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International Journal of
Environmental Research
and Public Health
Article
Chemical Composition and Source Apportionment of
PM2.5 in Urban Areas of Xiangtan, Central
South China
Xiaoyao Ma 1, Zhenghui Xiao 1, *, Lizhi He 2, Zongbo Shi 3, Yunjiang Cao 1, Zhe Tian 3,4,
Tuan Vu 3and Jisong Liu 1
1School of Resource, Environment and Safety Engineering, Hunan University of Science and Technology,
Xiangtan 411201, China; maxiaoyao2002@163.com (X.M.); caoyj-xt@sohu.com (Y.C.); ljs930106@163.com (J.L.)
2Atmospheric Environment Monitoring Station of Xiangtan, Xiangtan 411100, China; alphahe125@163.com
3School of Geography, Earth and Environmental Sciences, University of Birmingham,
Birmingham B15 2TT, UK; z.shi@bham.ac.uk (Z.S.); tianzhe1129@gmail.com (Z.T.);
v.vu@bham.ac.uk (T.V.)
4Epsom Gateways, Atkins, Epsom KT18 5AL, UK
*Correspondence: xiaozhenghui2003@163.com; Tel.: +86-731-5829-0040
Received: 31 December 2018; Accepted: 11 February 2019; Published: 13 February 2019
Abstract:
Xiangtan, South China, is characterized by year-round high relative humidity and very
low wind speeds. To assess levels of PM
2.5
, daily samples were collected from 2016 to 2017 at
two urban sites. The mass concentrations of PM
2.5
were in the range of 30–217
µ
g/m
3
, with the
highest concentrations in winter and the lowest in spring. Major water-soluble ions (WSIIs) and
total carbon (TC) accounted for 58–59% and 21–24% of the PM
2.5
mass, respectively. Secondary
inorganic ions (SO
42−
, NO
3−
, and NH
4+
) dominated the WSIIs and accounted for 73% and 74% at
the two sites. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in the PM
2.5
at
the two sites were higher than 40 ng/m
3
, and decreased in the order of winter > autumn > spring.
Enrichment factor analysis indicates that Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb mainly originates from
anthropogenic sources. Source apportionment analysis showed that secondary inorganic aerosols,
vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, steel
industry are the major sources of PM
2.5
, contributing 25–27%, 21–22%, 19–21%, 16–18%, 6–9%, and
8–9% to PM2.5 mass.
Keywords:
PM
2.5
; chemical components; source apportionment; positive matrix factorization (PMF);
Xiangtan City
1. Introduction
With rapid urbanization and industrialization in China, PM
2.5
pollution has become one of the
most important atmospheric related environmental issues. Extensive studies have shown that PM
2.5
not only adversely affects air quality, visibility, and human health, but it also causes regional and
global changes in climate [
1
–
3
]. Although the Chinese government issued the National Ambient Air
Quality Standard of China (NAAQS—China) in 2012, more than 60% of megacities in the country do
not yet meet the standard [
1
]. Water-soluble inorganic ions (WSIIs), especially secondary inorganic
ions (SIAs: SO42−, NO3−, and NH4+), were major chemical components of PM2.5 across China [1].
The Beijing—Tianjin—Hebei area (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD),
and Sichuan Basin have the highest aerosol pollution levels in China, and many studies have been
conducted in these regions to understand the general characteristics of the PM
2.5
pollution and its
chemical components, formation mechanism, and sources [
4
–
8
]. In addition, multiple studies have
Int. J. Environ. Res. Public Health 2019,16, 539; doi:10.3390/ijerph16040539 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019,16, 539 2 of 16
investigated the characteristics of atmospheric PM
2.5
during episodes of fog and haze in several
megacities in China [9–11].
The city of Xiangtan, Hunan Province, is in the central southern region of China. The area is an
intermountain basin within a subtropical climate zone and encircled by low and medium height hills
with higher elevations to the north, west, and south (Figure 1). These features produce a year-round
consistently high relative humidity and very low wind speeds.
Int. J. Environ. Res. Public Health 2019, 16, x 2 of 17
chemical components, formation mechanism, and sources [4–8]. In addition, multiple studies have
investigated the characteristics of atmospheric PM
2.5
during episodes of fog and haze in several
megacities in China [9–11].
The city of Xiangtan, Hunan Province, is in the central southern region of China. The area is an
intermountain basin within a subtropical climate zone and encircled by low and medium height hills
with higher elevations to the north, west, and south (Figure 1). These features produce a year-round
consistently high relative humidity and very low wind speeds.
Figure 1. Location map of Xiangtan City (a) and satellite image showing the two sampling sites (b).
Xiangtan plays an important role in the industrial base of Hunan Province and China as a
whole, and although some enterprises have closed owing to industrial restructuring, several large
enterprises (e.g., Xiangtan Iron and Steel Group Co. Ltd. and Datang Xiangtan Power Generation
Co. Ltd.) remain in the urban area [12]. These source almost all of their energy needs from coal.
The chemical composition and source apportionment of PM
2.5
aerosols under intermountain
topographical and meteorological conditions should be of great interest to researchers; however,
they have received little attention to date [12–14]. The results of the studies that have been conducted
show that atmospheric PM
2.5
pollution in Xiangtan City is relatively serious, especially in the winter
[12,13]. Zhang et al. [13] showed that the volume of atmospheric particles and some heavy metals
(e.g., Cd, Pb, and As) in winter exceed the national standard. Wang et al. [14] studied the regional
distribution characteristics of polycyclic aromatic hydrocarbons (PAHs) in the PM
2.5
to assess their
risk to health. Tang et al. [12] studied the chemical compositions of the source apportionment of
PM
2.5
from Chang-Zhu-Tan city clusters (i.e., Changsha, Xiangtan, and Zhuzhou) and found that the
mass concentrations of PM
2.5
collected from September 2013 to August 2014 exhibited distinct
regional differences (with the highest in Changsha and the lowest in Xiangtan) and seasonal
variations (winter > autumn > spring > summer). However, according to the newest monitoring data
(available from http://www.cnemc.cn), the daily mass concentrations are now higher in Xiangtan
than in Changsha owing to the addition of PM
2.5
pollution controls by local governments over the
last five years. Based on this data, it is essential that the chemical composition and source
apportionment of Xiangtan PM
2.5
be studied further.
In this study, daily PM
2.5
samples were collected simultaneously at two urban sites in the
spring, autumn, and winter of 2016–2017, and various chemical components, including major
water-soluble inorganic ions (WSIIs), carbonaceous species (i.e., OC (organic carbon) and EC
(elemental carbon)), and metal elements were analyzed. The main objective was to characterize the
seasonal and site differences of the PM
2.5
chemical components to identify the major sources of PM
2.5
particles and quantify their contributions.
Figure 1. Location map of Xiangtan City (a) and satellite image showing the two sampling sites (b).
Xiangtan plays an important role in the industrial base of Hunan Province and China as a whole,
and although some enterprises have closed owing to industrial restructuring, several large enterprises
(e.g., Xiangtan Iron and Steel Group Co. Ltd. and Datang Xiangtan Power Generation Co. Ltd.) remain
in the urban area [12]. These source almost all of their energy needs from coal.
The chemical composition and source apportionment of PM
2.5
aerosols under intermountain
topographical and meteorological conditions should be of great interest to researchers; however, they
have received little attention to date [
12
–
14
]. The results of the studies that have been conducted
show that atmospheric PM
2.5
pollution in Xiangtan City is relatively serious, especially in the
winter [12,13]
. Zhang et al. [
13
] showed that the volume of atmospheric particles and some heavy
metals (e.g., Cd, Pb, and As) in winter exceed the national standard. Wang et al. [
14
] studied the
regional distribution characteristics of polycyclic aromatic hydrocarbons (PAHs) in the PM
2.5
to assess
their risk to health. Tang et al. [
12
] studied the chemical compositions of the source apportionment
of PM
2.5
from Chang-Zhu-Tan city clusters (i.e., Changsha, Xiangtan, and Zhuzhou) and found that
the mass concentrations of PM
2.5
collected from September 2013 to August 2014 exhibited distinct
regional differences (with the highest in Changsha and the lowest in Xiangtan) and seasonal variations
(winter > autumn > spring > summer). However, according to the newest monitoring data (available
from http://www.cnemc.cn), the daily mass concentrations are now higher in Xiangtan than in
Changsha owing to the addition of PM
2.5
pollution controls by local governments over the last five
years. Based on this data, it is essential that the chemical composition and source apportionment of
Xiangtan PM2.5 be studied further.
In this study, daily PM
2.5
samples were collected simultaneously at two urban sites in the spring,
autumn, and winter of 2016–2017, and various chemical components, including major water-soluble
inorganic ions (WSIIs), carbonaceous species (i.e., OC (organic carbon) and EC (elemental carbon)),
and metal elements were analyzed. The main objective was to characterize the seasonal and site
differences of the PM
2.5
chemical components to identify the major sources of PM
2.5
particles and
quantify their contributions.
Int. J. Environ. Res. Public Health 2019,16, 539 3 of 16
2. Materials and Methods
2.1. Sampling Sites
The urban area of Xiangtan is divided by the Xiangjiang River, with the Yuetang and Yuhu Districts
located to the east and west of the river, respectively (Figure 1). Daily PM
2.5
samples were collected
simultaneously at two urban sites, denoted YT and KD: the rooftop of the First Teaching Building
on the South Campus of the Hunan Institute of Engineering in the Yuetang District (YT, 112
◦
55
0
E,
27
◦
48
0
N) and the rooftop of the Civil Engineering Building at the Hunan University of Science and
Technology in the Yuhu District (KD, 112
◦
55
0
E, 27
◦
54
0
N). Samples were collected at sampling heights
of approximately 18 and 20 m above the ground. The YT sampling site was chosen because of its
close proximity to industrial, residential, and high-traffic areas of Xiangtan, including the locations
of several important enterprises (e.g., Xiangtan Iron and Steel Group Co. Ltd.). In contrast, there is
no significant industrial activity in the vicinity of the KD sampling site although nearby construction
activities were ongoing.
2.2. Sample Collection
Daily (23 h) integrated PM
2.5
samples were collected in three seasons: spring (28 April to 25 May
2016), autumn (12 September to 22 October 2016), and winter (2 December 2016 to 15 January 2017).
Summer PM
2.5
samples were not collected because of unusual and non-representative conditions. In
order to be a ‘national civilized city’, in the summer of 2017 almost all of the buildings and roads in
urban Xiangtan were renovated, and some large coal-burning enterprises had to reduce their emissions.
At both sampling sites, PM
2.5
samples were collected in parallel on quartz fiber filters (Whatman Inc.,
90 mm, Piscataway, NJ, USA) to capture carbonaceous components and WSIIs, and polypropylene
fiber filters (Whatman Inc., 90 mm) to capture mass and trace elements. PM
2.5
sampling was conducted
using two medium-volume samplers (TH-150C, Wuhan Tianhong Ltd., Wuhan, China) with a flow
rate of 100 L/min.
Before sampling, the quartz filters were preheated to 550
◦
C for 4 h to remove any organic
compounds. Before and after each sampling period, the bank and sample filters were equilibrated at a
constant temperature (25
◦
C
±
1
◦
C) and relative humidity (40%
±
5%) for 48 h. During the sampling
period, field blank filters were also collected by exposing the filters in the sampler without drawing
air through them to account for any artefacts introduced during the sampling procedure. Once the
collection period was complete, polypropylene fiber filters were immediately covered by tin paper
and quartz fiber filters were stored in pre-baked aluminium foil and frozen at −18 ◦C until analyzed.
Meteorological parameters, including relative humidity (RH), wind speed (WS), temperature, and
concentrations of SO
2
, NO
2
, CO, and O
3
, were measured hourly by co-located air quality monitoring
stations operated by the Ministry of Environmental Protection in China.
2.3. Chemical Analysis
2.3.1. Ions
Water-soluble ion concentrations were determined using an ion chromatography (IC) system
(Dionex model ICS-3000, USA) as described by Xu et al. [
15
]. A quarter of each filter was cut into pieces
and placed in 10 mL of ultrapure water (resistivity = 18.25 MΩ·cm) for 30 min to create an extraction
solution, which was then filtered using a 0.22-
µ
m pore syringe filter (Dionex Corp., Sunnyvale, CA,
USA). The anion (F
−
, Cl
−
, SO
42−
, and NO
3−
) concentrations were measured using an AS11-HC
column (4
×
250 mm) with 30 mM KOH, while cation (NH
4+
, Na
+
, K
+
, Mg
2+
, and Ca
2+
) concentrations
were determined using an Ion Pac CS12A column (4
×
250 mm) with 20 mM methane sulfonic acid
as an eluent at a flow rate of 1.0 mL/min. Before conducting targeted sample analysis, a standard
solution and blank test were performed; the correlation coefficient of the standard samples was more
Int. J. Environ. Res. Public Health 2019,16, 539 4 of 16
than 0.999. The detection limits were all lower than 0.03 mg/L. The recovery rates of the ions were in
the range of 80–120%. All reported ion concentrations were corrected using field blanks.
2.3.2. Carbon
The concentrations of organic carbon (OC) and elemental carbon (EC) on the quartz filters were
measured using a Sunset Carbon Aerosol analyser (Sunset Laboratory Inc., Tigard, OR, USA) as
described by Zhang et al. [
16
]. The organic carbon in the filter membranes was catalysed by manganese
dioxide under the condition of no oxygen at 580
◦
C. Under the action of pyrolysis and fission, the
carbonaceous combustion products were converted into carbon dioxide and then into an He/O
x
mixture. Under aerobic conditions at 840
◦
C, methane gas was used as an internal standard during
the entire measurement process to calibrate the FID response signal. A sucrose solution was used for
external calibration to ensure sufficient measurement accuracy. All reported carbonaceous species
concentrations were corrected using field blanks.
2.3.3. Metals
The particles collected on the polypropylene fiber filters were analyzed for metal elements (i.e., Mg,
Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Tl, and Pb) via an inductively coupled
plasma-mass spectrometer (ICP-MS; XSeries 2, Thermo Fisher, Waltham, MA, USA) as described
by Liu et al. [
17
]. A quarter of the filters were digested in a high-pressure Teflon digestion vessel
with a mixture of ultra-high purity acids (15 mL of HNO
3
and 5 mL of HClO
4
) before being heated
in a microwave system. The temperature of the microwave system was increased to 200
◦
C and
maintained at that level for 30 min. Quality assurance and control were ensured through the analysis
of certified reference material SRM 1649a (urban particulate matter); the standard reference material
was pre-treated and analyzed using the same procedure. The resulting recoveries fell within
±
10%
of the certified values for most elements, except for Se, As, and Sb (
±
15%). All of the reported metal
element concentrations were corrected using field blanks.
2.4. PMF Model
Positive matrix factorization (PMF), which was developed by Paatero and Tapper (1994) [
18
],
is a receptor model that has been used to successfully identify the potential sources and source
contributions without a priori knowledge of the profile of the local sources [
5
,
12
,
18
,
19
]. The PMF
process for source apportionment of aerosol particles has been described in detail by many previous
studies [18–21].
3. Results and Discussion
3.1. Mass Concentration of PM2.5
The concentrations of the PM
2.5
, O
3
, SO
2
, and NO
2
, and the meteorological data obtained during
the sampling periods are shown in Figure 2and Table 1. The mass concentrations of the PM
2.5
during
the sampling periods varied greatly within the range of 30–217
µ
g/m
3
. The seasonal variations in
PM
2.5
concentrations were significant and the mean concentrations were 93.4
±
38.5, 82.2
±
30.9, and
61.7
±
18.5
µ
g/m
3
at YT, and 95.6
±
33.5, 68.2
±
20.2, and 56.9
±
19.3
µ
g/m
3
at KD in winter, autumn,
and spring, respectively. The spatial variations in the PM
2.5
concentrations were not significant, which
may indicate a similar regional pollution pattern for PM
2.5
in Xiangtan. This suggests that a significant
fraction of PM
2.5
may consist of secondary particles [
22
,
23
]. In general, the concentrations of PM
2.5
were lower than those of the Beijing—Tianjin—Hebei area (BTH) [
6
,
10
,
24
–
26
], Sichuan Basin [
1
,
4
], and
Lanzhou [15], but were higher than Fuzhou [15] and Shenzhen [27].
As shown in Figure 2and Table 1, the highest mass concentration of PM
2.5
in winter was largely
related to the combined effects of increased emissions (e.g., coal combustion for residential heating),
confirmed by higher concentrations of SO
2
and NO
2
(Table 1), and unfavorable atmospheric diffusion
Int. J. Environ. Res. Public Health 2019,16, 539 5 of 16
conditions (i.e., low wind speeds and frequent temperature inversions). The low concentrations of
PM
2.5
in spring were mainly attributed to greater precipitation and more windy days, diluting and
scavenging pollutants.
Table 1.
Average concentrations of SO
2
, NO
2
, and O
3
in the atmosphere and average temperature,
relative humidity, and wind speed during the sampling periods.
Pollutant and
Meteor. Parameters
YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
O3134.4 ±10.6 129.4 ±55.8 62.6 ±30.4 108.8 ±32.3 129.5 ±32.5 126.0 ±10.9 58.4 ±24.8 104.6 ±22.7
SO227.6 ±13.5 31.0 ±10.6 33.3 ±13.8 30.6 ±12.6 26.1 ±12.7 30.7 ±14.5 33.6 ±12.2 30.1 ±13.1
NO237.6 ±7.1 41.1 ±13.4 52.4 ±19.9 43.7 ±13.5 36.5 ±12.5 39.7 ±12.9 59.2 ±18.1 45.1 ±15.2
T (◦C) 20.8 ±1.1 22.1 ±6.0 9.5 ±3.7 17.4 ±3.6 20.6 ±1.2 22.1 ±6.6 9.1 ±2.3 17.3 ±3.4
RH (%) 83.8 ±3.1 78.5 ±10.2 79.3 ±12.3 80.5 ±8.5 83.0 ±4.3 78.7 ±10.7 80.4 ±10.2 80.7 ±98.4
WS (m/s) 1.4 ±0.3 1.9 ±0.8 1.9 ±0.9 1.7 ±0.7 1.7 ±0.5 2.1 ±0.8 1.8 ±0.9 1.9 ±0.7
T: average temperature; RH: average relative humidity; WS: average wind speed.
Int. J. Environ. Res. Public Health 2019, 16, x 5 of 17
Table 1. Average concentrations of SO2, NO2, and O3 in the atmosphere and average temperature,
relative humidity, and wind speed during the sampling periods
Pollutant
and meteor.
Parameters
YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
O3 134.4 ± 10.6 129.4 ± 55.8 62.6 ± 30.4 108.8 ± 32.3 129.5 ± 32.5 126.0 ± 10.9 58.4 ± 24.8 104.6 ± 22.7
SO2 27.6 ± 13.5 31.0 ±10.6 33.3 ± 13.8 30.6 ± 12.6 26.1 ± 12.7 30.7 ± 14.5 33.6 ± 12.2 30.1 ± 13.1
NO2 37.6 ± 7.1 41.1 ± 13.4 52.4 ± 19.9 43.7 ± 13.5 36.5 ± 12.5 39.7 ± 12.9 59.2 ± 18.1 45.1 ± 15.2
T (°C) 20.8 ± 1.1 22.1 ± 6.0 9.5 ± 3.7 17.4 ± 3.6 20.6 ± 1.2 22.1 ± 6.6 9.1 ± 2.3 17.3 ± 3.4
RH (%) 83.8 ± 3.1 78.5 ± 10.2 79.3 ± 12.3 80.5 ± 8.5 83.0 ± 4.3 78.7 ± 10.7 80.4 ± 10.2 80.7 ± 98.4
WS (m/s) 1.4 ± 0.3 1.9 ± 0.8 1.9 ± 0.9 1.7 ± 0.
7
1.7 ± 0.5 2.1 ± 0.8 1.8 ± 0.9 1.9 ± 0.
7
T: average temperature; RH: average relative humidity; WS: average wind speed.
0
50
100
150
200
0
20
40
60
80
0
30
60
90
120
28-Apr
1-May
4-May
7-May
10-May
13-May
16-May
19-May
22-May
25-May
12-Sep
15-Sep
18-Sep
21-Sep
24-Sep
27-Sep
30-Sep
3-Oct
6-Oct
9-Oct
12-Oct
15-Oct
18-Oct
22-Oct
2-Dec
5-Dec
8-Dec
11-Dec
14-Dec
17-Dec
20-Dec
23-Dec
26-Dec
29-Dec
1-Jan
4-Jan
7-Jan
10-Jan
13-Jan
15-Jan
--
5
10
15
20
25
30
50
60
70
80
90
100
0
1
2
3
4
5
6
0
50
100
150
200
250
YT
PM
2.5
(μg/m
3
)
(a)
(c)
SO
2
(μg/m
3
)
(d)
NO2
(μg/m
3
)
(g)
Temp.
(℃)
Spring Autumn Winter
(f)
RH
(%)
(e)
WS
(m/s)
O
3
(μg/m
3
)
(b)
KD
Figure 2. Concentrations of PM2.5, O3, SO2, and NO2, and meteorological data during the sampling
periods: (a) PM2.5; (b) O3; (c) SO2; (d) NO2; (e) wind speed; (f) relative humidity; and (g) ambient
temperature.
3.2. Chemical Compositions of PM2.5
3.2.1. WSIIs
The mass concentrations of major WSIIs, their contribution to the PM2.5 concentration, and the
sulphur (SOR) and nitrogen oxidation ratios (NOR) are shown in Table 2. The respective average
concentrations of the total WSIIs were 44.6 ± 14.7 and 40.9 ± 12.6µg/m3 at YT and KD, accounting for
59.2% and 57.7% of the PM2.5 mass, respectively. The concentrations of the WSIIs were dominated by
SO42−, NO3−, and NH4+, followed by Cl—, with respective mean concentrations of 15.9 ± 4.7, 10.6 ± 6.1,
6.5 ± 2.7, 2.5 ± 1.0 µg/m3 at YT and 14.4 ± 4.3, 9.6 ± 5.0, 5.6 ± 2.7, 5.4 ± 1.5, 2.4 ± 1.0, and 1.2 ± 0.4 µg/m3
at KD. Secondary inorganic ions (SIAs: SO42−, NO3−, and NH4+) accounted for 73.0% and 73.5% of the
WSIIs at the two sites. From Table 2 and Figure 3, the mean concentrations of the total WSIIs
Figure 2.
Concentrations of PM
2.5
, O
3
, SO
2
, and NO
2
, and meteorological data during the sampling periods:
(a) PM2.5; (b) O3; (c) SO2; (d) NO2; (e) wind speed; (f) relative humidity; and (g) ambient temperature.
3.2. Chemical Compositions of PM2.5
3.2.1. WSIIs
The mass concentrations of major WSIIs, their contribution to the PM
2.5
concentration, and the
sulphur (SOR) and nitrogen oxidation ratios (NOR) are shown in Table 2. The respective average
concentrations of the total WSIIs were 44.6
±
14.7 and 40.9
±
12.6
µ
g/m
3
at YT and KD, accounting for
59.2% and 57.7% of the PM
2.5
mass, respectively. The concentrations of the WSIIs were dominated
by SO
42−
, NO
3−
, and NH
4+
, followed by Cl
−
, with respective mean concentrations of
15.9 ±4.7
,
10.6 ±6.1
, 6.5
±
2.7, 2.5
±
1.0
µ
g/m
3
at YT and 14.4
±
4.3, 9.6
±
5.0, 5.6
±
2.7, 5.4
±
1.5, 2.4
±
1.0,
and 1.2
±
0.4
µ
g/m
3
at KD. Secondary inorganic ions (SIAs: SO
42−
, NO
3−
, and NH
4+
) accounted for
73.0% and 73.5% of the WSIIs at the two sites. From Table 2and Figure 3, the mean concentrations
of the total WSIIs exhibited distinctly seasonal variations and were highest in spring and lowest in
Int. J. Environ. Res. Public Health 2019,16, 539 6 of 16
winter. The concentrations of WSIIs were comparable with those from Chengdu and Chongqing [
1
,
4
],
but higher than those in Lanzhou [
25
]. The concentrations of WSIIs and SIAs were lower than those in
North China (e.g., Beijing—Tianjin—Hebei) and some cities (e.g., Nanjing and Heze) of East China,
whereas the opposite was true for the corresponding percentage contribution to PM
2.5
[
10
,
19
,
20
,
26
,
28
].
SOR and NOR can be used to evaluate the extent of the atmospheric conversion of SO
2
to SO
42−
and NO2to NO3−as [24]
SOR = n(SO42−) / [n(SO42−) + n(SO2)], (1)
NOR = n(NO3
−) / [n(NO3
−) + n(NO2)]. (2)
As shown in Table 2, the SORs and NORs at the two sites during the sampling periods were all
above 0.1, indicating that sulfate and nitrate were mainly produced by secondary transformation of
SO
2
and NO
2
in the atmosphere [
10
,
29
]. Both the SOR and concentrations of particulate sulphate were
highest in spring and lowest in winter at both sites, while the NOR and concentrations of particulate
nitrates were highest in winter and lowest in spring.
Sulphate concentrations showed a poor correlation with relative humidity (Figure 4a,b),
temperature (Figure 4c,d) and O
3
concentrations (Figure 4e,f). The concentrations of nitrates are
not correlated with relative humidity (Figure 4a,b) and O3concentrations (Figure 4c,d) but decreases
with increasing temperature. One of the possible reasons is that nitrate aerosol is less volatile at
lower temperature [
4
,
19
]. The impact of meteorological conditions on sulfate and nitrate aerosol
concentration is highly complex and the low time resolution of the samples (23 h average) makes it
difficult to disentangle this effect.
Table 2.
Major water-soluble ion concentrations (
µ
g/m
3
) and the corresponding contribution to PM
2.5
(%), sulphur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) in PM
2.5
at the YT and KD sites.
Ions YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
PM2.5 (µg/m3)61.7 ±18.5 82.2 ±30.9 93.4 ±38.5 79.1 ±29.3 56.9 ±19.3 68.2 ±20.2 95.6 ±33.5 73.6 ±24.3
Cl−(µg/m3)1.6 ±0.6 2.1 ±0.8 3.8 ±1.6 2.5 ±1.0 1.8 ±0.8 1.6 ±0.7 3.7 ±1.6 2.4 ±1.0
NO3−(µg/m3)8.0 ±3.9 9.0 ±6.3 14.9 ±8.3 10.6 ±6.1 6.3 ±4.4 7.9 ±4.5 14.6 ±6.0 9.6 ±5.0
SO42−(µg/m3)17.2 ±4.4 15.6 ±4.2 14.7 ±5.4 15.9 ±4.7 15.1 ±4.3 14.1 ±4.3 13.9 ±4.4 14.4 ±4.3
NH4+(µg/m3)6.9 ±2.4 5.5 ±2.0 7.1 ±3.7 6.5 ±2.7 4.5 ±2.6 5.1 ±2.4 7.1 ±3.3 5.6 ±2.7
Cl−/PM2.5 (%) 2.6 ±0.9 2.7 ±1.0 4.3 ±1.6 3.2 ±1.3 2.1 ±0.9 1.8 ±0.8 4.3 ±1.8 2.8 ±1.2
NO3−/PM2.5 (%) 12.4 ±2.8 11.0 ±6.6 16.0 ±5.9 13.2 ±5.1 9.5 ±5.11 9.3 ±5.2 17.1 ±7.1 12.0 ±5.8
SO42−/PM2.5 (%) 28.2 ±2.1 20.3 ±5.1 16.5 ±4.1 21.7 ±3.8 17.7 ±5.0 16.6 ±5.0 16.3 ±5.1 16.9 ±5.0
NH4+/PM2.5 (%) 11.1 ±1.2 6.9 ±2.1 7.9 ±3.5 8.6 ±2.6 5.3 ±3.1 6.0 ±2.8 8.3 ±3.8 6.5 ±3.2
SIAs 32.1 ±10.7 30.1 ±10.0 36.6 ±16.3 32.9 ±12.3 27.7 ±10.5 27.2 ±9.1 35.6 ±12.4 30.2 ±10.6
WSIIs 42.3 ±12.0 41.2 ±11.9 50.3 ±20.2 44.6 ±14.7 36.3 ±11.2 36.1 ±10.0 50.3 ±16.6 40.9 ±12.6
SIAs/WSIIs (%) 75.1 ±4.4 72.3 ±5.7 71.5 ±8.5 73.0 ±6.2 75.3 ±5.7 74.7 ±4.0 70.6 ±6.4 73.5 ±5.4
WSIIs/PM2.5 (%) 68.9 ±4.6 52.7 ±12.2 56.0 ±12.4 59.2 ±9.8 64.7 ±9.7 53.8 ±7.7 54.7 ±13.8 57.7 ±10.4
SIAs/PM2.5 (%) 51.7 ±4.5 38.2 ±9.4 40.4 ±10.8 43.5 ±8.3 49.0 ±9.9 40.2 ±6.1 38.5 ±9.6 42.6 ±8.5
NO3−/SO42−0.4 ±0.1 0.6 ±0.4 1.0 ±0.5 0.7 ±0.3 0.5 ±0.2 0.6 ±0.4 1.1 ±0.3 0.7 ±0.3
SOR 0.3 ±0.1 0.2 ±0.1 0.2 ±0.1 0.3 ±0.1 0.3 ±0.1 0.3 ±0.1 0.3 ±0.1 0.3 ±0.1
NOR 0.1 ±0.0 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1
It has been reported that the mass ratio of nitrate/sulphate can be used to evaluate the relative
contributions of mobile and stationary sources in the atmosphere [
15
,
30
]. On average, the mass ratios
of NO
3−
/SO
42−
at the two sites were less than one, especially in spring and autumn, which indicates
that stationary sources make a greater contribution to aerosol pollution than vehicle exhaust [
15
,
30
].
It should be noted that the mass ratios of NO
3−
/SO
42−
at the two sites increased greatly from
spring/autumn to winter (i.e., they were greater than one), which suggests that vehicle exhaust may
have a greater contribution to PM
2.5
in the winter. In addition, high atmospheric conversion of NO
2
to
NO
3−
and a low atmospheric conversion of SO
2
to SO
42−
may have contributed to the relatively high
NO3−/SO42−values in the winter.
Int. J. Environ. Res. Public Health 2019,16, 539 7 of 16
Int. J. Environ. Res. Public Health 2019, 16, x 7 of 17
0369121518
0
3
6
9
12
15
18
SO
4
2-
NO
3
-
GM
GM
NH
4
+
NH
4
+
EC
Cl
-
Cl
-
OC
SO
4
2-
NO
3
-
NH
4
+
GM
EC
Cl
-
K
+
K
+
K
+
KD concentrations(μg/m
3
)
K
+
Cl
-
EC
GM
NH
4
+
OC
NO
3
-
SO
4
2-
(a) Spring Line 1:1
0369121518
0
3
6
9
12
15
18
Line 1:1
(b) Autumn
0369121518
0
3
6
9
12
15
18
Line 1:1
(c) Winter
NO
3
-
SO
4
2-
OC
EC
KD concentrations(μg/m
3
)
YT concentrations(μg/m
3
)
0369121518
0
3
6
9
12
15
18
Line 1:1
(d) Average
OC
YT concentrations(μg/m
3
)
Figure 3. Seasonal mean concentrations of the major components of PM2.5 in (a) spring, (b) autumn,
(c) winter, and (d) their average concentrations during the sampling periods at the YT and KD sites.
It has been reported that the mass ratio of nitrate/sulphate can be used to evaluate the relative
contributions of mobile and stationary sources in the atmosphere [15,30]. On average, the mass
ratios of NO3−/SO42− at the two sites were less than one, especially in spring and autumn, which
indicates that stationary sources make a greater contribution to aerosol pollution than vehicle
exhaust [15,30]. It should be noted that the mass ratios of NO3−/SO42− at the two sites increased
greatly from spring/autumn to winter (i.e., they were greater than one), which suggests that vehicle
exhaust may have a greater contribution to PM2.5 in the winter. In addition, high atmospheric
conversion of NO2 to NO3− and a low atmospheric conversion of SO2 to SO42− may have contributed
to the relatively high NO3−/SO42− values in the winter.
3.2.2. Carbonaceous Species
The average mass concentrations of carbonaceous species at the two sites during the sampling
periods were 13.4 ± 6.1 and 6.0 ± 1.8 µg/m3 at YT and 11.2 ± 5.2 and 4.8 ± 1.6 µg/m3 at KD (Table 3).
The average total carbon (EC + OC) at YT accounted for 24.3% of the PM2.5 mass concentration
during the sampling period, which was slightly higher than that at KD (21.1%). In addition, the OC
concentration at the two sites during the sampling period exhibited strong seasonal variation, being
highest in winter and lowest in spring. The concentrations of EC were higher in autumn and winter
and lower in spring. Compared with OC, EC concentration exhibited less seasonal variability at the
two sites, indicating a fairly uniform local source (e.g., primary particles from incomplete fossil fuel
combustion). The OC and EC concentrations at Xiangtan were lower than those in
Beijing—Tianjin—Hebei [7,24,26,28], Nanjing, and Heze [19,20], but higher than those in Chengdu
and Chongqing [4].
Previous studies have shown that the OC/EC ratio is a potential indicator of the relative
contributions of primary (POC) and secondary organic aerosols (SOC) [19,31]. Secondary organic
carbon (SOC) is estimated as
SOC = (OC) −EC×(
OC
EC) (3)
Figure 3.
Seasonal mean concentrations of the major components of PM
2.5
in (
a
) spring, (
b
) autumn,
(c) winter, and (d) their average concentrations during the sampling periods at the YT and KD sites.
3.2.2. Carbonaceous Species
The average mass concentrations of carbonaceous species at the two sites during the sampling
periods were 13.4
±
6.1 and 6.0
±
1.8
µ
g/m
3
at YT and 11.2
±
5.2 and 4.8
±
1.6
µ
g/m
3
at KD (Table 3).
The average total carbon (EC + OC) at YT accounted for 24.3% of the PM
2.5
mass concentration
during the sampling period, which was slightly higher than that at KD (21.1%). In addition, the OC
concentration at the two sites during the sampling period exhibited strong seasonal variation, being
highest in winter and lowest in spring. The concentrations of EC were higher in autumn and
winter and lower in spring. Compared with OC, EC concentration exhibited less seasonal variability
at the two sites, indicating a fairly uniform local source (e.g., primary particles from incomplete
fossil fuel combustion). The OC and EC concentrations at Xiangtan were lower than those in
Beijing—Tianjin—Hebei [
7
,
24
,
26
,
28
], Nanjing, and Heze [
19
,
20
], but higher than those in Chengdu and
Chongqing [4].
Previous studies have shown that the OC/EC ratio is a potential indicator of the relative
contributions of primary (POC) and secondary organic aerosols (SOC) [
19
,
31
]. Secondary organic
carbon (SOC) is estimated as
SOC =(OC)tot −EC ×OC
EC prim
(3)
where SOC (
µ
g/m
3
) is the concentration of SOC, OC
tot
is the total OC, and OC (
µ
g/m
3
) and EC (
µ
g/m
3
)
are the concentrations of OC and EC. As reported by Wang et al. [
4
], (OC/EC)
min
was simplified
as (OC/EC)
prim
to estimate SOC in this study. The estimated SOC was only an approximation;
uncertainties mainly arise from the influence of biomass burning [
4
]. The results of this calculation are
shown in Table 3. The OC/EC ratios in autumn and winter were all above 2.0. The seasonal patterns of
SOC were observed to be similar to those of the PM
2.5
and decreased in the order of winter > autumn
> spring. The higher concentration of SOC in the winter is likely to be related to low temperature,
favouring the condensation of semi-volatile organic aerosols [4,32].
Int. J. Environ. Res. Public Health 2019,16, 539 8 of 16
Int. J. Environ. Res. Public Health 2019, 16, x 8 of 17
50-60 60-70 70-80 80-90 90-100
0
5
10
15
20
25
Concentration(μg/m
3
)
Concentration(μg/m
3
)
RH(%)
Nitrate Sulfate Ammonium
(a) YT
50-60 60-70 70-80 80-90 90-100
0
5
10
15
20
25 Nitrate Sulfate Ammonium
RH(%)
(b) KD
0-10 10-15 15-20 20-25 25-35
0
5
10
15
20
25 Nitrate Sulfate Ammonium
Concentrations(μg/m
3
)
Tem
p
erature
(
℃
)
(b) YT
0-10 10-15 15-20 20-25 25-35
0
5
10
15
20
25 Nitrate Sulfate Ammonium
Concentrations(μg/m
3
)
Tem
p
erature
(
℃
)
(d) KD
0-50 50-100 100-150 150-200 200-250
0
5
10
15
20
25 (e) YT
Concentrations(μg/m
3
)
O
3
Concentrations (μg/m
3
)
0-50 50-100 100-150 150-200 200-250
0
5
10
15
20
25 (f) KD Nitrate Sulfate Ammonium
Nitrate Sulfate Ammonium
Concentrations(μg/m
3
)
O
3
Concentrations (μg/m
3
)
Figure 4. Comparisons of ions concentration in PM2.5 at the YT and KD sites for different ranges of
meteorological factors: (a) and (b) relative humidity; (c) and (d) temperature; (e) and (f) O3
concentration.
where SOC (µg/m3) is the concentration of SOC, OCtot is the total OC, and OC (µg/m3) and EC
(µg/m3) are the concentrations of OC and EC. As reported by Wang et al. [4], (OC/EC)min was
simplified as (OC/EC)prim to estimate SOC in this study. The estimated SOC was only an
approximation; uncertainties mainly arise from the influence of biomass burning [4]. The results of
this calculation are shown in Table 3. The OC/EC ratios in autumn and winter were all above 2.0. The
seasonal patterns of SOC were observed to be similar to those of the PM2.5 and decreased in the order
of winter > autumn > spring. The higher concentration of SOC in the winter is likely to be related to
low temperature, favouring the condensation of semi-volatile organic aerosols [4,32].
Table 3. Seasonal distribution of carbonaceous species over the three seasons at the YT and KD sites
Carbonaceous YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
OC (µg/m3) 7.7 ± 1.4 16.2 ± 7.6 16.4 ± 9.3 13.4 ± 6.1 4.6 ± 2.1 13.0 ± 4.2 16.1 ± 9.1 11.2 ± 5.2
EC (µg/m3) 4.9 ± 0.8 6.7 ± 2.2 6.4 ± 2.3 6.0 ± 1.8 3.4 ± 1.3 5.0 ± 1.4 5.9 ± 2.2 4.8 ± 1.6
OC/EC 1.6 ± 0.2 2.4 ± 0.7 2.5 ± 1.0 2.1 ± 0.6 1.3 ± 0.2 2.7 ± 0.8 2.7 ± 1.2 2.2 ± 0.8
POC (µg/m3) 4.3 ± 0.7 5.9 ± 1.9 5.6 ± 2.0 5.3 ± 1.5 3.5 ± 1.3 5.2 ± 1.4 6.1 ± 2.2 4.9 ± 1.7
SOC (µg/m3) 3.4 ± 1.0 10.3 ± 6.0 10.8 ± 7.8 8.2 ± 4.9 1.1 ± 0.9 7.8 ± 3.5 10.1 ± 7.5 8.5 ± 6.5
OC/PM2.5 (%) 12.8 ± 2.3 19.8 ± 5.4 16.5 ± 6.1 16.4 ± 4.6 8.1 ± 3.2 19.3 ± 4.3 16.3 ± 6.8 14.5 ± 4.8
EC/PM2.5 (%) 8.2 ± 1.2 8.4 ± 1.2 7.1 ± 1.8 7.9 ± 1.4 6.0 ± 1.7 7.5 ± 1.3 6.3 ± 1.5 6.6 ± 1.5
SOC/OC (%) 43.6 ± 7.2 60.7 ± 9.5 56.9 ± 20.4 53.7 ± 12.4 19.8 ± 1.4 57.6 ± 12.9 53.8 ± 20.8 43.7 ± 16.0
SOC/PM2.5 (%) 5.7 ± 1.8 12.4 ± 5.3 10.3 ± 6.2 9.2 ± 4.4 1.9 ± 1.7 11.5 ± 4.6 9.8 ± 6.6 7.7 ± 4.3
OC: organic carbon, EC: elemental carbon, POC: primary organic carbon, SOC: secondary organic carbon.
Figure 4.
Comparisons of ions concentration in PM
2.5
at the YT and KD sites for different ranges of
meteorological factors: (a,b) relative humidity; (c,d) temperature; (e,f) O3concentration.
Table 3. Seasonal distribution of carbonaceous species over the three seasons at the YT and KD sites.
Carbonaceous YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
OC (µg/m3)7.7 ±1.4 16.2 ±7.6 16.4 ±9.3 13.4 ±6.1 4.6 ±2.1 13.0 ±4.2 16.1 ±9.1 11.2 ±5.2
EC (µg/m3)4.9 ±0.8 6.7 ±2.2 6.4 ±2.3 6.0 ±1.8 3.4 ±1.3 5.0 ±1.4 5.9 ±2.2 4.8 ±1.6
OC/EC 1.6 ±0.2 2.4 ±0.7 2.5 ±1.0 2.1 ±0.6 1.3 ±0.2 2.7 ±0.8 2.7 ±1.2 2.2 ±0.8
POC (µg/m3)4.3 ±0.7 5.9 ±1.9 5.6 ±2.0 5.3 ±1.5 3.5 ±1.3 5.2 ±1.4 6.1 ±2.2 4.9 ±1.7
SOC (µg/m3)3.4 ±1.0 10.3 ±6.0 10.8 ±7.8 8.2 ±4.9 1.1 ±0.9 7.8 ±3.5 10.1 ±7.5 8.5 ±6.5
OC/PM2.5 (%) 12.8 ±2.3 19.8 ±5.4 16.5 ±6.1 16.4 ±4.6 8.1 ±3.2 19.3 ±4.3 16.3 ±6.8 14.5 ±4.8
EC/PM2.5 (%) 8.2 ±1.2 8.4 ±1.2 7.1 ±1.8 7.9 ±1.4 6.0 ±1.7 7.5 ±1.3 6.3 ±1.5 6.6 ±1.5
SOC/OC (%) 43.6 ±7.2 60.7 ±9.5 56.9 ±20.4 53.7 ±12.4 19.8 ±1.4 57.6 ±12.9 53.8 ±20.8 43.7 ±16.0
SOC/PM2.5 (%) 5.7 ±1.8 12.4 ±5.3 10.3 ±6.2 9.2 ±4.4 1.9 ±1.7 11.5 ±4.6 9.8 ±6.6 7.7 ±4.3
OC: organic carbon, EC: elemental carbon, POC: primary organic carbon, SOC: secondary organic carbon.
3.2.3. Metals
The concentrations of metal elements in PM
2.5
at YT and KD over the course of the sampling
period are shown in Table 4and Figure 5. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As,
and Mn at both sites were higher than 40 ng/m
3
. The average concentrations of almost all detected
metal elements at YT were higher than those at KD where the Xiangtan Iron and Steel Group Co. Ltd.
is located (Figure 1); the exception was for Ca, Mn, and Se. The seasonal patterns of the detected metal
elements (except Ca, Cr, Fe, Zn, As, and Cd) were also similar to that of the PM
2.5
and decreased in the
order of winter > autumn > spring. The seasonal patterns of Zn at the two sites decreased in the order
of autumn> spring > winter; for Cd, the order was autumn > winter > spring. There were no uniform
seasonal patterns for Ca, Cr, Fe, or As at either site.
Int. J. Environ. Res. Public Health 2019,16, 539 9 of 16
Table 4. Concentrations (ng/m3) of metal elements in PM2.5 during the sampling periods at the YT and KD sites.
Metal YT KD
Spring Autumn Winter Average Spring Autumn Winter Average
Mg 117.7 ±86.3 185.1 ±126.8 219.2 ±126.3 174.0 ±113.2 117.9 ±74.6 152.1 ±102.3 200.4 ±79.4 156.8 ±85.4
Al 366.7 ±300.9 451.8 ±224.5 482.3 ±264.7 463.6 ±263.6 351.2 ±267.0 441.8 ±221.0 448.6 ±225.0 413.9 ±237.7
K 519.1 ±301.1 1090.2 ±515.4 1244.6 ±595.9 951.3 ±470.8 481.4 ±329.6 938.1 ±390.0 1027.4 ±433.1 815.6 ±384.2
Ca 136.4 ±60.3 255.1 ±171.1 250.8 ±155.6 214.1 ±129.0 295.1 ±279.8 175.5 ±93.5 237.3 ±96.5 236.0 ±156.6
V 2.4 ±1.8 4.3 ±1.7 4.1 ±3.1 3.6 ±2.2 3.1 ±1.8 3.3 ±1.3 3.8 ±1.4 3.4 ±1.5
Cr 4.2 ±2.5 11.7 ±13.6 10.6 ±4.1 8.8 ±6.7 4.1 ±1.7 5.4 ±2.3 9.8 ±4.1 6.4 ±2.7
Mn 25.2 ±18.4 35.5 ±17.4 41.5 ±25.1 34.1 ±20.3 20.4 ±10.5 34.6 ±19.0 51.8 ±27.6 35.6 ±19.0
Fe 925.7 ±893.8 548.2 ±246.0 734.8 ±792.7 736.2 ±644.2 404.5 ±297.0 502.0 ±238.5 710.7 ±616.5 539.1 ±384.0
Co 15.5 ±5.4 23.6 ±5.3 25.6 ±9.5 21.5 ±6.7 16.9 ±5.4 20.4 ±3.8 24.9 ±7.6 20.7 ±6.0
Ni 3.5 ±1.4 5.3 ±2.1 5.5 ±4.0 4.8 ±2.5 4.5 ±2.7 4.6 ±2.4 4.8 ±2.3 4.6 ±2.5
Cu 23.6 ±17.8 29.0 ±14.5 38.0 ±33.2 30.2 ±21.8 15.3 ±4.9 27.3 ±20.3 30.9 ±26.7 24.5 ±17.3
Zn 299.6 ±224.2 361.9 ±198.0 239.9 ±204.3 300.5 ±208.8 294.6 ±209.9 323.3 ±255.7 208.0 ±168.5 275.3 ±211.3
As 113.7 ±105.4 71.1 ±61.2 51.1 ±42.9 78.6 ±69.8 28.6 ±18.5 47.8 ±32.5 42.9 ±34.6 39.8 ±28.6
Se 6.0 ±3.8 11.0 ±4.1 11.5 ±6.9 9.5 ±4.9 7.9 ±4.8 13.4 ±4.0 15.8 ±7.4 12.4 ±5.4
Mo 0.7 ±0.5 1.2 ±0.7 1.6 ±1.3 1.2 ±0.8 0.5 ±0.3 0.9 ±0.4 1.2 ±0.4 0.9 ±0.4
Cd 4.4 ±6.3 7.9 ±7.0 4.9 ±4.2 5.7 ±5.8 4.7 ±5.1 5.8 ±6.7 5.3 ±7.6 5.3 ±6.5
Sb 207.4 ±51.9 344.2 ±75.4 412.9 ±146.6 321.5 ±91.3 242.9 ±70.6 293.6 ±46.5 404.0 ±116.5 313.5 ±77.9
Ba 14.1 ±7.4 33.5 ±26.5 65.9 ±68.3 37.9 ±34.1 18.3 ±15.6 21.4 ±11.8 53.8 ±48.9 31.2 ±25.4
Tl 0.5 ±0.5 0.9 ±0.5 1.4 ±1.1 0.9 ±0.7 0.4 ±0.3 0.8 ±0.7 0.9 ±0.6 0.4 ±0.5
Pb 53.3 ±49.0 134.4 ±105.6 155.1 ±118.1 114.3 ±90.9 60.4 ±46.2 105.0 ±109.4 123.2 ±107.3 96.2 ±87.6
Int. J. Environ. Res. Public Health 2019,16, 539 10 of 16
To identify origins and evaluate the degree of anthropogenic influences, enrichment factors (EF)
were calculated for the measured elements for each season. The calculation method is described in
detail by Li et al. [
19
] and Liu et al. [
20
]. In this study, Al was used as the reference element [
20
]. The EF
values of the detected elements in the PM
2.5
at the two sampling sites during the sampling period are
shown in Figure 6. During each season, the EF values for Mg, Al, K, Ca, V, Fe, and V at the two sites
were all below 10, indicating that these metal elements may originate from crustal sources. The EF
values for elements such as Cr, Ni, and Mo, were between 10 and 100, indicating a mixed (geological
and anthropogenic) origin. In contrast, the EF values for Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb at the
two sites were all above 100, indicating an anthropogenic origin. For Mn and Ba, the EF values were
higher than 10 in winter, suggesting a mixed origin. However, the EF values of Mn and Ba were less
than 10 in the other seasons, indicative of crustal origin.
Int. J. Environ. Res. Public Health 2019, 16, x 11 of 17
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
Ni
(a) Spring
Tl
KD concentrations(ng/m
3
)
Mo
V
Ni
Cr
Cd
Se
Ba
Co Cu
Mn
Pb
As
Mg
Ca
Sb Zn
Al K
Fe
Tl
Mo
V
Cu
Ba
Co
Cr
Mn As
Pb
Mg Ca
Sb Zn
AlFe K
Se
(b) Autumn
Cd
K
Fe
Al
Sb
Pb
Ba
Mn
As
Cu
Co
Se
Cr
Cd
Ni
V
(c) Winter
KD concentrations(ng/m
3
)
YT concentrations(ng/m
3
)
Tl
Mo
Ca
Zn
Mg
Fe
Sb
Zn
Ca
Mg
Pb
As
Mn
Ba
Cu
Co
Se
Cr
Cd
Ni
(d) Average
YT concentrations(ng/m
3
)
Tl
Mo
V
K
Al
Figure 5. Seasonal mean concentrations of metal elements in spring (a), autumn (b), winter (c), and
their average concentrations during the sampling periods (d) at the YT and KD sites.
Mg Al K Ca V Cr Mn Fe Co Ni Cu Zn As Se Mo Cd Sb Ba Tl Pb
1
10
100
1,000
10,000
100,000
EF
Spring Autumn Winter Average
(a) YT
Mg Al K Ca V Cr Mn Fe Co Ni Cu Zn As Se Mo Cd Sb Ba Tl Pb
1
10
100
1,000
10,000
100,000
EF
(b) KD
Figure 6. Enrichment factors (EF) values of the detected elements in the PM2.5 at the two sampling
sites during the sampling periods.
3.3. Source Apportionment using PMF Models
In this study, 24 chemical components were used as inputs to the PMF model, including Al, Mg,
Ca, K, V, Cr, Mn, Co, Ni, Cu, Fe, Zn, Pb, Cd, Sb, Ba, As, Se, Cl−, SO42−, NO3−, NH4+, OC, and EC. In
total, 20 runs were performed for each factor and the lowest value of Qrobust was 14,284.1 with a
Qrobust/Qtrue ratio of more than 0.90. Six appropriate source factors were identified at both sites,
1000
1000
Figure 5.
Seasonal mean concentrations of metal elements in spring (
a
), autumn (
b
), winter (
c
), and
their average concentrations during the sampling periods (d) at the YT and KD sites.
Int. J. Environ. Res. Public Health 2019, 16, x 11 of 17
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
0.1 1 10 100 1000
0.1
1
10
100
1000
Ni
(a) Spring
Tl
KD concentrations(ng/m
3
)
Mo
V
Ni
Cr
Cd
Se
Ba
Co Cu
Mn
Pb
As
Mg
Ca
Sb Zn
Al K
Fe
Tl
Mo
V
Cu
Ba
Co
Cr
Mn As
Pb
Mg Ca
Sb Zn
AlFe K
Se
(b) Autumn
Cd
K
Fe
Al
Sb
Pb
Ba
Mn
As
Cu
Co
Se
Cr
Cd
Ni
V
(c) Winter
KD concentrations(ng/m
3
)
YT concentrations(ng/m
3
)
Tl
Mo
Ca
Zn
Mg
Fe
Sb
Zn
Ca
Mg
Pb
As
Mn
Ba
Cu
Co
Se
Cr
Cd
Ni
(d) Average
YT concentrations(ng/m
3
)
Tl
Mo
V
K
Al
Figure 5. Seasonal mean concentrations of metal elements in spring (a), autumn (b), winter (c), and
their average concentrations during the sampling periods (d) at the YT and KD sites.
Mg Al K Ca V Cr Mn Fe Co Ni Cu Zn As Se Mo Cd Sb Ba Tl Pb
1
10
100
1,000
10,000
100,000
EF
Spring Autumn Winter Average
(a) YT
Mg Al K Ca V Cr Mn Fe Co Ni Cu Zn As Se Mo Cd Sb Ba Tl Pb
1
10
100
1,000
10,000
100,000
EF
(b) KD
Figure 6. Enrichment factors (EF) values of the detected elements in the PM2.5 at the two sampling
sites during the sampling periods.
3.3. Source Apportionment using PMF Models
In this study, 24 chemical components were used as inputs to the PMF model, including Al, Mg,
Ca, K, V, Cr, Mn, Co, Ni, Cu, Fe, Zn, Pb, Cd, Sb, Ba, As, Se, Cl−, SO42−, NO3−, NH4+, OC, and EC. In
total, 20 runs were performed for each factor and the lowest value of Qrobust was 14,284.1 with a
Qrobust/Qtrue ratio of more than 0.90. Six appropriate source factors were identified at both sites,
1000
1000
Figure 6.
Enrichment factors (EF) values of the detected elements in the PM
2.5
at the two sampling
sites during the sampling periods ((a) YT, (b) KD).
Int. J. Environ. Res. Public Health 2019,16, 539 11 of 16
3.3. Source Apportionment Using PMF Models
In this study, 24 chemical components were used as inputs to the PMF model, including Al, Mg,
Ca, K, V, Cr, Mn, Co, Ni, Cu, Fe, Zn, Pb, Cd, Sb, Ba, As, Se, Cl
−
, SO
42−
, NO
3−
, NH
4+
, OC, and EC.
In total, 20 runs were performed for each factor and the lowest value of Qrobust was 14,284.1 with a
Qrobust/Qtrue ratio of more than 0.90. Six appropriate source factors were identified at both sites,
representing industrial emissions, fugitive dust, coal combustion, secondary inorganic aerosol, vehicle
exhaust, and steel industry. The factor profiles are shown in Figure 7.
Factor 1 in Figure 7was associated with industrial emissions sources. The factor profile is
characterized by a high load of Pb, Zn, Fe, and Cu, which are tracer elements of metal manufacturing
plants and storage industrial emissions (e.g., Xiangtan Iron and Steel Group Co., Ltd.) [
19
,
33
,
34
].
In addition, other chemical components, such as Mn, Se, and Cd, also had high loadings for this source.
An array of tracer species (Cr, Co, Cd, Zn, As, Fe, Cu, and Mn) have been used in India to identify
specific industrial emissions [
35
,
36
]. Pb and Zn are major elements emitted from nonferrous metal
smelting processes and from waste incinerators [
37
]. This factor contributed 8.5% and 6.3% to the
PM2.5 mass at YT and KD sites (Figure 8).
Factor 2 in Figure 7has been identified as fugitive dust (e.g., re-suspended dust), which show
elevated loadings of Al, Ca, Fe, and Mg. The presence of Al, Ca, Fe, and Mg in PM
2.5
from fugitive
dust has been documented by many studies [
5
,
6
,
20
,
33
]. The EF values of the Al, Ca, Fe, and Mg
were all less than 10, as shown in Figure 6, which further indicates that they primarily originated
from crustal sources. This factor contributed 16.4% and 18.0% to the PM
2.5
mass at YT and KD sites.
As expected, this source contributed more at the KD site owing to a large area of arable land (Figure 1)
and construction activities within the vicinity of the site.
Factor 3 in Figure 7is likely to be associated with coal combustion and secondary aerosol
(mixed sources). The factor is characterized by high loadings of SO
42−
, Cl
−
, Sb and EC, typical
of coal combustion; the relatively high loadings of NH
4+
also suggests a contribution from secondary
aerosols [
5
,
6
,
19
–
21
,
38
,
39
]. Some of the NH
4+
may come from the after-treatment equipment for
removing acidic gases from coal combustion; although the relatively high contribution of to NH
4+
the
profile suggests a non-negligible contribution from secondary sources to this factor. NH
4+
is formed
from gaseous NH
3
, which is emitted mainly from the agricultural sector (most notably animal manure
and fertilizer application) [
19
,
40
]. This factor contributed 18.6% and 21.3% to the PM
2.5
mass at YT
and KD sites (Figure 8). According to the Xiangtan Statistical Yearbook 2016, the total amount of coal
consumed in Xiangtan was ~8129 million tons in 2016, which accounted for about 70% of the total
energy consumption.
Factors 4 in Figure 7were identified as secondary inorganic aerosols, which were characterized
by high loads of NO
3−
, NH
4+
, and SO
42−
. This factor contributed 26.6% and 24.6% to the PM
2.5
mass
at YT and KD sites (Figure 8). Previous studies have found that these inorganic ions are markers of
secondary inorganic aerosols [
5
,
6
,
19
–
21
,
31
], and as discussed, are often formed by heterogeneous and
homogeneous processes under favourable meteorological conditions [
4
,
10
,
41
–
44
]. NO
3−
is mainly
converted from ambient NO
x
emitted by both vehicle exhausts and fossil fuel combustion, while the
precursor of aerosol SO
42−
is SO
2
, which primarily originates from coal combustion [
1
,
32
]. Therefore,
the actual contributions of coal combustion and vehicle exhaust to PM
2.5
mass discussed above were
likely underestimated.
Factor 5 in Figure 7is likely to be from vehicle exhaust, which has high loadings of OC and EC.
This factor contributed 21.7% and 20.9% to the PM
2.5
mass at YT and KD sites (Figure 8). The presence
of OC and EC in the PM2.5 from vehicle exhaust has been documented previously [6,20,21,37].
Factor 6 in Figure 7were associated with steel industry sources, which were characterized by
a high load of Fe. In addition, other chemical components, such as Zn, Cu, and Pb, also had high
loadings for this source. The attribution of Fe, Zn, Pb, in PM
2.5
to the iron and steel industry has been
documented by many other studies [
19
,
33
,
34
]. This factor contributed 8.1% and 8.9% to the PM
2.5
mass at YT and KD sites (Figure 8).
Int. J. Environ. Res. Public Health 2019,16, 539 12 of 16
Overall, secondary inorganic aerosols (25.7%) was found to be the largest contributor to PM
2.5
at
Xiangtan city, followed by vehicle exhaust (21.3%), coal combustion and secondary aerosols (19.9%),
fugitive dust (17.1%), steel industry (8.5%), and industrial emissions (7.5%).
Based on the results of source apportionment, we recommend that emissions due to vehicle exhaust
and coal combustion should be the priority targets to reduce the PM
2.5
pollution in Xiangtan. This will
not only reduce the primary emissions but also the secondary aerosols formed from SO2and NOx.
Int. J. Environ. Res. Public Health 2019, 16, x 13 of 17
This will not only reduce the primary emissions but also the secondary aerosols formed from SO
2
and NO
x
.
Figure 7. Factor profiles (bars and left y-axis) and percentage contributions (dots and right y-axis) of
each chemical component resolved from the positive matrix factorization (PMF) model. Factor 1 to 6
represents industrial emissions, fugitive dust, coal combustion and secondary aerosols, secondary
inorganic aerosols, vehicle exhaust, and steel industry.
10
1
10−1
10−2
10−3
10−4
10−5
10
10−1
10−3
10−5
10
10−1
10−3
10−5
10
10−1
10−3
10−5
10
10−1
10−3
10−5
10
1
10−1
10−2
10−3
10−4
10−5
Figure 7.
Factor profiles (bars and left y-axis) and percentage contributions (dots and right y-axis) of
each chemical component resolved from the positive matrix factorization (PMF) model. Factor 1 to
6 represents industrial emissions, fugitive dust, coal combustion and secondary aerosols, secondary
inorganic aerosols, vehicle exhaust, and steel industry.
Int. J. Environ. Res. Public Health 2019,16, 539 13 of 16
Int. J. Environ. Res. Public Health 2019, 16, x 14 of 17
Coal combustion and
secondary aerosols
(18.6%)
Fugitive dust
（16.4%）
Secondary inorganic aerosols
(26.6%)
Vehicle exhaust
(21.7%)
Steel industry
(8.1%) Industry emissions
(8.5%)
(a) YT
Coal combustion and
secondary aerosols
(21.3%)
Fugitive dust
（18.0%）
Secondary inorganic
aerosols (24.6%)
Vehicle exhaust
(20.9%)
Steel industry
(8.9%) Industry emissions
(6.3%)
(b) KD
(a) (b)
Figure 8. Contributions of different sources (factors) to PM2.5 mass at (a) YT and (b) KD.
4. Conclusions
In this study, seasonal and spatial variations as well as the potential sources of PM2.5 collected in
two urban areas of Xiangtan, central south China, were investigated. The mass concentrations of
PM2.5 during the sampling periods were in the range of 30–217 µg/m3, being highest in winter and
lowest in spring.
The mean concentrations of WSIIs were 44.6 ± 14.7 and 40.9 ± 12.6 µg/m3 at YT and KD sites,
respectively, accounting for 59.2 ± 9.8% and 57.7 ± 10.4% of the PM2.5 mass, respectively. The WSIIs
were dominated by secondary inorganic ions (i.e., SO42−, NO3−, and NH4+), which accounted for 43.5 ±
8.3% and 42.6 ± 8.5% of the PM2.5 mass concentration at YT and KD, respectively. The highest
concentrations of SO42− and SOR at the two sites occurred in the spring while the lowest were in
winter. These findings differ from those for NO3− and NOR. The average concentrations of total
carbon (EC + OC) were 19.4 ± 7.8 and 16.0 ± 6.8 µg/m3 at YT and KD, accounting for 24.3% and 21.1%
of the PM2.5 mass, respectively. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in
PM2.5 at the two sites were relatively high (more than 40 ng/m3). EF values for Mg, Al, K, Ca, V, Fe,
and V at the two sites were all below 10, which indicates that they may be primarily originated from
crustal sources.
Six factors were identified by PMF at Xiangtan, representing secondary inorganic aerosols,
vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, and
steel industry. The first three sources are the dominant ones, contributing over 67% to PM2.5 mass.
Thus, it is recommended that secondary inorganic aerosols, coal combustion, and vehicles are the
primary targets in order to reduce PM2.5 pollution in Xiangtan.
Author Contributions: Conceptualization, Z.X. and Z.S.; Data analysis, Z.X., Z.S., T.V. and Z.T.; Investigation,
Y.C., X.M., and J.L.; Methodology, X.M. L.H., and J.L.; Writing—original draft, Z.X. and X.M.
Funding: This research was funded by the National Natural Science Foundation of China (grant no. 41603046),
and the Hunan Provincial Postgraduate Technology Innovation Project (no. CX2017B644).
Acknowledgments: We thank the editors and the three anonymous reviewers, for their constructive comments
and suggestions on the manuscript.
Conflicts of Interest: The authors declare no competing financial interest.
Figure 8. Contributions of different sources (factors) to PM2.5 mass at (a) YT and (b) KD.
4. Conclusions
In this study, seasonal and spatial variations as well as the potential sources of PM
2.5
collected
in two urban areas of Xiangtan, central south China, were investigated. The mass concentrations of
PM
2.5
during the sampling periods were in the range of 30–217
µ
g/m
3
, being highest in winter and
lowest in spring.
The mean concentrations of WSIIs were 44.6
±
14.7 and 40.9
±
12.6
µ
g/m
3
at YT and KD sites,
respectively, accounting for 59.2
±
9.8% and 57.7
±
10.4% of the PM
2.5
mass, respectively. The WSIIs
were dominated by secondary inorganic ions (i.e., SO
42−
, NO
3−
, and NH
4+
), which accounted for
43.5
±
8.3% and 42.6
±
8.5% of the PM
2.5
mass concentration at YT and KD, respectively. The highest
concentrations of SO
42−
and SOR at the two sites occurred in the spring while the lowest were in
winter. These findings differ from those for NO
3−
and NOR. The average concentrations of total
carbon (EC + OC) were 19.4
±
7.8 and 16.0
±
6.8
µ
g/m
3
at YT and KD, accounting for 24.3% and 21.1%
of the PM
2.5
mass, respectively. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in
PM
2.5
at the two sites were relatively high (more than 40 ng/m
3
). EF values for Mg, Al, K, Ca, V, Fe,
and V at the two sites were all below 10, which indicates that they may be primarily originated from
crustal sources.
Six factors were identified by PMF at Xiangtan, representing secondary inorganic aerosols, vehicle
exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, and steel industry.
The first three sources are the dominant ones, contributing over 67% to PM
2.5
mass. Thus, it is
recommended that secondary inorganic aerosols, coal combustion, and vehicles are the primary targets
in order to reduce PM2.5 pollution in Xiangtan.
Author Contributions:
Conceptualization, Z.X. and Z.S.; Data analysis, Z.X., Z.S., T.V. and Z.T.; Investigation,
Y.C., X.M. and J.L.; Methodology, X.M., L.H. and J.L.; Writing—Original Draft, Z.X. and X.M.
Funding:
This research was funded by the National Natural Science Foundation of China (grant no. 41603046),
and the Hunan Provincial Postgraduate Technology Innovation Project (no. CX2017B644).
Acknowledgments:
We thank the editors and the three anonymous reviewers, for their constructive comments
and suggestions on the manuscript.
Conflicts of Interest: The authors declare no competing financial interest.
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