 2010). UNEP (2009)
suggested that the concentration of PM10 in Beijing was reduced by
20 % due to the emission reduction measures. Liu et al. (2013) reported
that the concentrations of SO2, NO2, PM10, and
PM2.5 were reduced by 66.8, 51.3, 21.5, and 17.1 %, respectively,
during the 2010 Asian Games in Guangzhou, China, and during which stricter
control measures for emission sources were implemented. Furthermore, further
stricter controls for emission sources were implemented in both Beijing and
its surrounding regions during the 2014 Asia-Pacific Economic
Cooperation (APEC) summit and parade. Compared to no control during APEC and
parade, a decreasing trend with 51.6–65.1 and 34.2–64.7 % of PM2.5
concentrations during the control period was reported (Wang et al., 2017).
Eventually, all the efforts led to blue-sky days during the APEC, which was
acknowledged as “APEC Blue” (H. B. Wang et al., 2016). As we can see, the
air quality can be improved in response to stricter emission controls for
international events held in China. However, once these stricter control
measures were repealed and the air quality subsequently deteriorated
(http://www.mep.gov.cn/gkml/hbb/qt/201412/t20141218_293152.htm, last
access: 7 August 2017), it was clear that the prevention and control of air
pollution in China still had a long way to go.
Map of the online monitoring stations and the filter membrane
sampling sites in Shijiazhuang. The 24 online monitoring stations mainly
include the 22nd middle school (TSMS), Fenglong Mountain (FLM), high-tech
zone (HTZ), Great Hall of the People (GHP), Century Park (CP), water source
area in the northwest (WSAN), university area in the southwest (UAS), staff
hospital (SH), Gaoyi (GY), Gaocheng (GC), Xingtang (XT), Jinzhou (JZ),
Jingxing mining district (JXMD), Lingshou (LS), Luquan (LQ), Luancheng (LC),
Pingshan (PS), Shenze (SZ), Wuji (WJ), Xinle (XL), Yuanshi (YS), Zanhuang (ZH),
Zhaoxian (ZX), and Zhengding (ZD). The filter membrane sampling sites are
mainly located in TSMS, LQ, and LC.
Shijiazhuang (38.05∘ N, 114.58∘ E), a hinterland city of the North
China Plain with a high population density, is an important city in the
Beijing–Tianjin–Hebei region (Sun et al., 2013). The rapid industry
development has a great contribution to this city's economic growth and
degradation of air quality at the same time (Du et al., 2010; Li et al.,
2015; Yang et al., 2015; L. L. Yang et al., 2016). Shijiazhuang has been one of the cities
with the most serious air pollution in the world
(https://www.statista.com/chart/4887/the-20-worst-cities-worldwide-for-air-pollution/, last access: 9 August 2017),
and deteriorating air quality poses a great risk to public health
(http://www.who.int/en/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health, last access: 9 August 2017), as well as the expansion
of the economy. The government of Shijiazhuang has adopted a variety of control
measures (http://www.sjzhb.gov.cn/, last access: 9 August 2017); however, it seems that there is no improvement
in air quality of Shijiazhuang so far, and the
atmospheric pollution is still heavy. In 2016, the annual concentrations of
PM2.5 and PM10 in Shijiazhuang reached up to 70
and 123 µg m-3, respectively, which were 2.0 and 1.8 times higher
than the NAAQS (GB3095-2012 Grade II) (http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/dqhjzlbz/, last access: 10 August 2017). Especially in the heating period in winter, the degree of
atmospheric pollution in Shijiazhuang was even more serious. The
effectiveness of control measures has been queried in recent years.
Therefore, based on previous examples of APEC, parade and the Asian Games,
etc., a large-scale controlling experiment for atmospheric pollutant
sources (i.e. TECA) was designed and implemented to investigate whether
control measures in Shijiazhuang were effective for the atmospheric
pollution. The experiment was carried out in Shijiazhuang from 1 November
2016 to 9 January 2017, during which more stringent control measures of
atmospheric pollution than usual were put into practice. Then, by combining the changes of atmospheric pollutants concentrations, emission source
contributions, and other factors such as meteorological conditions, regional
transmission, etc., the effectiveness of control measures was evaluated
before and after the control measures were taken.
The meteorological conditions during the four stages (NCANHP,
NCAHP, CAHP, and ACA) of the TECA period in Shijiazhuang.
NCANHPc
NCAHPd
CAHPe
ACAf
Ave.a
SDb
Ave.
SD
Ave.
SD
Ave.
SD
Temperature (∘C)
8.4
3.6
7.4
2.4
3.1
3.8
0.7
2.7
Relative humidity (%)
77.7
17.0
73.4
15.7
71.5
18.0
83.3
18.1
Wind speed (m s-1)
0.7
1.2
0.6
0.6
0.4
1.0
0.5
1.1
Height of mixed layer (m)
540
144
590
274
474
299
431
360
a Ave. represents average value. b SD represents standard deviation. c NCANHP
represents the no-control action and no-heating period. d NCAHP represents the
no-control action and heating period. e CAHP represents the control action and
heating period. f ACA represents after control action.
Materials and methods
Site description
Shijiazhuang City is located in the east of the Taihang Mountains in the
north of China (Fig. 1), and the urban area is 15848 km2, with a
population of more than 10 million in 2016. Shijiazhuang is a large
industrial city that is famous for raw materials, energy production, and the
steel, power, and cement industries. The number of vehicles was more than
2.0 million until 2016. Shijiazhuang has a typical temperate and monsoonal
climate with four clearly distinct seasons, with northeasterly,
southeasterly, and northwesterly winds prevailing during the TECA period
(Fig. S1 in the Supplement). The mean wind speed was 0.6 m s-1, and
the average temperature was 4.9 ∘C during the
TECA period. The mean relative humidity was up to 76.5 %, and the mean
height of the mixed layer was 509 m during the TECA period. The
meteorological conditions during the four stages of the TECA period in
Shijiazhuang were shown in Table 1.
The seven monitoring sites including the 22nd middle school (TSMS), high-tech
zone (HTZ), Great Hall of the People (GHP), Century Park (CP), water source
area in the northwest (WSAN), university area in the southwest (UAS), and
staff hospital (SH) are located in urban area of Shijiazhuang. The other 17
sites including Fenglong Mountain (FLM), Gaoyi (GY), Gaocheng (GC),
Xingtang (XT), Jinzhou (JZ), Jingxing mining district (JXMD), Lingshou (LS),
Luquan (LQ), Luancheng (LC), Pingshan (PS), Shenze (SZ), Wuji (WJ), Xinle (XL),
Yuanshi (YS), Zanhuang (ZH), Zhaoxian (ZX), and Zhengding (ZD) are situated
in suburbs of Shijiazhuang. More details are shown in Table S1 in the
Supplement.
Sampling and analysis
Sampling
From 1 November 2016 to 9 January 2017, the concentrations of PM2.5,
PM10, SO2, NO2, CO, and O3 and synchronous meteorological
conditions (temperature, relative humidity, wind speed, and wind direction)
were monitored in the 24 online monitoring sites belonging to national,
provincial, and city controlling points (Fig. 1). More details about
monitoring instruments are described in Table S2. The heights of the mixed
layer were measured with a lidar scanner (AGHJ-I-LIDAR; HPL), which was set
at an atmospheric gradient monitoring station in Shijiazhuang near the CP site
(Fig. 1), and more details are shown in the Supplement. The
PM2.5 filter membrane samples were collected at the TSMS, LQ, and LC sites
from 24 November 2015 to 9 January 2017. Three sampling sites were set on
the rooftops of buildings at 12–15 m above ground level. Meanwhile, the
parallel samples and the field blanks were also collected at each site. More
details about filter membrane sampling are shown in Table S3. Before
sampling, the quartz filter membranes (47 mm in diameter, Whatman, England)
and polypropylene filter membranes (47 mm in diameter, Beijing Synthetic
Fiber Research Institute, China) were baked in the oven at 500
and 60 ∘C, respectively. All the filter membranes after sampling
were stored at 4 ∘C before subsequent gravimetric and chemical
analysis to improve the accuracy of experimental results.
Gravimetric and chemical analysis
A 24 h equilibrium process of PM2.5 filter membranes was performed
at a condition of constant temperature (20 ± 1 ∘C) and humidity
(45–55 %) before gravimetric analysis. For the gravimetric analysis, all
the filter membranes were weighted twice on a microbalance with a resolution
of 0.01 mg (Mettler Toledo, XS105DU) before and after sampling. An
electrostatic eliminating device was applied to ensure the accuracy of
gravimetric results.
After the gravimetric analysis, the quartz filter membranes which carried
atmospheric particulates were used to analyse water-soluble ions by ion
chromatography (Thermo Fisher Scientific, Dionex, ICS-5000+). One-eighth
of the filter membrane was cut up and put into a 25 mL glass tube with 20 mL
ultrapure water. After 1 h of ultrasonic extraction and 3 min of
centrifugalization, the supernatant was filtered with a disposable filter head
(0.22 µm) for subsequent instrumental analysis. The ions analysed
included SO42-, NO3-, Cl-, NH4+, K+,
Ca2+, Na+, and Mg2+, and more details were shown in Figs. S2 and S3. Prior to the ion detection, standard solutions were prepared and
detected more than three times and low relative standard deviations (RSDs)
were obtained. Analytical quantification was carried out by using
calibration curves made from standard solutions prepared.
Polypropylene filter membranes were used for elemental analysis by
inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700x).
The perchloric acid–nitric acid digestion method was applied for the
pretreatment of filter membranes. Aggregately, 10 elemental species (Al, Si,
Ti, Cr, Mn, Fe, Cu, Zn, As, and Pb) were determined. The detection limits of
all the elements are shown in Table S4. For quality assurance and quality
control (QA / QC), standard reference materials were pretreated and analysed
with the same procedure, with the recovered values for all the target
elements falling into the range of or within 5 % of certified values.
The organic carbon (OC) and elemental carbon (EC) were determined on a 0.558 cm2 quartz filter membrane
punch by Desert Research Institute (DRI) model 2001 thermal–optical carbon
analyser with IMPROVE A thermal–optical reflectance (TOR) protocol. The
quartz filter membrane was heated stepwise to temperatures of 140, 280, 480, and 580 ∘C in
a non-oxidizing helium (He) oven to analyse OC1, OC2, OC3, and OC4,
respectively. Then, the oven was added to an oxidizing atmosphere of 2 %
oxygen (O2) and 98 % He, and the quartz filter membrane was
gradually heated to 580, 740, and 840 ∘C to analyse EC1, EC2, and EC3, respectively. The POC is defined as the
carbon combusted after the initial introduction of oxygen and before the
laser reflectance signal achieves its original value and the POC is
specified as the fraction of OC. According to the IMPROVE A protocol, OC is
defined as OC1 + OC2 + OC3 + OC4 + POC, and EC is defined as
EC1 + EC2 + EC3 - POC. For QA / QC, we carried out the measurement with the
field blank filter membranes, standard sucrose solution, and repeated
analysis in the study. During each season, the field blanks were sampled and
the particulate samples have been corrected by the average concentration of
the blanks. For checking the precision of the instrument, a replicate sample was
analysed for every 10 samples, and the standard deviation < ±5 % was accepted. The method detection limits (MDLs) of OC and EC are
0.45 and 0.06 µg cm-2, respectively.
PMF model
The PMF model can decompose a matrix of sample data (X) into two
matrices: source profile (F) and source contribution
(G), in terms of observations at the sampling sites (Paatero and
Tapper, 1994). The principle of PMF model can be described by
Xij=∑k=1pgikfkj+eij,
where Xij represents the concentration of the jth species in
the ith sample, gik represents the contribution of the kth source to
the ith sample, fkj represents the source profile of the jth species
from the kth source, eijrepresents the residual for the jth species
in the ith sample, and p represents the number of sources.
PMF can identify emission sources of PM2.5 without source profiles.
Data below MDLs are retained for use in the PMF model with the related
uncertainty adjusted in terms of the characteristic that the PMF model admits
data to be signally weighed. To assess the stability of the solution, the
object function Q can be allowed to review the distribution of each species,
which is expressed by
Q=∑i=1n∑j=1mxij-∑k=1pgikfkjμij2,
where μij represents the uncertainty of the jth species in the
ith sample, which is applied to weight the observations that include the
sampling errors, missing data, detection limits, and outliers.
The purpose of the PMF model was to minimize the function (Eq. ). Data below
MDLs were retained and their uncertainties were set to 5/6 of the MDLs.
Missing values were replaced by the median concentration of a given species,
with an uncertainty of 4 times the median (Brown et al., 2015). For values
that were larger than the MDLs, the calculation of uncertainty was in terms
of a user-supplied fraction of the concentration and MDLs, and the error
fraction was suggested as 10 % by Paatero (2000). Uncertainty was
described by
uncertainty=(errorfraction×concentration)2+(0.5×MDL)2.
In this study, the EPA PMF 5.0 model was used to identify the PM2.5
sources in Shijiazhuang City based on the field investigation and change of
Q values, and finally five factors were chosen in PMF
analysis. When five
factors were chosen and input into PMF model, the calculated Q value (5162)
from the PMF model was close to theoretical values (5045). The observed
PM2.5 concentrations and calculated PM2.5 concentrations from the
PMF model showed high correlation
(r= 0.96) (Fig. S4). S/N is the signal-to-noise ratio, which is used to
address weak and bad variables when running the PMF model (Paatero and Hopke,
2003). The signal vector is identified as S and the noise vector is
identified as N. Next, S/N is defined as Eq. (). Variables with
S/N ≤ 0.2 were removed from the analysis, while weak variables
(0.2 ≤ S/N ≤ 2.0) were down-weighted (Ancelet et al., 2012).
S/N ratios of As, Ti, and Cr were lower than 1.0 in this study, and these
species were set as weak variables.
S/N=∑si2/∑ni2,
where i represents the chemical species in PM2.5.
Backward trajectory and PSCF model
In this study, the 72 h backward trajectory arriving in Shijiazhuang
(38.05∘ N, 114.58∘ E) was calculated at 1 h intervals
during the CAHP by the Hybrid Single-Particle Lagrangian Integrated
Trajectory (HYSPLIT) model. The final global analysis data were produced
from the National Centers for Environmental Prediction's Global Data
Assimilation System wind field reanalysis (https://ready.arl.noaa.gov/archives.php, last access: 6 July 2017). The
model was run four times per day at starting times, i.e. 00:00, 06:00, 12:00,
18:00 LT; the starting height was set at 100 m above the ground. The potential source contribution function
(PSCF)
model was used to identify the potential source areas in terms of the
HYSPLIT analysis. The study region was divided into i×j small, equal grid cells.
The trajectory clustering and PSCF model were performed by using the
GIS-based software TrajStat (Y. Q. Wang et al., 2009; Liu et al., 2017a). The
PSCF value was defined as
PSCF=mijnij,
where i and j were the latitude and longitude indices, nij represented the
number of endpoints that fell in the ij cell, and mij was the number of
endpoints in the same cell that were related to the samples that were
greater than the threshold criterion.
Based on the NAAQS (GB3095-2012 guideline value (24 h) of Grade II), the
criterion values of PM2.5, PM10, NO2, and CO were set to 75, 150, 80 µg m-3, and 4 mg m-3,
respectively. The criterion values of SO2 and O3 were set to 68 and 15 µg m-3 respectively, in terms of the average
during the CAHP. When nij is smaller than 3 times the grid average
number of the trajectory endpoint (nave), a weighting function
W(nij) was used to reduce uncertainty in cells (Dimitriou et al., 2015).
The weighting function was defined by
WPSCFij=mijnij∗W(nij)
W(nij)=1.00,3nave<nij0.70,1.5nave<nij≤3nave0.40,nave<nij≤1.5nave0.20,nij≤nave.
The studying field ranged from 33 to 51∘ N, and
97 to 121∘ E, and the region that was covered by the
backward trajectories was divided into 432 grid cells of 1.0∘ × 1.0∘. The total number of endpoints during the CAHP
was 12 672. Accordingly, there was an average of 29 trajectory endpoints
per cell (nave= 29).
Measures taken in the controlling experiment
The measures taken in the controlling experiment began on 18 November 2016
and ended on 31 December 2016 in Shijiazhuang
(http://www.sjz.gov.cn/col/1490076478426/2016/11/17/1496988006188.html, last access: 11 July 2017). The
measures taken in the control action were mainly aimed at controlling
emission sources of atmospheric pollutants in Shijiazhuang, which mainly
included five aspects: (1) reduce the usage of coal, (2) decrease industrial
production, (3) inhibit dust emission, (4) restrict driving, and
(5) prohibit open burning. More details are described in the Supplement.
A total of 1543 enterprises were shut down in the whole city of
Shijiazhuang during the control action period, including pharmaceutical,
steel, cement, coking, casting, glass, ceramics, calcium and magnesium,
sheet, sand, and stone processing and other industries. Specific closed enterprises in different districts and counties
are shown in Table S5. In closed enterprises in Shijiazhuang, the number of
mining and stone processing enterprises was the largest, which was up to 733
and accounted for 48 % of all the closed enterprises. The number of
casting and building materials enterprises was up to 297 and 227,
respectively, accounting for 19 and 15 % of all enterprises, respectively.
In addition, 64 enterprises related to the pharmaceutical industry were halted
only for the volatile organic compound (VOC) technology, and the 17 enterprises related to chemical
industry were required to stop production. The number of closed enterprises for the cement
and calcium–magnesium industries was up to 49 and 40, respectively. The
number of closed factories related to furniture and tanneries was 43, and
the number of closed steel and coking enterprises was up to four and seven,
respectively.
The average value of daily social electricity consumption from 18 November to
31 December 2016 was 103 470 000 kW h-1 (Fig. S5), which declined
10 % compared to that of daily social electricity consumption from 1 to
17 November 2016, and declined 6 % compared to that of daily social
electricity consumption during the same period in 2015. Restriction of motor
vehicles based on the odd-and-even license plate rule in the urban area of
Shijiazhuang resulted in the decrease in the average traffic flow on arterial
roads, which was reduced about 30 % compared to before the control action
(Fig. S6). The dust emission can be reduced about 390 t per day by a series
of dust control measures. Compared to before the control action, the daily
emissions of SO2, NOx, smoke dust, and VOCs reduced
about 20, 33, 15, and 7 %, respectively, during the control action
period, on the basis of the statistics on pollutant emission inventories.
The variations in atmospheric pollutant concentrations during the
four stages (NCANHP, NCAHP, CAHP, and ACA) of the TECA period in
Shijiazhuang.
Results and discussion
Variations in atmospheric pollutant concentrations
Temporal trend
The time series of atmospheric pollutant concentrations during the TECA
period are shown in Fig. 2. The average concentrations of PM2.5 and
PM10 during the TECA period in Shijiazhuang were up to 181 and 295 µg m-3, respectively, which was 5.2 and
4.2
times the limit value (annual) of Grade II in the NAAQS. The ratio of
PM2.5 / PM10 reached up to 0.62 during the TECA period, indicating
that fine particulate dominated the particulate pollution in
Shijiazhuang. The mean concentration of PM2.5 during the TECA period
was significantly higher than that during winter in Beijing (95.50 µg m-3), Tianjin (144.6 µg m-3),
Hangzhou (127.9–144.9 µg m-3), Heze (123.6 µg m-3), and Xinxiang (111 µg m-3)
(Gu et al., 2011; Cheng et al., 2015; Liu et al., 2015; Feng et al., 2016; Liu et al., 2017a) and lower than those during winter in Handan (240.6 µg m-3) and Xi'an (266.8 µg m-3) (Meng et al., 2016; Zhang et
al., 2011). Additionally, the NAAQS (GB3095-2012, Grade II) values of
SO2, NO2, O3, and CO were 60, 40, 160 µg m-3, and 4 mg m-3, respectively. During the
TECA period, the average concentration of SO2 (60 µg m-3)
could meet the NAAQS, and that of NO2 (81 µg m-3) far
exceeded the NAAQS, while those of CO (3.4 mg m-3) and O3 (15 µg m-3) were less than the NAAQS.
As is well known, coal-fired heating in Shijiazhuang began in
15 November 2016
(http://www.sjz.gov.cn/col/1497948647350/2016/11/16/1497954667980.html, last access 13 June 2017).
Depending on the changes of atmospheric pollution sources and meteorological
conditions (Table 1), the timeline of the TECA was divided into four stages:
stage 1: no-control action and no-heating period (NCANHP), ranging from
1 to 14 November 2016; stage 2: no-control action and heating period
(NCAHP), ranging from 15 to 17 November 2016; stage 3: control action and
heating period (CAHP), ranging from 18 November to 31 December 2016; stage 4: after control action (ACA), ranging from 1 to 9 January 2017.
During the TECA period, the variations in atmospheric pollutant
concentrations were mainly affected by the heating for winter and the
control measures of the control action except for the meteorological
conditions. Therefore, we defined the following equations to evaluate the
effects of the heating and control action, respectively, based on the
atmospheric pollutant concentrations during the different stages of TECA
(i.e. NCANHP, NCAHP, CAHP, and ACA).
Pi-heating=(Ci-NCAHP-Ci-NCANHP)×100Ci-NCANHP
Pi-action=(Ci-NCAHP-Ci-CAHP)×100Ci-NCAHP
Pi-heating represents the increasing percentage (%) of
atmospheric pollutant concentration because of the combined effects of
heating for winter and meteorological conditions; Pi-action
represents the decreasing percentage (%) of atmospheric pollutant
concentration because of the combined influences of control action and
meteorological conditions; Ci-NCANHP represents the concentration
(µg m-3, CO: mg m-3) of atmospheric pollutant during the
no-control action and no-heating period; Ci-NCAHP represents the
concentration (µg m-3, CO: mg m-3) of atmospheric pollutant
during the no-control action and heating period; Ci-CAHP represents the concentration (µg m-3, CO: mg m-3) of
atmospheric pollutant during the control action and heating period.
The concentration variations in PM2.5, PM10, and gaseous
pollutants during the four stages (NCANHP, NCAHP, CAHP, and ACA) of the TECA
period in Shijiazhuang. The error bar represents the standard deviation.
The Pi-heating and Pi-action of PM2.5,
PM10,
and gaseous pollutants (SO2, NO2, CO, and O3) calculated
using
Eqs. () and () in an urban area and suburb in Shijiazhuang.
During the NCANHP, the mean concentrations of PM2.5 and PM10 were
156 and 253 µg m-3 in Shijiazhuang, respectively. With the
beginning of heating, the mean concentrations of PM2.5 and PM10
increased by 44 and
64 µg m-3 during the
NCAHP, respectively, and the PPM2.5-heating and
PPM10-heating values were up to 28 and 25 %
(Figs. 3 and 4). However, during the CAHP, the mean concentrations of
PM2.5 and PM10 were 185 and 291 µg m-3,
respectively, which decreased by 15 and
26 µg m-3 compared to the NCAHP. And
the PPM2.5-action and
PM10-action values were 8 and 8 %,
respectively. The mean height of the mixed layer and the mean wind speed and
temperature during the CAHP were lower than those during the NCAHP (Table 1).
Unfavourable meteorological conditions during the CAHP had an offset effect
on the control measures for emission sources. In view of Eq. (), it
can be seen that the positive values for
PPM2.5-action and
PPM10-action are more able to show that control
action was effective. During the ACA, the concentrations of PM2.5 and
PM10 were 227 and 383 µg m-3, respectively, which
increased significantly by 42 and 92 µg m-3 compared to the
CAHP. The variations in SO2 and NO2 concentrations during
different stages of TECA were similar to those of PM2.5 and PM10
concentrations. The PSO2-heating and
PNO2-heating values were 50 and 33 %,
respectively, and the PSO2-action and
PNO2-action values were 5 and 19 %. Note that
the mean concentration of CO in Shijiazhuang City varied from
2.2 mg m-3 during the NCANHP to 5.5 mg m-3 during the ACA
period, which showed an increasing tendency (Fig. 3). Because CO was mainly
produced from the uncompleted combustion of fossil fuels, the usage of
domestic coal might increase with the gradual decrease in temperature from
the NCANHP (8.4 ∘C) to the ACA period
(0.7 ∘C) (Table 1). Meanwhile, it can also
be inferred that the control of domestic coal during the TECA period in
Shijiazhuang City showed little efficiency. Because of the lack of emission
inventories for domestic coal or small-boiler coal in Shijiazhuang, the
control measures were less targeted. Additionally, the concentrations of
O3 during different stages of TECA were lower compared to other
pollutants (Figs. 2 and 3). Overall, the control measures of emission sources
in Shijiazhuang during the TECA period were effective, while the
coal heating for winter and the unfavourable meteorological conditions during
the CAHP had an offset effect on the efforts of control measures for
pollutant sources to some extent. The average wind speed during the CAHP
(0.4 m s-1 on average) was lower than that during the other stages of
the TECA period (0.5–0.7 m s-1 on average) (Table 1), and the wind
directions were changeable (Fig. S1), which was in favour of the accumulation
of atmospheric pollutants, thus causing the concentrations of atmospheric
pollutants to increase during the CAHP. Note that the heights of the mixed
layer showed a noticeably
decreasing tendency from the NCANHP (540 m on average) and the NCAHP (590 m
on average) to the ACA (431 m on average), and the height of the mixed layer
during the CAHP was only 474 m on average (Table 1). The decrease in the
height of the mixed layer can cause the concentrations of atmospheric
pollutants near the ground to be significantly compressed and subsequently
enhanced. In addition, during the CAHP, the multidirectional air masses that
mainly originated from Beijing–Tianjin–Hebei and its surrounding areas
(e.g. Henan, Shandong, and south of Hebei) displayed an overlap with each
other in Shijiazhuang (Fig. S7) and further aggravated the level of air pollution in Shijiazhuang.
Spatial variation
The concentration variations in PM2.5, PM10, and related gaseous
pollutants (SO2, NO2, CO, and O3) during the four stages (NCANHP,
NCAHP, CAHP, and ACA) in an urban area and suburb in Shijiazhuang are shown in
Figs. 3 and 5. During the NCANHP, the average concentrations of PM2.5
in an urban area and suburb were 166 and 152 µg m-3,
respectively. The concentrations of PM2.5 in an urban area and suburb
increased significantly during the NCAHP (t test, p < 0.01). The
meanly increased concentration of PM2.5 (46 µg m-3) in an urban
area was higher than that in the suburb (43 µg m-3), but the value
of PPM2.5-heating in the suburb (29 %) was higher than that in the urban
area (27 %) (Fig. 4). Note that the mean concentration of PM2.5 in
the urban area was up to 243 µg m-3 during the CAHP, which showed an
increasing tendency, and the PPM2.5-action value was -15 % (Fig. 4),
likely due to the unfavourable meteorological conditions such as lower wind
speed (0.4 m s-1) and lower height of the mixed layer (474 m) (Table 1 and
Fig. S7). Conversely, compared to the NCAHP, the concentrations of
PM2.5 in the suburb (a mean of 161 µg m-3) decreased significantly
during the CAHP (t test, p < 0.01), and the PPM2.5-action was up
to 18 % (Fig. 4), indicating that the control measures of PM2.5 sources
in the suburb might be more effective than in the urban area. The tendency of SO2
concentrations during different stages of TECA (except the ACA period) was
similar to that of PM2.5. The PSO2-heating and PSO2-action
values in the urban area were up to 58 and -4 %, respectively, and were
up to 47 and 8 % in the suburb during the TECA period (Fig. 4). However,
the concentrations of SO2 in the urban area and suburb decreased remarkably
during the ACA compared to the CAHP (t test, p < 0.01), probably due
to the effective control measures.
The spatial variations in atmospheric pollutants (PM2.5,
PM10, SO2, NO2, CO, and O3) during the four stages
(NCANHP, NCAHP, CAHP, and ACA) of the TECA period in Shijiazhuang. The
pictures were produced using the ArcGIS-based kriging interpolation method.
The average concentrations and percentages of chemical species in
PM2.5 in Shijiazhuang during the whole sampling period: 24 November
2015 to 9 January 2017. The error bar represents the standard deviation.
During the NCANHP, the average concentrations of PM10 in the urban area and
suburb were 280 and 242 µg m-3, respectively. Then, the meanly
increased concentrations in the urban area and suburb were up to 65 and 64 µg m-3 during the NCAHP, which were comparable with each other.
Nevertheless, the mean PPM10-heating value in the suburb (26 %) was higher
than that in the urban area (23 %) (Fig. 4). During the CAHP, the
meanly decreased concentration of PM10 in the urban area was 1 µg m-3, and noticeably lower than that of the suburb (36 µg m-3).
Furthermore, the mean PPM10-action values in the urban area and suburb were
0.4 and 12 %, respectively (Fig. 4). It can be seen that the control
of PM10 sources in the suburb was more effective compared to the urban area,
in the case of exclusion of unfavourable meteorological conditions (Table 1 and
Fig. S7), probably related to more than 700 enterprises that
mainly carried out ore mining and stone processing in the suburb that were closed down (Tables S1 and
S5). The tendency of NO2 concentrations in the urban area and suburb was
similar to that of PM10 during different stages of the TECA period. The
mean PNO2-heating values in the urban area and suburb were up to 31
and 34 %, respectively, while the mean PNO2-action values in the urban
area and suburb were up to 17 and 21 %, respectively. Note that the
concentrations of CO in the urban area and suburb showed an increasing tendency
from the NCANHP (2.1–2.4 mg m-3) to the ACA period (5.5 mg m-3)
(Fig. 3). The PCO-heating and PCO-action values in the urban area were
22 and -15 %, respectively, while those in the suburb were 32 and
-20 % during the TECA period. In addition, as shown in Fig. 5, the
concentrations of CO in the eastern and northern suburbs in Shijiazhuang were
significantly higher than those of urban areas (t test, p < 0.01).
Note that the concentrations of O3 in the urban area and suburb were lower
during different stages of TECA (Fig. 5). Overall, during the TECA period,
the effect of control measures for atmospheric pollutant sources in the suburb
was better than in the urban area, especially for the effect of control measures
for particulate matter sources. The effect of control measures for CO was
not notable during the TECA period, especially in the suburb, likely due to the
increasing usage of domestic coal in the suburb along with the
decreasing temperature
(Table 1).
Variations in chemical species in PM2.5
The average concentrations of chemical species in PM2.5 in Shijiazhuang
during the whole sampling period are shown in Fig. 6. The annual mean
concentrations of OC, SO42-, NO3-, and NH4+ in
PM2.5 were 43.1, 39.0, 33.6, and 25.6 µg m-3, respectively, and their contributions
to PM2.5 were up to 23.1, 20.0, 17.3, and 12.3 %,
respectively. The annual mean concentrations of EC and Cl- were 11.7 and 7.7 µg m-3, respectively, which accounted for
5.9 and 4.1 % of PM2.5. Note that the annual mean
concentrations of elements in PM2.5 were relatively lower, which varied
from 0.03 to 2.6 µg m-3, accounting for 0.02–2.4 % of
PM2.5. Compared to other elements, the annual mean concentrations of Si
(2.6 µg m-3) and Al (1.4 µg m-3) were relatively higher
during the whole sampling period, which accounted for 2.4 and 1.2 %
of PM2.5, respectively. In this study, the annual mean concentrations
of OC, SO42-, NO3-, and NH4+ in Shijiazhuang
were clearly higher than Beijing (Gao et al., 2016), Tianjin (Wu et al.,
2015), Jinan (Gao et al., 2011), Shanghai (H. L. Wang et al., 2016), Chengdu (Tao
et al., 2013b), Xi'an (P. Wang et al., 2015), Hangzhou (Liu et al., 2015), and
Heze (Liu et al., 2017a).
The values of Pi-heating and Pi-action of different chemical
species in PM2.5 were calculated by using Eqs. () and (). The
variations in chemical species in PM2.5 during the four stages of the TECA and
the values of Pi-heating and Pi-action in Shijiazhuang are shown
in Figs. 7 and 8. Compared to the NCANHP, the concentrations of chemical
species during the NCAHP showed a significantly increased tendency (t test,
p < 0.01); the concentrations of SO42-, Cl-, OC, EC,
Si, Al, Ca2+, and Mg2+ increased by 7.9, 3.7, 6.7, 3.2, 1.6, 0.6,
0.4, and 0.1 µg m-3, respectively, and the Pi-heating values of
these species were up to 30.0, 40.2, 14.6, 22.1, 78.8, 63.5, 47.4, and 45.9 %, respectively, during the NCAHP. As
these species (i.e. SO42-, Cl-, OC, EC, Si, Al,
Ca2+,
and Mg2+) were closely associated with coal combustion (Cao et al.,
2011; Liu et al., 2015; Liu et al., 2016; Liu et al., 2017a, b, c), coal combustion for heating in winter probably had a great impact
on increasing these chemical species in PM2.5. Furthermore, compared
to the NCANHP, the concentrations of Cr, Cu, Fe, Mn, Ti, Zn, and Pb increased
by 0.02, 0.02, 0.34, 0.02, 0.02, 0.28, and 0.07 µg m-3,
respectively, and the Pi-heating values of these species were 72.7, 33.1, 34.4, 21.0, 45.8, 48.3, and 36.2 %,
respectively, during the NCAHP. Cr, Cu, Fe, Mn, Ti, Zn, and Pb were
closely related to industrial sources (Kabala and Singh,
2001; Morishita et al., 2011; Mansha et al., 2012; Liu et al., 2015; Yao et al., 2016); thus,
the industrial emissions might have a higher influence on PM2.5 during
the NCAHP than during the NCANHP. Also, it might be closely associated
with the unfavourable meteorological factors (Table 1 and Fig. S7).
The variations in chemical species in PM2.5 during the four
stages (NCANHP, NCAHP, CAHP, and ACA) of the TECA period. The error bar
represents the standard deviation.
The Pi-heating and Pi-action of chemical species in
PM2.5 during the TECA period in Shijiazhuang.
Compared to the NCAHP, the concentrations of SO42-, Cl-,
OC,
and EC during the CAHP increased by 16.8, 0.3, 19.8, and 14.6 µg m-3, respectively, and the Pi-action values were up to
-48.8, -2.0, -37.3, and -83.0 %, respectively, during the
CAHP. As coal combustion was an important source of SO42-,
Cl-, OC, and EC (Cao et al., 2011; Liu et al., 2015; Liu et al., 2016, 2017a, b, c), it can be inferred that the influence of coal
combustion might increase noticeably during the CAHP compared to the NCAHP,
which was likely due to the increased usage of coal for domestic heating during winter (Table 1). Additionally,
unfavourable meteorological conditions during the CAHP can have an offset
effect on the control measures for coal combustion sources. As also in shown
Fig. 5,
the concentrations of CO during the CAHP were higher than those
during the NCAHP, especially in rural areas. Furthermore, OC and EC were
associated with vehicle exhaust (Liu et al., 2016, 2017a, b); thus, the effect of motor vehicle management and control measures during
the CAHP might be offset by the unfavourable meteorological conditions to
some extent during the CAHP (Table 1 and Fig. S7). However, compared to the
NCAHP, the concentrations of Si, Al, Ca2+, and Mg2+ during the CAHP
decreased by 1.1, 0.1, 0.6, and 0.1 µg m-3, respectively, and the
Pi-action values were up to 30.3, 4.5, 47.0, and
45.2 %, respectively. As Si, Al, Ca2+, and Mg2+ mainly
originated from the crustal dust (Shen et al., 2010; Liu et al., 2016; P. Wang
et al., 2015; H. N. Yang et al., 2016), the influence of crustal dust
on PM2.5 during the CAHP might decrease clearly compared to the NCAHP.
This is closely related to the control measures on dust
emission during the TECA period (as shown in Sect. 2.5). In general, in regard to the variation in PM2.5 speciation, there was no doubt that
the TECA had a certain positive environmental effect on the improvement of
air quality. However, the ambient pollutant concentration was impacted by
not only the emission sources but also the meteorological conditions,
regional background level, and distant transportation; it was understandable
that the concentration of CO had a rebound effect during the CAHP as the
height of the mixing layer was only 474 m and wind speed was low at 0.4 m s-1.
Source profiles obtained with the PMF for PM2.5. Filled bars
identify the species that mainly characterize each factor profile.
Source contributions of PM2.5 during different stages in
Shijiazhuang. WY represents the whole year: 24 November 2015 to 9 January
2017.
Variations in PM2.5 source contributions
The filter membrane samples of PM2.5 were collected at three sites (LQ,
LC, and TSMS) in Shijiazhuang from 24 November 2015 to 9 January 2017, and
source apportionment was carried out by using EPA PMF 5.0. Five
factors were also identified during the period (Figs. 9 and 10). The chemical
profile of factor 1 was mainly represented by Si (72.3 %), Ca2+
(74.0 %), Mg2+ (43.9 %), and Al (71.3 %), which were derived
mainly from crustal dust (Shen et al., 2010; P. Wang et al., 2015; Liu et al., 2016). Thus, factor 1 was viewed as crustal dust. The contribution
proportions of factor 1 to PM2.5 decreased from 19.5 % (38.5 µg m-3) during the whole year (WY), 18.7 % (42.1 µg m-3) during the
NCANHP, and 16.9 % (48.0 µg m-3) during the NCAHP to 15.0 % (40.3 µg m3)
during the CAHP, and increased up to 16.3 % (48.3 µg m-3) during the ACA. The main species of factor 2 were
SO42- (53.9 %), NO3- (89.8 %), and NH4+
(75.0 %). Therefore, factor 2 was easily identified as secondary sources (Santacatalina et al., 2010; Srimuruganandam and
Nagendra, 2012; Liu et al., 2015; Liu et al., 2016, 2017a). The contribution proportions of factor 2 to PM2.5
ranged from 29.5 % (66.4 µg m-3) during the NCANHP, 30.8 %
(87.9 µg m-3) during the NCAHP, and 31.6 % (84.8 µg m-3)
during the CAHP to 32.7 % (64.6 µg m-3) during the WY, and
decreased to 28.8 % (85.2 µg m-3) during the ACA. Factor 3 was
represented by relatively high loadings of OC (55.9 %), EC (70.9 %), Cu (26.9 %), and Zn (26.5 %). OC and EC are
generally predominant in the reported source profile of vehicle exhaust (Yao et al., 2016; Liu
et al., 2016, 2017a), Zn is widely used as an additive
for lubricant in two-stroke engines, and Cu is closely associated with brake
wear (Begum et al., 2004; Canha et al., 2012; Shafer et al., 2012; Lin et al., 2015; Liu et al.,
2017a). Therefore, factor 3 was identified as vehicle
emissions. The contribution proportions of factor 3 to PM2.5 decreased
from 14.2 % (32.0 µg m-3) during the NCANHP, 13.4 % (26.4 µg m3) during the WY, and 13.3 % (37.8 µg m-3) during the
NCAHP to 10.6 % (28.5 µg m-3) during the CAHP, and increased to
14.1 % (41.7 µg m-3) during the ACA. Factor 4 was characterized
by the high contributions of Ca2+ (26.0 %), Mg2+ (31.0 %),
Si (13.3 %), As (84.9 %), Cl- (38.6 %), OC (20.2 %), and
SO42- (26.7 %), and the combination of these species in factor
4 allowed us to infer they were co-emissions from coal combustion (Cao et al., 2011; Zhang et al., 2011; Liu
et al., 2015; Liu et al., 2016, 2017a, c). Therefore, factor 4 was
identified as coal combustion. The contribution proportions of factor 4 to
PM2.5 increased from 26.2 % (51.7 µg m-3) during the WY,
28.0 % (63.2 µg m-3) during the NCANHP, and 29.5 % (84.0 µg m-3) during the NCAHP to 31.7 % (85.2 µg m-3) during the
CAHP, and lightly increased to 32.6 % (96.3 µg m-3) during the
ACA. Factor 5 was identified as industrial emissions, with high loadings of
Cr (66.7 %), Cu (63.7 %), Fe (83.2 %), Mn (51.3 %), Ti (70.0 %), Zn (69.2 %),
Pb (42.1 %), and Cl- (41.0 %) (Morishita et al., 2011; Mansha et al.,
2012; Almeida et al., 2015; Liu et al., 2015; Liu et al., 2016; Yao et al., 2016). The contribution proportions of factor 5 to
PM2.5 ranged from 5.0 % (11.3 µg m-3) during the
NCANHP and
5.1 % (10.0 µg m-3) during the WY to 5.9 % (16.7 µg m-3) during the NCAHP,
and decreased to 5.3 % (14.2 µg m-3) during the CAHP and 4.9 % (14.4 µg m-3) during the
ACA. Note that the contribution of industrial emissions to PM2.5 was
relatively lower than other sources (Fig. 10).
In general, crustal dust, secondary sources, vehicle emissions, coal
combustion, and industrial emissions were identified as PM2.5 sources in
Shijiazhuang (Fig. 9). Compared to the WY and NCANHP, the contribution
concentrations and proportions of coal combustion to PM2.5 increased
significantly during other stages of the TECA period (Fig. 10), which was
closely associated with the coal heating for winter (Liu et al., 2016) and
the unfavourable meteorological conditions (Table 1 and Fig. S7). The
contribution concentrations and proportions of crustal dust and vehicle
emissions to PM2.5 decreased noticeably during the CAHP compared to
other stages of the TECA period (Fig. 10). This indicated that the control
effects of motor vehicles and crustal dust were remarkable during the CAHP,
even under unfavourable meteorological conditions (Table 1), and the results
were consistent with the above analysis. The contribution proportions of
secondary sources to PM2.5 during the CAHP showed little change compared
to other stages of the TECA period (Fig. 10). However, compared to the WY and
NCANHP, the contribution concentrations of secondary sources to PM2.5
increased significantly during the NCAHP, CAHP, and ACA (Fig. 10), likely due
to high concentrations of gaseous precursors (i.e. SO2 and NO2)
(Fig. 5), unfavourable meteorological conditions (Table 1), and frequent hazy
events during these periods, when there were significant secondary reactions
(Han et al., 2014; J. J. Li et al., 2016). In addition, it also illustrated
that the discharge of atmospheric pollutants might still be enormous even
under such strict control measures. Note that the contribution concentrations
and proportions of industrial emissions to PM2.5 during the CAHP
decreased noticeably compared to the NCAHP (Fig. 10), indicating that the
control of industrial emissions was also effective during the CAHP.
P. L. Chen et al. (2016) reported that the concentrations of particles during
the 2014 Youth Olympic Games (YOG) period (August) were much lower than the
before-games period (July) and after-games period (September). Furthermore,
fugitive dusts, construction dusts, and secondary sulfate aerosol decreased
obviously during the YOG, which means mitigation measures have played an
effective role in the reduction of particulate matter. Wang et al. (2017)
found that the contributions of vehicles, industrial sources, fugitive dust,
and other sources decreased 13.5–14.7, 10.7–11.2, 4.5–5.6, and
1.7–2.7 %, respectively, during the Asia-Pacific Economic
Cooperation (APEC) and the 2015 China Victory Day Parade, compared to the period before the control actions. Guo et
al. (2013) found that primary vehicle contributions were reduced by 30 %
at the urban site and 24 % at the rural site, compared with the
non-controlled period before the 2008 Beijing Olympics. The reductions in
coal combustion contributions were 57 % at the Peking University (PKU) site and 7 % at the
Yufa site.
As we can see, these control actions on the strict measures taken for
emission sources during the international events held in China, including the
TECA in Shijiazhuang, were all very important practical exercises and rarely
scientific experiments. However, they cannot be advocated as the normalized
control measures for atmospheric pollution in China. These strict measures
taken during these periods were temporary, and there was a normal recovery of
all the emissions after the operation. Once adverse weather conditions occur,
haze events may continue to happen. In short, the direct cause of the severe
atmospheric pollution in China is that the emission of pollutants is beyond
the air environment's self-purification capacity and is caused by an
unreasonable and unhealthy pattern for economic development in China.
Five clusters of the 72 h air mass backward trajectories during
the CAHP. The red star represents Shijiazhuang City.
The average concentrations of atmospheric pollutants in different
clusters during the CAHPa.
Clusters
Probability of
Atmospheric pollutants (µg m-3)
occurrence (%)
SO2
NO2
O3
CO (mg m-3)
PM10
PM2.5
1
31.3
68
88
14
3.9
358
237
2
14.2
67
78
24
3.0
290
181
3
27.3
65
69
20
2.8
232
152
4
16.5
50
58
27
2.1
189
119
5
10.8
83
104
16
4.8
451
303
a CAHP represents the control action and heating period.
Potential source areas of atmospheric pollutants obtained from the
PSCF model during the CAHP. The red star represents Shijiazhuang City. The
colours represent potential source areas influencing the atmospheric
pollutants; the red colour is relatively important
source areas (the values of WPSCF are higher) while the blue colour means unimportant potential
source areas (the values of WPSCF are lower).
Backward trajectory and PSCF analysis
The backward trajectory analysis was used to identify the transport pathways
of the air mass during the CAHP. In terms of the directions and travelled
areas, these trajectories were divided into five groups (Fig. 11).
Trajectory cluster 1, accounting for 31.3 % of the total, originated
from Shanxi Province and passed over the north of Hebei before arriving at
Shijiazhuang. Trajectory cluster 1 reflected the features of small-scale,
short-distance air mass transport (Fig. 11). The higher concentrations of
PM10 (358 µg m-3), PM2.5 (237 µg m-3), and CO
(3.9 mg m-3) might be due to the variety of emission sources and the
accumulation of pollutants from surrounding areas since the moving speed of
the
air mass in cluster 1 was much lower than other trajectories (Fig. 11 and
Table 2). Trajectory clusters 2, 3, and 4 accounted for 58.0 % of the total
trajectories, and began from the northwest of China and passed through
Inner Mongolia and Shanxi, showing the features of large-scale,
long-distance air mass transport. The relatively lower concentrations of
PM10 (189–290 µg m-3), PM2.5 (119–181 µg m-3),
SO2 (50–67 µg m-3), NO2 (58–78 µg m-3), and CO
(2.1–3.0 mg m-3) were closely associated with the high moving speed of air
mass (Fig. 11 and Table 2) and relatively less anthropogenic emission
sources in the northwest of China. Trajectory cluster 5 mainly
originated from Ningxia Province and passed over Shaanxi, Shanxi, and Hebei
before arriving at Shijiazhuang, accounting for 10.8 % of the total
trajectories, showing the features of small-scale, short-distance air mass
transport and significantly elevated levels of PM10 (451 µg m-3),
PM2.5 (303 µg m-3), SO2 (83 µg m-3), NO2 (104 µg m-3), and CO (4.8 mg m-3) with
trajectory cluster 5 possibly associated with the sources and
accumulation of pollutants from surrounding areas. As it is well known that the
Beijing–Tianjin–Hebei region is one of the most severely polluted areas in China
(Gu et al., 2011; Zhao et al., 2012; Chen et al., 2013; Bi et al., 2014; Wang et al., 2014), it might be an important reason why the concentrations
of atmospheric pollutants were higher with trajectory clusters 1 and 5 (Fig. 11 and Table 2).
In this study, the PSCF model was used to analyse the potential source areas
of atmospheric pollutants by combining backward trajectories and the
concentrations of atmospheric pollutants in Shijiazhuang during the CAHP, and
the results were shown in Fig. 12. The values of the weighted potential
source contribution function (WPSCF) of CO were higher in the north of
Shaanxi, south of Shanxi, and central and southern Inner Mongolia, which were
the main potential source areas of CO concentrations in Shijiazhuang
(Fig. 12a). The WPSCF values of NO2 were higher in the north of Henan
and Shaanxi, Hebei, Shanxi, and central and southern Inner Mongolia, which
were the main potential source areas of NO2 concentrations in
Shijiazhuang (Fig. 12b). The WPSCF values of O3 and SO2 were
higher in the north of Henan and Shaanxi, Shanxi, and the south of Hebei,
which were distinguished as major potential source areas of O3 and
SO2 concentrations in Shijiazhuang (Fig. 12c, d). Moreover, the
southwest of Shandong was also identified as the main potential source area
of SO2 concentrations in Shijiazhuang. As for PM2.5 and
PM10, the WPSCF values were higher in the south of Hebei, and east of
Shanxi, which were identified as the main potential source areas of
PM2.5 and PM10 concentrations in Shijiazhuang (Fig. 12e, f).
Overall, the potential source areas of the atmospheric pollutants in
Shijiazhuang were mainly concentrated in the surrounding regions of
Shijiazhuang, including the south of Hebei and north of Henan and Shanxi.
Previous studies also reported that Shanxi, Hebei, and Henan provinces had
serious air pollution problems (Zhu et al., 2011; Kong et al., 2013; Feng et
al., 2016; Meng et al., 2016), revealing the regional nature of the
atmospheric pollution in the North China Plain. Therefore, there is an urgent
need for making cross-boundary control policies in addition to local control
measures given the high background level of pollutants.