Introduction
Atmospheric fine particles (PM2.5, dP ≤ 2.5 µm), a mixture
of many inorganic and organic components, reside for a long time in the
atmosphere and can penetrate deep into the lungs. Prolonged exposure to
PM2.5 can cause adverse health impacts and premature mortality in
humans (Betha et al., 2014). Potential health benefits and an improvement in
general mortality could be expected if the control policies were implemented
(Boldo et al., 2011). The adverse effects of PM2.5 can reach
intercontinental scales (Anenberg et al., 2014) due to the potential
transport of PM2.5 over hundreds to thousands of kilometres (Seinfeld
and Pandis, 2012). The sources of PM2.5, particularly motor vehicle
emissions, are associated with an increase in hospital admissions
(Kioumourtzoglou et al., 2014). A study by Bell et al. (2014) suggested that
controlling some of the sources of PM2.5 could protect public health
more efficiently than the regulation of particle concentration. Thus, the
possible reduction in health risks from the predominant sources of
PM2.5 is desired as part of the mitigation strategy. Diesel emissions
and biomass burning, as the primary risk sources of PM2.5, should be
closely monitored and regulated (Wu et al., 2009).
The identification of PM2.5 sources is becoming a widely recognized way
to protect human health as well as the environment. Multivariate receptor
models are very useful in the source apportionment of PM2.5. Widely used
multivariate methods are (a) a chemical mass balance model (CMB) (Watson et
al., 1990), (b) positive matrix factorization (PMF) (Paatero, 1997; Paatero
and Tapper, 1994), (c) Unmix (Henry, 1987), (d) principal component analysis
coupled with absolute principal component score (PCA–APCS) (Thurston and
Spengler, 1985), (e) pragmatic mass closure (PMC) (Harrison et al., 2003), and
(f) a new source-type identification method for PM2.5 known as Reduction
and Species Clustering Using Episodes (ReSCUE) (Vedantham et al., 2014). PMF
is the most reliable method for source-type identification for the following
reasons: (i) it uses a weighted least-squares fit and estimates error of the
measured data and can impose non-negativity constraints weighing each data
point individually (Paatero, 1997; Paatero and Tapper, 1994), (ii) a priori
knowledge of pollutants is not necessary, and (iii) it is able to deal with
missing values, noisy data, outliers, and values below detection limit
(Baumann et al., 2008; Khan et al., 2012, 2015b; Polissar et al., 1998a, b).
A recent study by Gibson et al. (2015) suggested that PMF can resolve
PM2.5 concentrations even below 2 µg m-3 more accurately
compared to PMC and CMB.
Source apportionment studies of PM2.5 based on monsoonal seasonal
changes in Malaysia are of widespread interest due to the influence of local
sources as well as trans-boundary haze pollution. This haze pollution
reaches its worst level during the south-west (SW) and north-east (NE)
monsoons each year. Therefore, the main objectives of this work are to
investigate (a) the monsoonal effect on the variability of PM2.5, and its
chemical composition, (b) factors influencing the sources of PM2.5 and
(c) to quantitatively characterize the non-carcinogenic and carcinogenic
risks to the potentially exposed human populations by selected heavy metals
in PM2.5 released from the particular sources. The PM2.5 mass
concentration contributed by each source will be calculated using PMF 5.0.
Methodologies
Description of the study area
Figure 1 shows the sampling location, which is on the roof top of the Biology
Building of the Faculty of Science and Technology (FST), Universiti Kebangsaan Malaysia (UKM), Malaysia (2∘55′31.91′′ N,
101∘46′55.59′′ E, about 65 m above sea level). This site is less
than 1 km from the main Bangi road.
Map of the study area showing the sampling site and nearby line
sources.
Sampling and analysis of PM2.5 samples
Sampling was carried out on a 24 h basis for a period from July to September 2013 and January to February 2014 for a total of 27 samples. The PM2.5
samples were collected on quartz microfiber filters (203 mm × 254 mm, Whatman™, UK) through a PM2.5 high-volume sampler (HVS;
Tisch, USA) at a flow rate of 1.13 m3 min-1. Several other
researchers also conducted sampling using the quartz microfiber filters for
the analysis of trace metals (H. Li et al., 2015; Martins et al., 2016;
Kholdebarin et al., 2015; Cusack et al., 2015; Sánchez-Soberón et
al., 2015). Prior to use, the filters were preheated at 500 ∘C
for 3 h to remove any deposited organic compounds. All filter papers either
blank or exposed were conditioned in a desiccator for 24 h before and after
sampling prior to weighing using a 5-digit high-resolution electronic
balance (A&D, GR-202, Japan) with a 0.01 mg detection limit. The filter
samples were then stored at -18 ∘C until the extraction
procedure. A microwave-assisted digestion system (Start D, Milestone,
Germany) was employed for the preparation of the trace element samples. The
microwave was operated at two temperature stages, 180 ∘C for 20 min and ramping to 220 ∘C for 15 min. The power was
set at 500 watts during the procedure when the number of samples ≤ 3. However,
the power was set at 1000 watts if the number of samples exceeded three. A
4 : 1 ratio of 12 mL nitric acid (65 %, Merck KGaA, Germany) and 3 mL
hydrogen peroxide (40 %, Merck KGaA, Germany) was used as the reagent in
this digestion process. A portion of the filter was soaked in the
tetrafluoromethaxil (TFM) vessels (SK-10, Milestone, Germany) of the
microwave where total mass of the sample and reagent was maintained below
0.25 g for quality assurance purposes. Upon completion, the samples were
filtered using a syringe filter (Acrodisc®, 0.2 µm, Pall Gelman Laboratory, MI, USA) with a 50 cc mL-1 Terumo syringe
(Terumo®, Tokyo, Japan) before dilution to 25 mL
using ultrapure water (UPW; 18.2 MΩ cm,
Easypure® II, Thermo Scientific, Canada). For the
preparation of samples for water-soluble ion analysis, a portion of the
filter samples was cut into small pieces and placed directly into 50 mL
centrifuge tubes with UPW. For this extraction, a combination of ultrasonic
vibration, centrifuge, and mechanical shaking were applied. The samples were
first sonicated in an ultrasonic bath (Elmasonic S70H, Elma, Germany) for 20 min. Then, the extraction solutions were centrifuged at 2500 rpm (Kubota
5100, Japan) for 10 min before shaken using a vortex mixer for 10 min. The
sonication and centrifuged steps were repeated for two more times before the
extract was filtered through glass microfiber filters (Whatman™, UK).
Both the trace elements and water-soluble ion extracts were refrigerated at
4 ∘C until further analysis. The trace elements (Al, Ba, Ca, Fe,
Mg, Pb, Zn, Ag, As, Cd, Cr, Li, Be, Bi, Co, Cu, Mn, Ni, Rb, Se, Sr, and V)
were determined by inductively coupled plasma mass spectroscopy (ICP-MS;
PerkinElmer ELAN 9000, USA) while the water-soluble ionic composition
(Na+, NH4+, K+, Ca2+, Mg2+, Cl-,
NO3-, and SO42-) was determined using ion chromatography (IC)
(Metrohm 850 model 881 Compact IC Pro, Switzerland). Metrosep A-Supp
5-150/4.0 and C4-100/4.0 columns were used in the determination of cations
and anions, respectively; 1.7 mmol L-1 nitric and 0.7 mmol L-1
dipicolinic acid (Merck KGaA, Germany) were prepared to be used as eluents
for cations. Eluents of 6.4 mmol L-1 sodium carbonate
(Na2CO3) (Merck KGaA, Germany) and 2.0 mmol L-1 sodium
bicarbonate (NaHCO3) (Merck KGaA, Germany) were prepared and used to
measure anions (Cl-, NO3- and SO42-) with a flow
rate of 0.7 mL min-1; 100 mmol L-1
Suprapur® sulfuric acid (H2SO4) (Merck
KGaA, Germany) was also prepared to use as a suppressor regenerant and ions
were detected by a conductivity detector.
The statistical parameters of the PM2.5 and its compositions.
Species
Overall (n=27)
SWb (n=9)
NEc (n=18)
MDLd
Recovery (%)
(ng m-3)
Mean ± SDa
Range
Mean ± SD
Range
Mean ± SD
Range
Al
267.6 ± 145.0
98.48–826.6
203.1 ± 118.42
98.48–416.09
299.8 ± 149.2
101.9–826.6
0.70
70 (54–97)
Ba
1660 ± 1501
319.2–6092
1372 ± 1480
319.2–5187
1804 ± 1532
447.6–6092
0.02
–
Ca
1770 ± 725.9
n.d.–3150
1584 ± 325.5
1234–2154
1975 ± 683.4
882.1–3150
2.88
33 (23–47)
Fe
3052 ± 654.6
2171–4567
2513 ± 239.6
2171–2893
3322 ± 630.4
2530–4567
0.40
80 (69–95)
Mg
207.6 ± 83.85
34.43–371.7
207.1 ± 72.85
119.0–356.0
207.9 ± 90.86
34.43–371.7
0.17
95 (81–111)
Pb
21.84 ± 16.30
3.57–76.17
28.06 ± 20.27
13.1–76.17
18.72 ± 13.49
3.57–51.70
0.01
119 (89–134)
Zn
389.2 ± 179.8
178.8–817.9
526.8 ± 236.3
178.8–817.9
320.4 ± 90.25
184.7–448.2
0.22
102 (84–129)
Ag
0.09 ± 0.05
n.d.–0.21
0.05 ± 0.04
0.01-0.11
0.10 ± 0.05
0.02–0.21
0.01
120 (97–170)
As
5.76 ± 4.74
1.10–18.33
5.22 ± 2.93
1.55–9.79
6.04 ± 5.49
1.10–18.33
0.45
88 (81–94)
Cd
0.54 ± 0.29
0.13–1.15
0.44 ± 0.22
0.13–0.81
0.58 ± 0.32
0.17–1.15
< 0.01
85 (81–89)
Cr
107.68 ± 18.57
82.32–152.62
91.06 ± 7.52
82.32–104.4
115.9 ± 16.78
91.17–152.6
0.02
56 (31–87)
Li
0.22 ± 0.12
0.04–0.43
0.11 ± 0.06
0.04–0.21
0.28 ± 0.10
0.07–0.43
0.09
–
Be
0.01 ± 0.01
n.d.–0.03
0.003 ± 0.01
n.d.–0.01
0.01 ± 0.01
n.d.–0.03
< 0.01
–
Bi
0.76 ± 0.60
0.08–2.08
0.67 ± 0.35
0.13–1.17
0.80 ± 0.70
0.08–2.08
0.03
–
Co
0.85 ± 0.47
0.39–2.36
1.16 ± 0.61
0.39–2.36
0.70 ± 0.30
0.39–1.38
0.08
96 (87–109)
Cu
28.33 ± 11.02
16.83–62.55
32.39 ± 10.08
19.78–49.27
26.30 ± 11.17
16.83–62.55
0.30
101 (96–105)
Mn
4.03 ± 1.91
0.23–7.18
3.13 ± 2.07
0.23–6.08
4.49 ± 1.71
1.46–7.18
0.95
126 (114–147)
Ni
17.24 ± 8.55
7.86–46.70
23.59 ± 11.11
7.86–46.70
14.06 ± 4.66
8.84–27.03
0.67
91 (82–99)
Rb
3.59 ± 1.08
1.74–6.16
4.14 ± 1.29
2.23–6.16
3.32 ± 0.87
1.74–4.69
0.13
78 (52–113)
Se
0.65 ± 0.33
0.20–1.24
0.36 ± 0.10
0.20–0.53
0.79 ± 0.31
0.39–1.24
0.09
94 (78–110)
Sr
40.25 ± 31.05
13.75–120.93
35.88 ± 32.10
13.75–118.47
42.43 ± 31.22
15.72–120.9
0.38
91 (75–125)
V
5.13 ± 3.05
0.63–13.16
3.70 ± 2.47
0.63–7.82
5.85 ± 3.12
2.21–13.16
< 0.01
85 (77–93)
Na+
532.1 ± 262.0
n.d.–1029.07
363.9 ± 185.6
159.9–778.8
606.90
23.66–1029.1
62.68
–
NH4+
598.9 ± 399.2
82.60–1622.17
542.5 ± 320.8
82.60–1141.4
627.2 ± 439.0
105.5–1622.2
–
–
K+
343.3 ± 183.2
70.18–696.04
307.8 ± 103.5
175.6–484.6
361.1 ± 212.7
70.18–696.0
2.35
–
Ca2+
255.9 ± 84.22
87.55–455.55
295.1 ± 95.8
186.4–455.6
236.3 ± 72.84
87.55–360.4
23.21
–
Mg2+
42.26 ± 17.57
12.70–77.60
32.61 ± 18.32
12.70–71.94
47.09 ± 15.49
15.65–77.60
23.71
–
Cl-
56.71 ± 44.94
4.67–151.18
67.63 ± 24.21
40.07–107.18
51.25 ± 52.13
4.67–151.2
0.98
–
NO3-
926.9 ± 1031.8
98.66–3523.7
194.8 ± 73.63
98.66–311.3
1293 ± 1095
136.5–3524
16.51
–
SO42+
2127 ± 2068
n.d.–6211
n.d.
n.d.
2127 ± 2068
350.5–6211
1.82
–
PM2.5e
25.13 ± 9.21
7.01–42.28
22.16 ± 9.14
7.01–35.73
26.61 ± 9.14
12.76–42.28
–
–
a SD: standard deviation; b SW:
south-west monsoon;
c NE: north-east monsoon; d MDL: method detection
limit; e PM2.5 (µg m-3); n.d.: not detected; “–”: no
data.
Quality assurance and quality control
As part of quality assurance and quality control (QA/QC), the concentrations of the composition of PM2.5 were
corrected from the reagent and filter blanks samples, which were treated
with a similar procedure to the exposed filters. To determine the recovery
(%) of the heavy metals, a standard reference material (SRM), urban particulate matter SRM 1648a obtained from the National Institute of
Standards and Technology (NIST), USA, was treated using the procedures
outlined above. The method detection limit (MDL) for trace elements is
calculated as 3 times the standard deviation of 10 replicates of the
reagent blank. Three samples of filter blanks were used to calculate the MDL
of water-soluble ions. Overall MDL were as reported in Table 1. During the
trace element analysis by ICP-MS, two modes of analysis were applied with
updated calibration curves each time. Based on trial runs and SRM1648a, the
elements were initially screened for concentration levels, which resulted in
two modes analysis: (a) a set of metals (Al, Ca, Fe, Mg, Zn, and Mn) with
high concentrations (with several dilution factors), and (b) a set of metals
(Ba, Pb, Ag, As, Cd, Cr, Li, Be, Bi, Co, Cu, Ni, Rb, Se, Sr, and V) with low
concentrations.
Local circulation of wind and biomass fire hotspots
Each year, Peninsular Malaysia experiences two monsoon regimes, the
SW monsoon (June–September) and the NE monsoon
(December–March). During the SW monsoon, south-west winds dominate the wind
pattern in Peninsular Malaysia, inducing drier weather. During the NE
monsoon, strong north-east winds dominate over Peninsular Malaysia,
bringing more rainfall to the east coast. To investigate this, the regional
synoptic wind field 10 m above the surface and a resolution of 0.25 × 0.25∘ ranging from -10∘ S to 25∘ N,
85 to 125∘ E was plotted using open Grid Analysis and Display System (GrADS version 2.0.2). The wind field used to
demonstrate the monsoon regimes in this study is a gridded product produced
by the global atmospheric reanalysis known as ERA-Interim, by the European
Centre for Medium-Range Weather Forecasts (ECMWF) (Dee et al., 2011).
Monthly climatology wind vector from January 2004 to June 2014.
The ERA-Interim 10 m surface wind vectors (January 2004 to June 2014) show
the two opposite monsoon regimes experienced by Peninsular Malaysia (Fig. 2).
It can be seen that the south-west wind, from June to August, that blew from
Sumatra Island, Indonesia, to Peninsular Malaysia was generally weaker with
wind speed around 1–2 m s-1. Whereas the north-east wind, from November
to January, was much stronger, with wind speeds of around 5–7 m s-1
(Fig. 2).
Biomass fire hotspots and the travel path of the monthly back trajectories
of each season were also plotted (Fig. 3). The mean clusters of back
trajectories were produced using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT 4.9), and were re-plotted using the
graphical software, IGOR Pro 6.0.1 (WaveMetrics, OR, USA). A release height
of about 500 m for 120 h back trajectories with 6 h intervals was chosen.
Trajectory start time was chosen at 16:00 (UTC) to represent 24:00 (local
time). The cluster mean of trajectories was regarded by numeral number and
colour (1 – red, 2 – green, 3 – turquoise, and 4 – purple). The fire hotspot data of
the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to
investigate the biomass burning hotspots in the specific area of interest.
The data were downloaded from the National Aeronautics and Space
Administration (NASA) Land Atmosphere near-Real-Time Capability for Earth
Observing System (EOS) Fire Information for Resource Management System (NASA
LANCE FIRMS) fire archive covering an area from 15∘ S to
30∘ N and 80∘ W to 130∘ E. In addition, to
investigate the variability of the boundary layer height around the region
of Peninsular Malaysia, ERA-Interim boundary layer height (BLH) gridded data
from January 2000 to December 2014 were downloaded from the ECMWF. The resolution of this data was
0.5∘ × 0.5∘, covering the domain of
Peninsular Malaysia (99–105∘ E, 0–9∘ N).
Yearly daily means of the ERA-Interim BLH data were calculated using the
climate data operators (CDO) version 1.6.9 software
(https://code.zmaw.de/projects/cdo) developed by the Max Plank Institute, by
first calculating the area mean.
The location of biomass fire hotspots and the monthly mean cluster of
backward trajectories by HYSPLIT 4.9 model for 120 h and 500 m releasing
height starting from 16:00 UTC during the south-west and the
north-east monsoon.
Enrichment factor
The enrichment factor (EF) of the heavy metals was calculated based on the abundance of elements
in the Earth's crust published by Taylor (1964). The EF of each element can
be defined using the following equation
EF=EAlPM2.5EAlCrust,
where E/ Al is the concentration ratio of element, E, to the reference
metal, Al. Al was selected as the reference element to calculate the annual
and seasonal EF. Several other researchers also used Al as the reference
element (Birmili et al., 2006; Khan et al., 2010a; Sun et al., 2006).
Chester et al. (2000), Cheung et al. (2012), Khan et al. (2010a), Mohd Tahir
et al. (2013), and Torfs and Van Grieken (1997) proposed a EF cut-off of 10 to
differentiate between crustal and natural and anthropogenic origins of heavy
metals. Thus, we consider EF = 10 as the cut-off point. Therefore, a good
number of the metals (Zn, Cr, Rb, Be, V, Fe, Ca, Co, Sr, Pb, As, and Bi) in
PM2.5 in this study can be assumed to originate from anthropogenic
sources (Fig. 5a). These heavy metals were not natural or from the Earth's
crust. No seasonal differences were observed in the EF of the heavy metals.
Mass closure model
A study by Harrison et al. (2003) introduced a PMC model for the source
apportionment of particulate matter, which is the basis for this study. The
variables were grouped into the following four sub-classes: (i) mineral
matter (MIN), (ii) sea salts (SS), (iii) secondary inorganic aerosol (SIA),
(iv) trace elements (TE), and (v) undefined (UD). MIN is derived from the sum
of Al, Mg, K, Ca, and Fe multiplied by the appropriate factors to convert
them into their corresponding oxides as described by the following Eq. (2).
Ca was multiplied by a factor of 1.95 to account for CaO and CaCO3 as
this metal is assumed to be present in these two forms (Remoundaki et al.,
2013; Sillanpää et al., 2006; Terzi et al., 2010)
MIN=1.89Al+1.66Mg+1.21K+1.95Ca+1.43Fe.
The contribution of SS was estimated by assuming that soluble Na+ in
PM2.5 samples originated solely from the marine source and is based on
the composition of seawater, ignoring potential atmospheric transformation
(Seinfeld and Pandis, 2012). Following Terzi et al. (2010), the composition
of sea salt comprised of the following Eq. (3)
SS=Na++ss-Cl-+ss-Mg2++ss-K++ss-Ca2++ss-SO42-,
where ss-Cl-= 1.8*Na+, ss-Mg2+= 0.12*Na+,
ss-K+= 0.036*Na+, ss-Ca2+= 0.038*Na+, and
ss-SO42-= 0.252*Na+. Meanwhile, SIA can be estimated by
the sum of non-sea-salt sulfate (nss-SO42-), NO3- and
NH4+ as explained by Remoundaki et al. (2013) and Terzi et al. (2010)
with the following Eq. (4)
SIA=nss-SO42-+NO3-+NH4+.
Finally, TE is calculated by the sum of rest of the metals analysed in this
study and UD represents unidentified gravimetric mass of PM2.5.
Therefore, the overall mass closure equation applied in this work can be
expressed as the following Eq. (5)
PM2.5MC=MIN1.89Al+1.66Mg+1.21K+1.95Ca+1.43Fe+SSNa++ss-Cl-+ss-Mg2++ss-K++ss-Ca2++ss-SO42-+SIAnss-SO42-+NO3-+NH4++TE+UD.
Source apportionment of PM2.5 using PMF
Source apportionment of PM2.5 was conducted using the US EPA PMF 5.0
model of the United States Environmental Protection Agency (US EPA) as
suggested by Norris et al. (2014). The PMF model is a mathematical
factor-based receptor model that interprets source types with a robust
uncertainty estimate. Two sets of data were run through the PMF model: (i) concentration and (ii) uncertainty. The concentration of each element was
pretreated and validated based on the outliers, missing values and/or values
below MDL. In particular, variables with outliers were excluded. Species
with concentrations below MDL were replaced with the half of the MDL
(Baumann et al., 2008; Polissar et al., 1998a, b). The
uncertainty value of each variable of each sample was calculated following
the empirical formula Eq. (6)
σij=0.01Xij+Xj‾,
where σij is the estimated measurement error
for jth species in the ith sample, Xij is
the observed elements concentration, and Xj‾ is the mean value. The
factor 0.01 was determined through trial and error procedures following by
Ogulei et al. (2006a). Thus, the measurement of uncertainty
(Sij) can be computed with Eq. (7) as applied by
Chueinta et al. (2000):
Sij=σij+CXij,
where σij the estimation of measurement error
(Eq. 6) and C is a constant. In this study, we used a value of 0.4 for C,
which, according to Ogulei et al. (2006b), produced the best Q value as it
is the closest to theoretical value and physically interpretable results.
Other main researchers have also applied this procedure for the calculation
of uncertainty (Harrison et al., 2011; Hedberg et al., 2005; Khan et al.,
2015b). An additional 5 % uncertainty was added to cover any
methodological errors during the preparation of filter papers, gravimetric
mass measurements, and preparing the calibration curves.
Initially, PMF factors were resolved using the numbers of 20 runs with a seed
value of 9. The number of factors was changed to optimize the goodness-of-fit
parameter of Q over the theoretical Q. Five factors were decided upon based
on the lowest Q (Robust) and Q (True) value of 180.26 with the Q
(true) /Qexp value of 0.50 after 604 computational steps and the
convergence of the PMF results. The Q/Qexp ratio for most of the
variables was < 5 to 0.92,, which indicates that the Q values were
very similar to the expected value. Some of the variables, however, showed a
ratio of 0.5 because the computed Q values were smaller than the expected
Q values. A study by Brown et al. (2012) described this discrepancy as
contributing to the increase of global uncertainty. However, the sharp drop
for PM2.5 mass ratio (0.03) was due to the down-weighting of the signal
to noise (S/N) values. To show the stability of the results, we estimated
the error of the concentration for each variable using bootstrap (BS),
displacement (DISP), and a combination of BS-DISP. A comparison of the error
estimates with base model runs is demonstrated in Fig. S1 in the Supplement.
The five-factor results were relatively stable with meaningful physical
interpretation and satisfactorily comparable with the bootstrap analysis. Fe
and Cr were reported as outliers and therefore excluded in the calculation.
Referring to Table 2, the overall PM2.5 concentration is well explained
within ±10 % by the PMF 5.0 considering the fpeak=0.
Health risk assessment of PM2.5 and associated various
sources
The human health risk posed by heavy metals may occur through inhalation of
PM2.5. We applied the US EPA supplemented guidance to estimate the risk
posed by heavy metals in PM2.5 mass concentration and their various
sources. As part of the health risk assessment (HRA), we considered lifetime non-carcinogenic and
carcinogenic risk. US EPA (2011) describes the exposure concentration for inhalation (ECinh) by
the following equation
ECinh=C×ET×EF×EDATn,
where C is the concentration of metals in PM2.5 estimated for each
source with µg m-3 unit for the estimation of ECinh; EF is the
exposure frequency (151 days year-1) representing July, August,
September, January, and February; ED is exposure duration (24 years for
adult); BW is the average body weight (70 kg for adult); ET is the exposure
time (h day-1); and ATn is the average time (ATn = ED ×365 days ×24 h day-1 for non-carcinogenic and ATn = 70 year ×365 days year-1×24 h day-1 for carcinogenic risk). ED, BW, and AT values
are based on the study by Hu et al. (2012).
The contribution of sources to PM2.5 and the compositions
estimated by PMF 5.0 model.
Variables
Mineral/road dust
Motor vehicle emissions/
Nitrate aerosol
Coal burning
Marine/sulfate aerosol
biomass burning
(mean ± SDa)
(mean ± SD)
(mean ± SD)
(mean ± SD)
(mean ± SD)
ng m-3
%
ng m-3
%
ng m-3
%
ng m-3
%
ng m-3
%
PM2.5
3.17 ± 0.15b
13 ± 1
7.47 ± 1.26b
31 ± 5
4.11 ± 0.47b
17 ± 2
4.60 ± 0.37b
19 ± 2
4.99 ± 0.67b
20 ± 3
Al
42.65 ± 3.17
19 ± 1
45.37 ± 3.85
20 ± 2
69.06 ± 2.45
31 ± 1
29.84 ± 1.73
13 ± 1
36.71 ± 2.51
16 ± 1
Ba
269.3 ± 205.9
22 ± 17
32.85 ± 146.9
3 ± 14
166.9 ± 71.90
13 ± 6
661.7 ± 246.9
52 ± 19
117.8 ± 116.8
10 ± 11
Ca
445.1 ± 32.07
28 ± 2
235.43 ± 37.76
15 ± 2
350.6 ± 35.82
22 ± 2
303.4 ± 30.14
19 ± 2
267.1 ± 26.00
17 ± 2
Mg
92.36 ± 5.02
52 ± 3
47.59 ± 21.66
27 ± 12
25.43 ± 12.33
14 ± 7
10.32 ± 6.11
6 ± 3
1.23 ± 5.50
1 ± 3
Pb
3.56 ± 0.79
20 ± 4
9.11 ± 2.32
50 ± 13
0.58 ± 0.40
3 ± 2
3.61 ± 0.42
20 ± 2
1.25 ± 1.36
7 ± 8
Zn
157.7 ± 17.09
48 ± 5
45.66 ± 30.11
14 ± 9
60.74 ± 21.74
18 ± 7
50.56 ± 19.46
15 ± 6
14.33 ± 8.53
4 ± 3
As
0.18 ± .35
4 ± 7
1.76 ± 0.55
41 ± 14
0.05 ± 0.10
1 ± 2
2.37 ± 0.65
53 ± 13
0.05 ± 0.22
1 ± 6
Cd
0.03 ± 0.01
6 ± 2
0.22 ± 0.06
44 ± 12
0.07 ± 0.02
13 ± 3
0.13 ± 0.02
27 ± 3
0.05 ± 0.02
10 ± 5
Cu
12.38 ± 0.59
50 ± 2
3.55 ± 2.37
14 ± 10
4.20 ± 1.45
17 ± 6
3.27 ± 1.16
13 ± 5
1.45 ± 0.42
6 ± 2
Mn
–
–
0.84 ± 0.27
25 ± 8
1.16 ± 0.19
35 ± 6
0.62 ± 0.26
18 ± 7
0.71 ± 0.09
21 ± 3
Ni
7.21 ± 0.50
48 ± 4
2.79 ± 1.18
18 ± 8
1.70 ± 0.77
11 ± 5
3.11 ± 0.80
20 ± 5
0.36 ± 0.23
2 ± 2
Rb
1.33 ± 0.07
38 ± 2
0.76 ± 0.20
22 ± 6
0.45 ± 0.14
13 ± 4
0.67 ± 0.10
19 ± 3
0.26 ± 0.04
7 ± 1
Se
0.05 ± 0.01
8 ± 2
0.14 ± 0.03
24 ± 6
0.14 ± 0.02
23 ± 3
0.11 ± 0.02
19 ± 3
0.16 ± 0.01
27 ± 1
Sr
8.26 ± 4.51
25 ± 14
1.19 ± 3.18
4 ± 11
4.60 ± 1.80
14 ± 6
15.05 ± 5.05
45 ± 15
3.59 ± 2.40
11 ± 9
V
0.19 ± 0.08
5 ± 2
0.81 ± 0.24
20 ± 6
1.25 ± 0.20
30 ± 6
0.59 ± 0.32
14 ± 7
1.28 ± 0.17
31 ± 4
Na+
88.10 ± 28.60
19 ± 6
17.28 ± 56.76
4 ± 12
120.8 ± 10.99
26 ± 3
7.93 ± 4.69
2 ± 1
234.2 ± 20.31
50 ± 5
NH4+
59.48 ± 30.60
11 ± 6
241.1 ± 61.51
44 ± 11
82.56 ± 18.67
15 ± 4
8.55 ± 16.10
2 ± 3
156.2 ± 48.24
28 ± 8
K+
65.10 ± 18.20
20 ± 6
91.08 ± 16.94
28 ± 5
50.69 ± 6.14
16 ± 2
9.53 ± 3.42
3 ± 1
108.4 ± 16.41
33 ± 5
Ca2+
99.79 ± 3.69
42 ± 1
50.52 ± 18.74
21 ± 8
47.25 ± 9.79
20 ± 4
12.39 ± 6.67
5 ± 3
26.39 ± 4.03
11 ± 2
Mg2+
8.18 ± 1.46
23 ± 4
6.96 ± 1.06
19 ± 3
9.27 ± 0.31
26 ± 1
1.92 ± 0.38
5 ± 1
9.72 ± 0.32
27 ± 1
Cl-
15.88 ± 4.06
36 ± 10
1.83 ± 2.95
4 ± 8
–
–
5.90 ± 0.73
13 ± 2
20.58 ± 6.45
46 ± 13
NO3-
90.86 ± 36.16
11 ± 4
6.66 ± 21.39
1 ± 2
611.0 ± 27.43
75 ± 3
5.75 ± 16.39
1 ± 2
103.4 ± 53.25
13 ± 7
SO42-
307.2 ± 142.1
21 ± 10
58.02 ± 152.1
4 ± 11
74.23 ± 33.91
5 ± 2
89.77 ± 10.15
6 ± 1
935.1 ± 112.5
64 ± 7
a SD: standard deviation; b unit: µg m-3;
“–”: no data.
Further, we examined the non-carcinogenic risk (presented by the hazard
quotient, HQ) and lifetime carcinogenic risk (LCR) of selected heavy metals
as classified by the International Agency for Research on Cancer (IARC). The
following equations were involved for the calculation of HQ and LCR:
HQ=ECinhRfCi×1000µgm-3LCR=IUR×ECinh,
where RfCi is the inhalation reference concentration
(mg m-3), and
IUR is the inhalation unit risk ((µg m-3)-1). The
non-carcinogenic risk or HQ represents the observable health effects from
exposure to the PM2.5 based on the dose–response relationship
principles. The cut-off point for significant health risks to the exposed
population is HQ > 1. The carcinogenic risk refers to a person's
chance of developing cancer from exposure to any carcinogenic agent. LCR
represents the excess lifetime cancer risk described in terms of
the probability that an exposed individual will develop cancer because of
that exposure by age 70 as defined by US EPA Risk Communication
(http://www.epa.gov/superfund/community/pdfs/toolkit/risk_communicati-onattachment6.pdf). The carcinogenic risk from the lifetime
exposure of those hazardous metals is regulated by the acceptable or
tolerance level (1×10-6) set by the US EPA, which corresponds
to lifetime exposure to an unpolluted environment (Satsangi et al., 2014).
Results and discussions
Concentration of PM2.5 and its chemical composition
Table 1 summarizes the statistics from the SW monsoon, the NE monsoon and
overall concentrations of PM2.5, heavy metals and major ions.
Overall, the 24 h average values of PM2.5 (avg = 25.13 µg m-3) in the study area are slightly higher than that of the WHO 24 h
guideline (25 µg m-3) but lower than that of 24 h US EPA National
Ambient Air Quality Standard (NAAQS) (35 µg m-3). Of the samples
taken during the day, 48 % exceeded the WHO 24 h guideline while 19 % of
them exceeded the US EPA 24 h NAAQS for PM2.5 (currently, Malaysia has
no set guidelines for PM2.5). If we compare the PM2.5 overall
value of 25.13 µg m-3 with the yearly mean of US EPA NAAQS (15 µg m-3), WHO (10 µg m-3),
European Union (EU) (25 µg m-3), or Department of Environment (DoE) (Australia) (8 µg m-3), the
concentration of PM2.5 is much higher with respect the guideline set by
all regulatory bodies. The average value of PM2.5 during the NE monsoon
was slightly higher than the SW monsoon. During the south-west monsoon
season, PM2.5 was mainly carried by the prevailing south-west wind
from the Sumatra Island of Indonesia, which is located at the south-west
quadrant of the Southeast Asia (SEA) region. On the other hand, during the north-east monsoon
season, the PM2.5 sources can be traced back to the Chinese mainland,
Indochina region, and the Philippines. This is due to the prevailing
north-east wind transporting PM2.5 from these locations to the
tropical region of SEA. The Student t test for paired independent samples showed that the
mean during these two monsoons varies insignificantly (t= 1.19, p > 0.05). However, the
monsoonal changes in this region as displayed in Fig. 3, showed that air
masses of different origins transport different pollutants to the area. The
back trajectory plots showed that there were high numbers of biomass fire
hotspots during both seasons but from different regions (Fig. 3). The period
of June to September is the dry season each year in Malaysia and Sumatra of
Indonesia. During this dry season, biomass fire hotspots are densely located
in this area due to the burning of agricultural waste and forest fires.
Several other researchers also reported the high number of biomass fire-related hotspots to these regions (Khan et al., 2015c; Sahani et al., 2014).
Time series of 24 h averages of PM2.5, wind speed (m s-1),
and yearly daily mean of the boundary layer height (BLH) over the region of Peninsular Malaysian.
On the other hand, December to March is usually the wet season in Malaysia.
However, the backward trajectories showed that air masses were transported
from Mainland China and neighbouring regions. In Mainland China and
neighbouring regions, this is dry season. During the dry season in this
region there are a lot of fires, as reported by Zhang et al. (2015) and Ho
et al. (2014), and this influences the pollution of air masses transported
to the present location. This scenario of biomass fire hotspots is commonly
noticed in these two seasons. In past years Malaysia and Singapore have
experienced intensified haze episodes in this particular season, e.g. 1997,
2005, 2013, and 2015. A study of this area by Kanniah et al. (2014) observed
that during the dry season (June to September) aerosols mainly originated
from the west and south-west (i.e. Sumatra, Indonesia), while during the wet
season (November to March) aerosols were mostly associated with the NE
monsoon winds coming from the South China Sea. Also, the variability of BLH
and wind speed (WS) were able to influence the concentration of the pollutants at a
particular location. Figure 4 shows the day to day variation of BLH and WS
with respect to the 24 h average of PM2.5 concentration. From the plot,
it is revealed that the daily average PM2.5 concentration is inversely
proportional to the BLH. Therefore, while factors such as traffic volume,
industrial emissions, power plants, land use, and population size can alter
the concentration of PM2.5, meteorological factors, which govern the day
to day variation of BLH in Peninsular Malaysia, might play a crucial role
too. These meteorological factors can include strong local convection, which
is a very common meteorological feature in this region, and also the
movement of air via a land–sea breeze due to the sea surrounding Peninsular
Malaysia. A study by Lelieveld et al. (2001) reported that strong convection
can ventilate the daily BLH. The small expansion of BLH that was observed
during NE monsoon was most likely due to the higher magnitude of WS to
Peninsular Malaysia during this season, as demonstrated in Fig. 2.
In comparison, our results of PM2.5 here on the west coast of
Peninsular Malaysia (avg = 25.13 µg m-3) are higher compared
to the east coast of Peninsular Malaysia at 14.3 µg m-3 (Mohd
Tahir et al., 2013). This PM2.5 concentration in this study area was
similar to the annual concentration of PM2.5 measured in Petaling Jaya,
Kuala Lumpur (26.85 µg m-3) by Rahman et al. (2011), Petaling
Jaya (33 µg m-3) and Gombak (28 µg m-3) by Keywood
et al. (2003) and Singapore (27.2 µg m-3) as reported by
Balasubramanian et al. (2003). The yearly mean value of PM2.5 in the
Bandung urban area and suburban location in Lembang of Indonesia are 14.03
and 11.88 µg m-3, respectively (Santoso et al., 2008), which are
much lower concentration compared to the this study. However, Lestari and
Mauliadi (2009) reported that the PM2.5 concentration of 43.5 µg m-3 in the Bandung city, Indonesia, was about 1.7 times larger than
that of the current location and by Budhavant et al. (2015) showed 19 µg m-3 in Male, Maldives (urban), which is lower as well compared
to this study. A comparative study conducted in Bangkok (34 µg m-3), Beijing (136 µg m-3),
Chennai (44 µg m-3), Bandung (45.5 µg m-3), Manila (43.5 µg m-3), and Hanoi (78.5 µg m-3) showed consistently higher
PM2.5 pollution in the Southeast and South Asian cities as compared to
this study (Kim Oanh et al., 2006). From Table 1, it can be seen that the
highest concentration of anions species was found for SO42-
followed by NO3-. A study by Zhang et al. (2012) suggested that
the photochemical conversion of SO2 to H2SO4 is the main
reason for the changes of sulfate concentration in PM2.5 and that
higher temperatures reduce the nitrate concentration by the partitioning of
nitrate into the gas phase. However, lower temperatures and a stable
atmosphere favours the formation of NO3- aerosol reacting with
NH4+, i.e. shifting the gas phase nitrate into the particle phase
(Mariani and de Mello, 2007). The formation of NH4NO3 normally
occurs at high humidity with lower temperatures (Morales and Leiva, 2006).
The average molar ratios of SO42- to NO3- were 6.0 with
a range of 0.16–38.24, which suggests that the sulfate aerosol is more
dominant over the nitrate aerosol and may have been transported from
trans-boundary sources. A similar observation was found in a study in the UK
by Abdalmogith and Harrison (2006). This ion balance ratio indicates the
possible sources of aerosol, and stationary sources dominate over the mobile
sources as explained by Arimoto et al. (1996). The average ratio of
SO42- to NH4+ was 1.28, which is much higher than the
ratio of NO3- to NH4+ (0.63), confirming that the
sulfate aerosol is more stable in tropical conditions as compared to the
nitrate aerosol. A study by Maenhaut et al. (2008) described a similar
observation. The formation of ammonium sulfate is favoured in the fine
fraction (Khan et al., 2010b). For the cations, the highest concentration
was NH4+ followed by Na+. However, the average molar ratio of
Cl- to Na+ did not reflect the seawater ratio. “Cl loss” may be
the cause of the drop in Cl- to Na+ ratio. Boreddy et al. (2014)
also noticed a chlorine depletion due to atmospheric processing in the
western North Pacific. According to Finlayson-Pitts and Pitts (2000),
sulfuric and nitric acids have a tendency to react rapidly with NaCl, the
major component of sea salt particles, to produce gaseous HCl under
50–100 % relative humidity conditions. For heavy metals, the predominant
metal reported was Fe with concentrations in the range 2171–4567 ng m-3. Ca showed the second-highest concentrations with the concentration
range of below MDL – 3150 ng m-3. A study by Yin and Harrison (2008)
suggested that Fe originates from non-traffic sources and that iron and
calcium are released into ambient air through the resuspension of surface
dust. Among other heavy metals of particular health concern, the average
concentrations of As, Pb, Cd, Mn, Ni, V, and Cr were 5.76, 21.84, 0.54, 4.03,
17.24, 5.13, and 107.68 ng m-3, respectively. The As concentration was
nearly equal to the WHO and US EPA guideline values of 6.6 and 6 ng m-3, respectively. Therefore, As may be of significant health concern.
The concentrations of other hazardous metals were well below the WHO and
European commission guidelines. The EF reveals that all metals of PM2.5
can be assumed to originate from anthropogenic sources with no seasonal
differences observed (Fig. 5a).
Mass closure model
The PM2.5 was reconstructed by the use of a PMC model (Harrison et al.,
2003). Employing the mass closure model outlined in the previous section,
the four major classes of chemical components contributing to PM2.5
were (i) MIN, (ii) SIA, (iii) SS, (iv) TE, and UD. As shown in Fig. 5c, the
overall reconstructed masses of MIN, SIA, SS, TE, and UD were 8970, 2841,
1727, 626.2, and 11511 ng m-3, respectively. MIN is released from soil
or crustal sources and represents the oxide form of the metals (Remoundaki
et al., 2013; Sillanpää et al., 2006; Terzi et al., 2010). The MIN
component comprises 35 % of the PM2.5 concentration. SIA, which
accounts for 11 %, is comprised of the most abundant secondary ions
(nss-SO42-, NO3- and NH4+). These are formed
in the atmosphere from the precursor gases (SO2, NH3 and NOx)
through a gas-to-particle conversion (Sillanpää et al., 2006) and
therefore are assumed to be in the form of (NH4)2SO4 and
NH4NO3 in the aerosol phase (Joseph et al., 2012). It is important
to mention that the behaviour of the formation pattern of the SIA in this
Southeast Asia region may differ from other regions due to the nearly
constant temperature throughout the year. SS and TE accounted for 7 and
2 % of the PM2.5. The UD, undefined fraction, accounted for 45 % of
PM2.5.
The average value of reconstructed PM2.5 by mass closure (MC) is
14.12 ± 4.32 µg m-3 with a minimum of 6.70 µg m-3 and a maximum of 24.19 µg m-3. On the other hand, the measured
PM2.5 determined gravimetrically by HVS was 25.13 ± 9.21 µg m-3 with a range of 7.01 to 42.28 µg m-3. A correlation plot
of PM2.5 (MC) and measured PM2.5 (HVS) is shown in Fig. 5e. The
correlation shows a good fit (r2=0.98) with a slope of 0.46 and an
intercept of 1.93. The results of the fit parameters suggest that the
PM2.5 mass (MC) concentration was underestimated compared to PM2.5
(HVS). The reported result of the mass closure model is based on the
analysed chemical components of filter samples (∼ 55 %). As
described in the mass closure, a large portion of PM2.5 mass
(∼ 45 %) was left unidentified; this unidentified component
is believed to be the organics or carbonaceous species. Elemental carbon
(EC), organic carbon (OC) and water-soluble organics were not measured due
to the lack of instrumentation. Other possible reasons for the un-identified
portion are (i) unaccounted for mineral oxides as they are abundant in
PM2.5, and (ii) water associated with salts.
(a) Enrichment factor (EF) of heavy metals in PM2.5,
(b) correlation plot of nss-K+ and total K+, (c) reconstructed mass
concentration of PM2.5 by mass closure model, (d) correlation plot of
K+ and Na+, and (e) correlation plot of estimated PM2.5 (MC)
and measured PM2.5 (HVS).
During the SW monsoon, the UD showed the higher concentrations; this can be
explained by the annual biomass haze episodes experienced in this area.
Thus, a large proportion of the UD of PM2.5 is probably formed from the
organic fraction. Such findings are consistent with a study conducted by
Abas and Simoneit (1996), which also found that the concentrations of organic
compounds observed were greater during the haze episodes than any other
periods in a year, and that some of them are suspected to be transported
from trans-boundary sources.
The seasonal variability of the results obtained from the mass closure model
is shown in Fig. 5c. The reconstructed masses of MIN, SIA, and SS were higher
in the NE than the SW monsoon. These haze events were very likely caused by
the slash-and-burn activities practiced by the agriculture industries, and
the occurrence of forest fires during this dry season. The regional
trans-boundary pollution during the NE and SW monsoon is the underlying
reason for the change in the chemical component concentrations as well as
the overall PM2.5.
(a) The source profiles of PM2.5 by positive matrix factorization
model and (b) comparison of PM2.5 (PMF) and PM2.5 (HVS).
Identification and apportionment of PM2.5 sources
Using US EPA PMF 5.0, the five identified sources of PM2.5 were (i) mineral and road dust, (ii) motor vehicle emissions and
biomass burning, (iii) nitrate aerosol, (iv) coal burning, and (v) marine and sulfate aerosol. Each of
the source profiles is shown in Fig. 6a, which demonstrates the concentration
and percentage of the variables to each factor. The reported PMF analysis is
based on the chemical components of filter samples. As described in the mass
closure, a large portion of the PM2.5 mass fraction (about 45 %) was
not apportioned. In the PMF 5.0 procedure, the contributions of five factors
were estimated and then the integrated contribution of the five factors was
regressed over the measured PM2.5 (HVS). The regression fit line was
forced through the origin. Thus, our regression of the PM2.5 (PMF) and
PM2.5 (HVS) showed that the PM2.5 had been reproduced by PMF 5.0
with an error of less than 10 % and the correlation of PM2.5 (PMF)
and PM2.5 (HVS) showed a strong and significant correlation (slope = 0.91, r2=0.88, p < 0.01) (Fig. 6b). To evaluate the results of
the PMF model, the regression between predicted and observed data for each
variable is shown during the operation. A linear correlation between the
predicted and measured mass represents the goodness-of-fit of linear
regression. Our values strongly suggested that the five identified sources
could be readily interpreted.
Factor component one: the predominant tracers are Mg, Zn, Cu, Ni, and
Ca2+. The mineral or natural fugitive dust component is identified
based on the presence of Mg (52 % of the Mg mass), Ca2+ (42 % of
Ca2+ mass), Ca (28 % of Ca mass), and Al (19 % of Al mass), as shown
in Table 2. Many other researchers cite these metals as markers for a
mineral dust source (Dall'Osto et al., 2013; Moreno et al., 2013; Mustaffa
et al., 2014; Viana et al., 2008; Waked et al., 2014). The possible cause of
the mineral dust is the rapid development activities of things such as construction,
renovation of road surface, etc., around this suburban region. Airborne soil
and construction material are the key sources of mineral dust (Dai et al.,
2013; Gugamsetty et al., 2012; Huang et al., 2014). Cu, Zn, and Ba are
associated with road dust due to the release of these metal markers from
cars and from non-exhaust sources (Amato et al., 2011). Several studies
identified that Cu is released from brake wear or the brake pads/tailpipes
of cars (Wåhlin et al., 2006) while Zn originates from tire wear
(Dall'Osto et al., 2013) and additives in cars as lubricant (Ålander et
al., 2005). A study by Wang and Hopke (2013) suggested that Ni was emitted
from gasoline engine and road dust sources. Ni (48 % of Ni mass) and V
(5 % of V mass) are moderately presented in this factor component, which
shows the existence of heavy lubricating oil combustion (Amato et al.,
2011). The average contribution of mineral or natural fugitive and road dust
sources to the PM2.5 was 3.17 µg m-3 or 13 %.
Factor component two: contains substantial Pb, NH4+, and K+.
Motor vehicle emissions and biomass burning sources accounted for 7.47 µg m-3 or 31 % of the total PM2.5 concentration, which makes
these the largest sources contributing to the PM2.5 concentration. Pb
along with the moderately enriched metals As, Cd, Zn, Ni, and V (Fig. 5a, refer
to previous section for detail), represents a motor vehicle emission source
(Wu et al., 2007). The brake wear dust of motor vehicles contains Pb (Garg
et al., 2000). A study by Begum et al. (2010) conducted in Dhaka and by
Santoso et al. (2013) at roadsides in Jakarta defined Pb in PM2.5
releasing from the pre-existing road dust by PMF. Choi et al. (2013) also
introduced Pb in PM2.5 as a tracer for the motor vehicle source. Zn is
released from the wear and tear of tyres (Srimuruganandam and Shiva
Nagendra, 2012). Further, Zn in PM2.5 appeared to have a motor vehicle
source as resolved by PMF, due to its use as fuel detergent and anti-wear
additive (Brown et al., 2007). Ni and V were widely reported in the
literature as markers for the combustion of engine oil or residual oil
combustion (Gugamsetty et al., 2012; Han et al., 2006; Huang et al., 2014;
Yu et al., 2013). Pb is no longer used as an additive in gasoline fuel.
Thus, the Pb does not reflect the emissions from engine combustion but does
reflect those from a non-exhaust traffic source. A study conducted by Rahman
et al. (2011) in Kuala Lumpur investigating Pb in PM2.5 found that it
originated from the soil dust source, indicating the influence of road dust.
Also, coal combustion is a predominant source of Pb (Tao et al., 2014). The
K+ ion has been widely cited in the literature as an excellent tracer
representing a wood or biomass burning source (Dall'Osto et al., 2013; Kim
and Hopke, 2007; Mustaffa et al., 2014; Wahid et al., 2013). The biomass
burning source is generally comprised of either wood burning as residential
fuel, agriculture residue/waste, and/or wild forest fires. In Kuala Lumpur,
the biomass burning source was described due to the presence of K from
PM2.5 measured by particle-induced X-ray emission (Rahman et al.,
2011). During the episode of biomass burning in Chengu, China, K+ and
other related tracers in PM2.5 were increased by a factor of 2–7. In
this suburban region, the smoke emissions released due to the burning of
wheat straw, rape straw, and other biomass fuel for domestic cooking or
heating purposes (Tao et al., 2013). K+ is also mainly emitted from
biomass burning in the suburb of Shenzhen, China (Dai et al., 2013),
Beijing, China (Yu et al., 2013; Zhang et al., 2013), and Colombo, Sri Lanka
(Seneviratne et al., 2011). In Seoul, Korea, biomass burning is
characterized by the presence of K and other related markers in PM2.5.
The character of burning in this East Asian city is typically post-harvest
field burning, biofuel burning for heating and cooking as well as forest
fire from the outside of the city (Heo et al., 2009). Thus, the local and
regional transport of smoke from the burning sources contribute to this
factor. Hong Kong experiences the influence of biomass burning in PM2.5
due to its trans-boundary origin (Huang et al., 2014). During the sampling
period in the SW monsoon, the MODIS detected a very high number of fire
counts over the Sumatra Island. In this monsoon season, the wind will
consistently travel from the south-west direction, bringing air masses from
these burning areas to Peninsular Malaysia. During the NE monsoon, on the
other hand, the wind will travel from the north-east direction, bringing air
masses from the China mainland, Indochina, and the Philippines to Peninsular
Malaysia. In this period of time, a high density of fire locations were
found on the Indo-China and China mainland. Zhang et al. (2015) demonstrate
that during the dry season there is important biomass burning activity in
the Pearl River Delta (China), which can result in trans-border transport
and a regional scale character of biomass burning. Therefore, under the
north-east monsoonal regime it is possible that outflow from that area can
maybe influence the specific area. A study by Streets et al. (2003)
estimated that China contributes 25 % to the total biomass burning in Asia
and showed a good agreement between national estimate of biomass burning and
adjusted fire count. Yang et al. (2013) applied spatial–temporal features of
fire counts and observed that the study area of Heilongjiang Province, China
was seriously affected by forest fires during 2000–2011. Reid et al. (2013)
reported a high intensity of fire counts in Vietnam–China region in April
and in Indonesia during September. Khan et al. (2015a) also reported a high
density of fire locations in Thailand, Vietnam, and Laos during February and
Sahani et al. (2014) reported many in the same regions during
June-September. The biomass burning is the dominant source of trace gas and
particulate matter and the fire emissions are mainly concentrated in
Indonesia, Thailand, Myanmar, and Cambodia (Chang and Song, 2010). Further, a
comparison of nss-K+ with the respective total K+ is shown in
Fig. 5b. The correlation of nss-K+ as a function of total K+ showed
a strong correlation coefficient (r2=0.95), which suggests that
K+ can be used as a biomass tracer. K+ may also be emitted from
local fire sources. Additionally, the molar equivalent of K+ and
Na+, as shown in Fig. 5c, demonstrated significant correlation (r2=0.70) with a slope value of 0.34, which is much higher compared to
0.0225–0.230 and 0.0218, reported by Wilson (1975) and Hara et al. (2012),
respectively. The higher molar ratio of K+ and Na+ indicates that
at the current location, Na+ depletion was high and the K+ might
also release from other dominant sources. Additional significant sources of
K+, which may attribute to the mass, are soil dust, sea salt, vegetation,
and meat cooking (Zhang et al., 2010).
Factor component three: this factor is mainly dominated by the concentration
of the nitrate ion (75 % of NO3- mass) suggesting that this
source is strongly related to the formation of nitrate aerosol.
NO3- is mainly formed from the conversion of NOx, which is
emitted from the exhaust of motor vehicles (Dai et al., 2013). Huang et al. (2014) also identified a nitrate source in PM2.5 by the use of PMF in
suburban areas of Hong Kong. In Beijing, a nitrate source appeared in
PM2.5 when source apportionment was performed by PMF (Song et al., 2006).
This source is also contributed to by the small amount of Al, Mn and
Ca2+. Overall, it accounted for 4.11 µg m-3 or about 17 %
of the PM2.5 concentration.
Factor component four: this fourth source has an abundance of As, Ba, and Sr
(Se moderately contributed) and thus is classified as coal burning source.
As constitutes the most to this fourth component at 53 % (of As mass),
which gives an indication that this source is related to coal
combustion. In Malaysia, several power plants are operating on the west
coast of Peninsular Malaysia, e.g. Port Dickson, Kapar, and Manjung. The
power plants located at Port Dickson and Kapar are about 50 km away from the
sampling station. These plants use coal as the raw material to generate
electric power. Other researchers have also used As a tracer for the coal
burning source (Moreno et al., 2013) and As and Se by Meij and te Winkel
(2007) and Querol et al. (1995). As and Se are categorized as of great concern
and Ba and Sr are of moderate environmental concern in the utilization of
coal, as reported by Vejahati et al. (2010). However, Ba is an indicator of
brake wear and tear from motor vehicles (Gietl et al., 2010). Overall, the
coal burning source accounts for 4.60 µg m-3 or about 19 % of
PM2.5.
Factor component five: this component features Na+ (50 % of Na+
mass), Cl- (46 % of Cl- mass), and sulfate (64 % of
SO42- mass) suggesting the presence of marine as well as sulfate
aerosol. Begum et al. (2010) identified sea salt in PM2.5 by PMF in
Dhaka, based on the appearance of Na and Cl. Choi et al. (2013) defined a
sea salt source in Seoul, Korea, due to the high contribution of Na+ and
Cl- in PM2.5. Several other studies in East, Southeast and South
Asia assigned a sea salt source in PM2.5 considering Na+ and
Cl- from the model output of PMF (Lee et al., 1999; Santoso et al.,
2008, 2013; Seneviratne et al., 2011). For sulfate, it shows
that nss-SO42- contributed 93 % to the total sulfate
concentration while ss-SO42- accounted for only 6 %. Therefore,
the sulfate aerosol in PM2.5 is released as a product from the
photochemical conversion of SO2, which mainly originates from
anthropogenic, large point sources as observed by Heo et al. (2009) in Seoul,
South Korea. A secondary sulfate source in PM2.5 was also identified by
Huang et al. (2014) in a suburban area of Hong Kong and by Song et al. (2006) in Beijing. The marine and sulfate aerosol, as the final identified
source, accounts for 4.99 µg m-3 or about 20 % of the total
PM2.5 concentration. A study by Kim and Hopke (2007) defined a sea salt
source by the high concentration of Na+ and Cl-, while sulfate
sources are based on the high concentration of sulfate. The secondary
aerosol fraction is an important source worldwide, which is also the case
here. It generally constitutes a predominant portion of PM2.5, which
splits into two modes, i.e. the nitrate-rich and sulfate-rich factors.
Studies by Chen et al. (2007) and McGinnis et al. (2014) also identified the
major contribution of the secondary aerosol fraction to PM2.5.
Hazard quotient (HQ) and lifetime carcinogenic risk (LCR) for
selected heavy metals in PM2.5 associated with sources.
Inhalation
Mineral/road dust
Motor vehicle/biomass
Coal burning
PM2.5
HQ
LCR
HQ
LCR
HQ
LCR
HQ
LCR
Pb
–
4.0 × 10-8
–
1.0 × 10-7
–
4.1 × 10-8
–
2.5 × 10-7
As
1.8 × 10-3
1.1 × 10-7
4.9 × 10-2
1.1 × 10-6
6.6 × 10-2
1.5 × 10-6
15.9 × 10-2
3.5 × 10-6
Cd
4.6 × 10-4
8.2 × 10-9
9.1 × 10-3
5.6 × 10-8
5.5 × 10-3
3.4 × 10-8
2.2 × 10-2
1.4 × 10-7
Cu
–
–
–
–
–
–
–
–
Mn
–
–
7.0 × 10-3
–
5.1 × 10-3
–
3.3 × 10-2
–
Zn
–
–
–
–
–
–
–
–
Ni
2.0 × 10-2
2.5 × 10-7
2.3 × 10-2
9.5 × 10-8
2.6 × 10-2
1.1 × 10-7
14.3 × 10-2
5.9 × 10-7
THR (HI
and LCR)
2.3 × 10-2
1.6 × 10-7
8.8 × 10-2
1.2 × 10-6
10.2 × 10-2
1.5 × 10-6
35.7 × 10-2
3.9 × 10-6
Pb: Pb (acetate); As: As (Inorganic); Cd: Cd (Diet); Mn: Mn (Diet); Zn: Zn (Metallic); Ni: Ni (Refinery Dust); “–”: no
data; THR: total health risk; HI: hazard index; LCR: lifetime cancer risk.
Health risk implications
Table 3 shows the non-carcinogenic (represented as HQ) and carcinogenic
risks posed by several selected metals (Pb, As, Cd, Cu, Mn, Zn, and Ni) in
PM2.5 mass concentration through inhalation exposure associated with
sources. The HQ values for As and Ni in PM2.5 mass concentration are
15.9×10-2 and 14.3×10-2, respectively,
suggesting the non-carcinogenic health risks posed by these metals might be
higher compared to other metals. The HQ for four selected metals (Pb, As, Cd,
and Ni) in PM2.5 mass was the highest in the PM2.5 mass originating
from a coal burning source and the least in PM2.5 originating from a
mineral/road dust source. The cut-off point for significant health risks or
the safe level to the exposed population is HQ > 1. Our results
showed that the sum of HQ for each metal are lower than the safe level (= 1) in PM2.5 mass concentration originating from each source. The sum of
HQ for PM2.5 is 35.7×10-2, which is lower than the HQs
of PM2.5 reported by Hu et al. (2012) in Nanjing, China (2.96); Cao et
al. (2014) in Shanxi Province, China (1.06×10+1); and Taner
et al. (2013) in a non-smoking restaurant in Turkey (4.09). A study by Hu et
al. (2012) reported HQ values for As and Ni in PM2.5 as 4.14×10-1 and 1.73×10-1, respectively, in Nanjing, China.
However, the HQs of PM2.5 estimated after inhalation at two sites in
Nanjing City, China (0.88, Xianlin, and 0.79, Gulou), were close to the safe
level (= 1) according to a study by Y. Li et al. (2015). At two urban
locations in Yangtze River Delta, China, the HQ for Cr in PM2.5 was
within the acceptable limit but higher for Mn (Niu et al., 2015). Although
the HQ calculated for As was the highest, it was below 1; thus, the
non-carcinogenic health risk was estimated to be at a safe level. In
addition, the hazard index (total-hazard quotient) of PM2.5
calculated for the four heavy metals (As, Cd, Mn, Ni) from the different
sources (Table 3) showed an insignificant health risk.
The carcinogenic risks from the carcinogenic heavy metals Pb, As, Cd, and Ni
in PM2.5 are shown in Table 3. Similar to the non-carcinogenic risks,
the lifetime carcinogenic risk level is estimated for PM2.5 mass
concentration and may be contributed to by several heavy metals from
different sources: mineral/road dust, motor vehicle emissions/biomass
burning and coal combustion. The total LCR from
heavy metals in the PM2.5 mass concentration was calculated at
3.9×10-6, which is a significant cancer risk. The main
carcinogenic heavy metal of concern to the health of people at the current
location is As; the other heavy metals (Ni, Pb, and Cd) did not pose a
significant cancer risk. Thus, the LCR from the PM2.5 mass
concentration originating from motor vehicle/biomass and coal burning
sources showed a value of 1×10-6, slightly above the
acceptable cancer risk level as recommended by USEPA, while the total LCR
from PM2.5 mass concentration from all sources was estimated to be
4×10-6, which is also slightly above the acceptable cancer
risk. The carcinogenic risk posed by As (3.66×10-3) in
PM2.5 in Shanxi Province, China (Cao et al., 2014), was higher than the
guideline value set by USEPA. A study by Niu et al. (2015) of
PM2.5-bound metals showed a high cancer risk in Yangtze River Delta,
China (2.47×10-4). A study by Pandey et al. (2013) conducted
in the vicinity of human activities observed that the concentrations of Cd,
Cr, Ni, and Pb in PM2.5 showed higher excess cancer risk (ECR) due to
those particle-bound metals compared to guideline level set by USEPA.
Satsangi et al. (2014) also reported a higher cancer risk from Cr, Ni and Cd
in PM2.5 compared to the USEPA guideline. The integrated carcinogenic
risk of six metals (Cr, As, Co, Pb, Ni, and Cd) in PM2.5 in Tianjin,
China were in the range 3.4×10-3–4.1×10-3,
which is reportedly beyond the tolerance level (Zhang et al., 2014). The
total ECRs based on the average values of As, Cd, Cr, Ni, and Pb in
PM2.5 is 4.34×10-5 in Delhi, India, implying that four
or five people might get cancer out of 100 000 people after exposure to
toxic metals in PM2.5 (Khanna et al., 2015). Our findings showed that
the lifetime cancer risk posed by the exposure of heavy metals in
PM2.5 mass concentration is 3–4 per 1 000 000 people at this
location. This significant cancer risk warrants further investigation. Our
findings showed that an insignificant non-carcinogenic risk and significant
cancer risk is posed to the population from exposure to PM2.5 at this
location. Detailed exposure assessment of the PM2.5 at the specific
sources and the health risks posed by individual hazardous elements of
concern may help to improve understanding about the exposure pathways as
well as the detailed risk factors involved in both carcinogenic and
non-carcinogenic risk.