Submitted,
accepted
and
published
doi:10.1016/j.envpol.2014.08.025, 2014, 195, 167-177.
by
Environmental
Pollution
SOURCE APPORTIONMENT OF ATMOSPHERIC PM2.5-BOUND POLYCYCLIC
AROMATIC HYDROCARBONS BY A PMF RECEPTOR MODEL. ASSESSMENT
OF POTENTIAL RISK FOR HUMAN HEALTH.
María Soledad Callén*, Amaia Iturmendi, José Manuel López
Department of Energy and Environment, Instituto de Carboquímica (ICB-CSIC), Zaragoza, 50018,
Spain
*
Corresponding author: E-mail: marisol@icb.csic.es, Tel +34 976 733977, Fax: +34 976
733318
Abstract:
One year sampling (2011-2012) campaign of airborne PM2.5-bound PAH was performed in
Zaragoza, Spain. A source apportionment of total PAH by Positive Matrix Factorization
(PMF) was applied in order to quantify potential PAH pollution sources.
Four sources were apportioned: coal combustion, vehicular emissions, stationary emissions
and unburned/evaporative emissions. Although Directive 2004/107/EC was fulfilled regarding
benzo(a)pyrene (BaP), episodes exceeding the limit value of PM2.5 according to Directive
2008/50/EC were found. These episodes of high negative potential for human health were
studied, obtaining a different pattern for the exceedances of PM2.5 and the lower assessment
threshold of BaP (LATBaP). In both cases, stationary emissions contributed majority to total
PAH. Lifetime cancer risk exceeded the unit risk recommended by the World Health
Organization for those episodes exceeding the LATBaP and the PM2.5 exceedances for the
warm season. For the cold season, the risk was higher for the LATBaP than for the PM2.5
exceedances.
Keywords: PAH; PM2.5; PMF; BaP-TEQ; risk assessment
Capsule
A potential risk of PAH exposure was found in Zaragoza, Spain for episodes exceeding the
limit values of PM2.5 and the lower assessment threshold of BaP according to EU Directives.
INTRODUCTION
The airborne particulate matter (PM) and the polycyclic aromatic hydrocarbons (PAH) are
two pollutants, which are regulated at European level according to the most recent Directives
(2008/50/EC, 2004/107/EC). Particles are defined by their size (PM10 and PM2.5) as the
mass of particles with aerodynamic diameters less than 10 and 2.5 m, respectively. Although
it is recognised that there are health effects associated with exposure to coarse particles,
termed either PM2.5-10 or PMcoarse, the growing concern of the PM is specially reflected on
the smaller particles (Pope and Dockery, 2006; Ostro et al., 2007), fine particles (PM2.5) and
recently ultrafine particles with a diameter less than 0.1 m (HEI, 2013). These small particles
are of special concern because when inhaled, they are able to penetrate deep into the alveoli
where they can deposit and be absorbed. In fact, ultrafine particles are believed to have
several more aggressive health implications than those of larger particulates (Penttinen et al.,
2001). The Directive 2008/50/EC includes the PM2.5 like an airborne pollutant and it
establishes an annual limit value of 25 g/m3, to be met on the 1st of January 2015 (first
stage). Nevertheless, not only the size of the particulate matter influences remarkably on the
harmful health effects of PM2.5 but also the chemical composition.
A group of organic pollutants, which can be components of the PM are PAH. PAH are formed
by the incomplete combustion of organic materials (Harvey, 1997) and they are only
constituted by carbon and hydrogen arranged in two or more fused aromatic rings. Their
carcinogenic and mutagenic potential (Luch, 2005; Sanderson et al., 2004; Vendrame et al.,
2001; Wang et al., 2007) and their wide distribution make that, 16 PAH have been included
on the list of priority pollutants by the Environmental Protection Agency of United States
(ATSDR 1990, US-EPA 2003). PAH exert their mutagenic and carcinogenic activity through
biotransformation to chemically reactive intermediates, which bind covalently to cellular
macromolecules damaging genetic material and evoking malignant changes in cells (Ames et
al., 1972; Teranishi et al., 1975). Mutagenic activity, while not as uniformly associated with
cancer, may have implications for other non-cancerous adverse health effects, such as
pulmonary diseases (DeMarini et al., 2004; Seagrave et al., 2002). PAH are released to the
atmosphere in gas phase and/or associated to the particulate matter depending on their
chemical and physical properties (Ravindra et al., 2008; Wang et al., 2011) so that they can
overcome long-range transport affecting not only the sites where these pollutants are
generated.
The low solubility in water and low vapour pressure of the high molecular-weight PAH make
them to be almost found in the particle phase in the atmosphere. These particulate-bound PAH
evidence higher carcinogenicity than the gaseous PAH (Ravindra et al., 2001). One of them,
the benzo(a)pyrene (BaP) is a known cancer-causing agent and for this reason it is being used
as an indicator of exposure to harmful PAH through Directive 2004/107/EC. In general, the
most carcinogenic PAH, like benzo(a)pyrene, dibenz(a,h)anthracene are majority bound to the
PM, concretely to the PM2.5 (Li et al., 2009; Sanderson et al., 2004).
A significant proportion of Europe's population lives in cities, where exceedances of air
quality standards occur. In particular, the EU air quality 24-hour limit value for particulate
matter PM10 was exceeded in 2010 (EEA Report, 2012) in Poland, Italy, Slovakia, the Balkan
region, Turkey and several other urban regions. For PM10, there are limit values for daily and
annual exposure but for PM2.5, there are only values for annual exposure (Directive
2008/50/EC) so it is proposed that Member States undertake more comprehensive monitoring
of ambient levels of PM2.5 in urban areas as a first step in reducing average urban
concentrations throughout their territory (IP/08/570). Furthermore, it is priority to discern the
sources producing these pollutants.
Receptor models based on statistical approaches are widely used to identify and apportion
PAH sources in different environments (air, soil, sediments)(Callén et al., 2013; Hopke et al.,
2006; Larsen and Baker, 2003; Zuo et al., 2007). Among these, positive matrix factorization
(PMF)(Paatero, 1997), UNMIX (Henry, 1997), factor analysis (Ozeki et al. 1995) and
Principal Component Analysis (PCA) on Absolute Principal Component Scores (PCA-APCS)
(Thurston and Spengler, 1985) are some of the most used multivariate models. These models
provide important knowledge for effective pollution control and abatement.
Present study basically aims to identify and quantify pollution sources of PAH in a sub-urban
area in Zaragoza by applying a receptor model based on PMF. Episodes of high negative
potential impact on human health were considered and these were studied according to the
pollution sources obtained by this model. An assessment of the lifetime cancer risk based on
carcinogenic equivalent concentrations (BaP-TEQ) and mutagenic equivalent concentrations
(BaP-MEQ) related to BaP was also carried out, being the first time that this goal is carried
out in Spain for the PM2.5-bound PAH.
Materials and methods
Sampling site and PAH analysis
The study was performed in Rio Ebro Campus (41º40' 50.5''N; 0º53' 17.1''W), University of
Zaragoza as previously described (Callén et al., 2008a, 2009)(Figure 1). Zaragoza is the fifth
city in ranking according to population in Spain. The sampling location was a sub-urban area
mainly influenced by vehicle traffic due to the proximity of the AP-2 highway (~50 m)
joining Zaragoza with Barcelona (daily average intensity in 2011 =11420 vehicles, 11.2% of
heavy-duty vehicles), heating oil and natural gas combustion mainly used for domestic
heating, industrial emissions (industrial parks located in the surroundings of the city, paper
fabrics and power stations), extraction of fossil fuels and production processes, waste
treatment and agriculture (San José et al., 2013).
A MCV high-volume air sampler (30 m3/h) (MCV, S.A.), provided with a PM2.5 cut off inlet
at 2.5 m and located at the top of the roof (approximately 6 m from the ground), was used to
collect particulate phase PAH over quartz fibre filters QF1-150 (150 mm diameter). A total of
61 samples (each sample corresponding to 24 h of continuous sampling, two samples per
month and two continuous weeks, from Monday to Saturday, for each season) were collected
from June 27th, 2011 to May 19th, 2012. The volume of air drawn through the filter was about
769 m3. After collection, the filters were wrapped separately in aluminium foil and stored in a
freezer at -20ºC until analysis.
PM2.5 mass concentration was determined gravimetrically by weighting the filters, before
and after sampling in a microbalance (accuracy 10 g), once the filters were conditioned in
desiccators.
The
following
PAH
(phenanthrene
(Phe),
anthracene
(An),
2+2/4-
methylphenanthrene (2+2/4MePhe), 9-methylphenanthrene (9MePhe), 1-methylphenanthrene
(1MePhe), 2,5-/2,7-/4,5-dimethylphenanthrene (DiMePhe), fluoranthene (Flt), pyrene (Py),
benzaanthracene (BaA), chrysene (Chry), benzobfluoranthene (BbF), benzojfluoranthene
(BjF),
benzokfluoranthene
(BkF),
benzoepyrene
(BeP),
benzoapyrene
(BaP),
indeno1,2,3-cdpyrene (IcdP), dibenza,hanthracene (DahA), benzoghiperylene (BghiP)
and coronene (Cor) were quantified according to previous publications using gas
chromatography-tandem mass spectrometry (CG/MS-MS) (Callén et al., 2008b).
Briefly, 1/2 of the filter was extracted by Soxhlet with dichloromethane (DCM) after the
addition of a surrogate standard solution (An-d10+BaP-d12+BghiP-d12). Samples were
concentrated by a rotary evaporator and afterwards by a nitrogen stream flow until 1 ml. Solid
phase extraction (SPE) cartridges were used as the pre-separation step before injection of the
sample in the GC/M-/MS. The SPE cartridges of 1 g Silica (MFE) were washed/conditioned
with 10 mL DCM before addition of the sample. The concentrated raw extract was loaded on
top of the SPE cartridge. The PAH fraction was eluted with 6 mL DCM and concentrated in a
pure N2 stream. The solvent was exchanged to n-hexane and p-terphenyl native was added as
recovery standard. Each compound was quantified by CG/MS-MS operating at electron
impact energy of 70 eV and using the multiple reaction monitoring (MRM) mode. A Varian
Select PAH capillary column (30 m x 0.25 mm internal diameter x 0.25 m film thickness)
was used to quantify PAH and 1 L of sample was injected in splitless mode. The GC
conditions were: 1.5 ml/min Helium flow; temperature-time programme: 70ºC, 1 min,
increasing 10º/min till 325ºC and isotherm for 18.5 minutes. The injector temperature was set
to 280ºC, the transfer line to 300ºC and the ion trap to 220ºC. The identification and
quantification of PAH were done according to retention times with standard solutions and the
internal standard method relative to the closest eluting PAH surrogate. Calibration curves
were prepared with PAH concentrations between 20-1000 ppb in n-hexane. The
concentrations of the surrogate and recovery standard were the same and identical as those of
the sample extracts. The correlation coefficients of the calibration curve for the different PAH
were R2>0.99.
Quality control
In the analysis, a procedural blank was performed periodically to confirm that there was no
laboratory contamination. Blank filters were extracted and passed through similar protocol of
the PM2.5 samples. The average blank concentration was subtracted from each sample to
correct for the effects of transport and handling. The limit of detection was calculated as three
times the standard deviation of the blank filters.
The quality assurance was checked by extracting five aliquots of 250 ppb containing PAH and
deuterated PAH and performing the whole process consisting of extraction, cleaning and
concentration. The recoveries of each individual PAH ranged between 91 and 131% for Cor
and Phe, respectively. Respective values of relative standard deviations (RSD) for these
standards (n=5) varied from 2.3 to 13.3%, demonstrating good repeatability for the analytical
method.
PMF receptor model
For the PMF model, a set of 61 samples and 19 species was used as input of the model. The
classification of species in strong, weak and bad was performed according to the signal/noise
ratio (S/N) and/or the percentage of samples below the detection limit (BDL) (Buzcu-Guven
et al., 2007; Paatero and Hopke, 2003). In general, those species with S/N>2 were categorized
as strong in the model. Those with S/N between 0.2 and 2, or with BDL> 50% were
categorized as weak. Those with S/N <0.2 or with BDL>60% were categorised as bad in
quality and were excluded from the PMF analysis. According to these, the following species
were considered as bad species: MePhe9, MePhe24 and DiMePhe (90%, 51% and 61% BDL,
respectively). Phe, An and total PAH were identified as weak species and the 13 remaining
species as strong variables. An extra modelling uncertainty of 5% was used to obtain model
output. The model was run with different number of factors ranging from 3 to 5 obtaining the
optimum solution with 4 factors. The values of Q (Robust) and Q (True) were equal to 591.5
for all the 30 runs and the minimum Q (Robust) estimated in the 4th run was 0.97 times the
Qtheoretical (610).
Q(Theoretical)=(samples*good species)+(samples*weak species)/3)-(samples*factors estimated))
(1)
Concentrations below the detection limit were substituted by half the detection limit and their
overall uncertainties were set at five-sixths of the detection limit values (Polissar et al., 1998).
There were no missing data. Sample specific uncertainties were provided according to Sij=
DL/3+c*xij, where sij= uncertainty, DL =detection limit, c =constant (0.1 if xij>3*DL, 0.2 if
Xij<3*DL) and xij=variable (based on Chueinta et al., 2000). In addition to the Q value, other
fit diagnostic guidelines were also employed to determine the appropriate number of factors
(the factors identified can be related to the known physical sources, scatter plots show good
correlation between the observed and the measured concentration of species, with the majority
of standardised residuals of species between +3 and -3 (93% of the data for this particular
case) and normally distributed, the G-space plots show data points lying within the source
axes, there is no rotational freedom and bootstrapping has been performed to estimate the
stability and uncertainty of that solution).
Uncertainties were estimated using a bootstrapping technique, which is a re-sampling method
in which “new” data sets are generated that are consistent with original data (Eberly, 2005).
This method allows the analyst to review the confidence intervals for each species to obtain
more robust profiles. The total PAH PMF results were bootstrapped using 100 runs, minimum
correlation R-value=0.6 and seed=random. The bootstrapping factors were consistently
mapped back to the original PMF factors as shown in Table S1, Supplementary Information.
Bootstrap results for the source profiles (25th to 75th percentiles) are shown in Figure S1,
Supplementary Information indicating that results of PMF analysis were good. Rotational
freedom in solutions were explored and controlled using Fpeak (Paatero and Hopke, 2002)
and observing the effect on the Q values, G–vector plots and residual plots (Paatero et al.,
2005). In this study, Fpeak values between -1.5 and 0.3 (intervals of 0.1) gave convergent
results with the minimum Q robust (598.4), Q true (592.3) for -0.1. By altering the Fpeak
value, it was not found to result in substantially better source profiles and contributions.
Consequently, default base run results are reported in this paper.
BaP-eq and Lifetime Cancer Risk
An assessment of the carcinogenic and mutagenic potential of PAH was carried out by
calculating the carcinogenic equivalent concentrations (BaP-TEQ) and the mutagenic
equivalent concentrations (BaP-MEQ) relative to BaP, respectively. Both were calculated by
multiplying the toxic equivalent factors (TEF) suggested by Larsen&Larsen (1998) (Callén et
al., 2014) and the mutagenic potency factors (MEF) (Durant et al., 1996, 1999) (Table S2,
Supplementary Information) by the concentration of each individual PAH in a sample.
Regarding the lung cancer risk via inhalation route, WHO (1987, 2000) suggested the unit risk
(UR) of 8.7x10-5 (ng/m3)-1 for the lifetime 70 years PAH exposure, assuming one exposed to
the average level of one unit BaP concentration (1 ng/m3). This UR was based on an
epidemiology study on coke-oven workers in Pennsylvania.
The corresponding concentrations of BaP producing excess lifetime cancer risks of 1/10000,
1/100000 and 1/1000000 are 1.2, 0.12 and 0.012 ng/m3, respectively (WHO, 2000). The
lifetime cancer risks attributable to PAH inhalation exposure was estimated by multiplying the
sum of the individual BaP-TEQ or BaP-MEQ, respectively by the UR of exposure to BaP
according to the formula: LCR= URxBaP-TEQ and URxBaP-MEQ where UR= unit risk
above mentioned.
Results and discussion
PM2.5, PAH concentrations and meteorological variables
Firstly, a summary of the PM2.5, individual and total PAH concentrations as well as the
meteorological conditions for the whole period and two seasons: warm season (summer and
spring) and cold season (winter and autumn) are shown in Table 1.
The average PM2.5 concentration was 13.05+6.56 g/m3 and no seasonal variations were
found by applying t- test for both seasons. Similar PM2.5 concentrations to the ones obtained
in this work were found in different rural background areas in Spain 12-17 g/m3 (Querol et
al., 2008) and in summer season in Italy (Masiol et al., 2012)(Table 2). Higher PM2.5
concentrations of 16.3 g/m3 were reported by Eeftens et al. (2012) in other Spanish cities
like Barcelona and by Querol et al. (2008) in traffic, urban background and suburban
industrial areas of Madrid and Barcelona. Regarding other European countries, very similar
PM2.5 concentrations were obtained in Vorarlberg, Austria, in Heraklion, Greece (14.7
g/m3), in Munich/Augsburg (14.3 g/m3) (Eeftens et al., 2012), in Venice (Contini et al.,
2011) and higher concentrations were determined in a suburban area of Athens (17.7
g/m3)(Pateraki et al., 2012). Higher PM2.5 concentrations (28.1 g/m3) were also reported in
other European countries like in Turkey, during January-December 2007 (Akyüz and Çabuk,
2009). Regarding other non-European countries, average PM2.5 concentrations of 54.03
g/m3 were obtained in Taiwan (Fang et al., 2006), 20.58 g/m3 in Yokohama, Japan (Khan et
al., 2010), 45.21 g/m3 in Mexico (Mugica-Álvarez et al., 2012) and 104.9 g/m3 and 91.1
g/m3 at urban and rural sites, respectively in Agra (India) (Kulshrestha et al., 2009).
Regarding PAH, most of the PAH presented seasonal variations statistically significant
(p<0.05) with the exception of 2/4MePhe, 9MePhe, DiMePhe and Chry obtaining, in general,
higher PAH concentrations during the cold season favoured by the impact of the
meteorological conditions (reduced atmospheric dispersion from lower mixing height, low
atmospheric temperature affecting the PAH gas and particle distribution) and anthropogenic
factors, as reported by different authors (Callén et al., 2008a; Li et al., 2006; Martellini et al.,
2012; Tsapakis and Stephanou, 2005). An exception was obtained for 9MePhe and DiMePhe
with higher concentrations in the warm season that favoured volatilization processes. The
dominant PAH were those with four and five rings being Chry, Fth, BbF, Py and BghiP the
compounds with the highest average concentrations (Table 1).
Individual PAH concentrations presented in this work were in the same order of magnitude
than other studies undertaken in other European cities (Table 2) like Madrid, Spain (Barrado
et al., 2012), Elche, Spain (Varea et al., 2011), in Basque Country, Spain (Villar-Vidal et al.,
2014) and in Oporto, Portugal (Slezakova et al., 2013). Similar PAH concentrations were also
obtained in other non-European locations like in Atlanta, USA (Li et al., 2009) and a suburban site in China (Hou et al., 2006; Zhang et al., 2013). Higher PAH concentrations were
reported in Turkey (Akyüz and Çabuk, 2009) and in different locations in China (Hou et al.,
2006; Kong et al., 2010).
Possible correlations among the individual, total PAH, PM2.5 and the meteorological
variables recorded: temperature and pressure (Table S3, Supplementary Information) were
studied by determining Pearson correlation coefficients.
A negative correlation (p<0.01) was obtained between the total PAH and the temperature
confirming that higher PAH concentrations were obtained during the cold season
corroborating the seasonal behaviour of total PAH (Akyüz and Çabuk, 2009; Barrado et al.,
2012; Varea et al., 2011). An exception was obtained for the DiMePhe showing a significant
positive correlation (p<0.05) with the temperature.
One of the variables affecting the seasonal behaviour of PAH is the temperature. Ambient
temperature affects significantly the gas–particle partitioning of PAH (Sitaras et al., 2004).
The increase of temperature causes the evaporation of particle phase PAH to gas phase while
the decrease in ambient temperature enhances the condensation of gas phase PAH onto
atmospheric particles, resulting in a negative correlation of particulate PAH and ambient
temperature (Tham et al., 2007; Tham et al. 2008; Tsapakis and Stephanou, 2005). In
addition, the higher impact of anthropogenic sources like domestic heating and consumption
of fuels would contribute to an increase in particulate PAH concentrations during cold season
(Mastral et al. 2003). High temperatures and high solar irradiation might promote PAH
degration (Beyer et al., 2003; Smith and Harrison, 1998; Panther et al., 1999) due to chemical
and photochemical reactions favouring PAH reduction in summer. These observations are
consistent with the findings of other researchers (Fang et al., 2004; Kitazawa et al., 2006;
López Cancio et al., 2002) and previous research carried out by this group (Callén et al.,
2014).
On the contrary, a positive correlation (p<0.05) was found between the total PAH and the
pressure. Positive correlations among different PAH and atmospheric pressure were also
obtained by different authors (Augusto et al., 2013; Barrado et al., 2012). At low
temperatures, atmospheric pressure is high and PAH will be more concentrated in air
(Augusto et al., 2013). This could explain the positive correlation between atmospheric
pressure and total PAH as well as the negative correlation with temperature.
No significant correlation was found between PM2.5 and total PAH. A lack of correlation
between total PAH and PM2.5 has also been reported by different authors (Anastasopoulos et
al., 2012; Ohura et al., 2004). Because PAH originate mainly from anthropogenic emissions,
the lack of correlation between total PAH and PM2.5 could indicate that different pollution
sources were affecting both pollutants. However, significant correlations at 95% levels were
obtained for the following PAH (BaA, Chry, BbF, BjF, BeP, BghiP) with the PM2.5
indicating that these heavy-PAH and PM2.5 presented common pollution sources.
Source apportionment of total PAH by PMF
The source profiles developed by PMF model and the individual contribution of each factor to
total PAH are shown in Figures 2a,2b,2c. The first factor was mainly defined by Flt, Py and
Phe (Figure 2a). High levels of these compounds and higher mass PAH were associated with
emissions from coal-fired plants (Liu et al., 2003) so this factor was considered as coal
combustion emissions (coal power stations and factories using coal as fuel), with a
contribution of 25.0% (Figure 3a), which remained constant for the different seasons (Figure
3b,3c). The second factor was related to IcdP, BghiP, Cor, BbF, BkF, BjF and BeP. IcdP, BbF
and BkF (Figure 2a) may be associated with vehicular emissions (Li and Kamens, 1993,
Venkataraman and Friedlander, 1994). IcdP+DahA, BghiP and Cor are typical markers of
traffic emissions (Simcik et al., 1999;Venkataraman and Friedlander, 1994) and IcdP was
associated with diesel emissions (Li and Kamens, 1993). Cor, suggested as a marker for
gasoline engine exhaust (Cass, 1998; Schauer et al., 1996), was also detected in the exhaust
from diesel-powered vehicles operated under the idle+creep driving cycle by Riddle et al.
(2007). In addition, high concentrations of Cor were obtained by Mastral et al. (2003) in areas
with high density of traffic using diesel as main fuel. This factor was named as vehicular
emissions without distinguishing between diesel and gasoline emissions and it contributed
24.2% to total PAH (Figure 3a). The third factor showed high loadings for BaP, BaA, BkF,
Chry and BbF (Figure 2a). Simcik et al. (1999) pointed out that BaA, Chry, Py, Phe and Flt
are associated with combustion of natural gas and BaA has been considered as tracer for this
source. One of the main fuels used in residential heating in Zaragoza is natural gas, which is
also used in industrial processes. The high factor loading of Chry, BbF indicates stationary
emission sources (Kulkarni and Venkataraman, 2000). Kulkarni and Venkataraman (2000)
have shown that BaP can be a good tracer for wood combustion therefore this factor was
attributed to stationary emissions, which could include natural gas, oil and wood combustions,
contributing 29.3% to total PAH (Figure 3a). This percentage was almost constant for the two
seasons indicating a lack of seasonal behaviour, typical of industrial emissions (Figure 3b,3c).
A fourth factor, which was not well defined (MePhe1, An, Phe, Chry)(Liu et al., 2009) was
indicative of volatile PAH. Methylphenanthrenes have been used to identify uncombusted
petroleum (Kavouras et al., 2001) and to identify evaporative/uncombusted petroleum sources
in a PMF study (Lee et al., 2004). This factor is suggested to be indicative of volatilization or
spill of petroleum-related products showing the petrogenic source of PAH (21.5% of total
PAH (Figure 3a)) and contributing with a higher percentage during the warm season (30.5%,
Figure 3b).
With regard to the adequacy of the PMF model, the PMF model underestimated the total PAH
concentration with a modeled value of 2.104 ng/m3 determining 98.5% of the experimental
PAH. A good correlation was obtained between the experimental and the modelled total PAH
(R2=0.98, slope=1.02, ordinate at the origin=-0.08)(Table S4, Supplementary Information).
Also, good correlations between the modelled and the experimental variables were obtained,
with R2 close to 1 for most of the species (the lowest R2=0.76 for Phe and the highest R2=1.00
for 1MePhe), with the maximum error for An (-26.5%) and with slopes and intercepts close to
1 and zero, respectively.
Episodes of high BaP and PM2.5 exceedances
Although no exceedances of BaP (BaP=1.0 ng/m3) were obtained during the sampling
campaign according to Directive 2004/107/EC, a study was performed by considering the
lower assessment threshold of BaP (LATBaP), which is 0.4 ng/m3. Episodes with BaP
concentrations higher than 0.4 ng/m3 were studied in more detail for this sampling campaign
(Figure S2, Supplementary Information). Only 6 episodes (10% of the total samples) showed
exceedances of this LATBaP, being 83% of the cases produced during the cold season. Only
one episode with date 16/05/2012 reached this high BaP concentration during the warm
season. This particular episode corresponded to operational work carried out close to the
sampling site for the tram installation in Zaragoza. The backward air trajectories for these
episodes are shown (Figure S2, Supplementary Information) and most of them (50% of the
cases) indicated contribution of long-range transport at regional and European level. For the
other 50% of the cases, Atlantic trajectories were dominant indicating that local emissions
were mainly responsible of the high BaP concentrations.
It was also studied samples exceeding the lower
assessment threshold of PM2.5 (12
g/m3)(LATPM2.5), the upper assessment threshold of PM2.5 (17 g/m3)(UATPM2.5) and
exceedances of the limit value of PM2.5 (25 g/m3) according to the Directive 2008/50/EC,
obtaining that 54.1%, 24.6% and 4.9% of the whole samples, respectively exceeded these
values. For the UATPM2.5, most of these episodes were produced during the warm period
(summer and spring), in particular 60% of the cases, whereas only 3 episodes of exceedances
of the limit value of PM2.5 were obtained, occurring 67% of them during cold weather and
only one episode during the warm season. The dates of these PM2.5 exceedances were
08/10/2011 (Atlantic episode), 18/02/2012 (European episode) and 16/05/2012 (Atlantic
episode), coinciding some of them with episodes of LATBaP (Figure S2, Supplementary
Information). These last two dates also showed high total PAH concentrations of 7.70 and
10.62 ng/m3, respectively, indicating the main contribution of local pollution sources for the
16/05/2012 and contribution from European transport for the 18/02/2012. In the case of the
08/10/2011, the total PAH concentrations were 0.22 ng/m3 indicating the different nature of
both pollutants: total PAH and PM2.5. With regard to the pollution sources explaining these
episodes (Figure S3, Supplementary Information), it was obtained that whereas for the
average of all samples, the four factor contributed at similar percentage, for the other
episodes: UATPM2.5, LATPM2.5, exceedances of PM2.5 and LATBaP, this trend was
different with a higher contribution of the stationary emissions factor (22.5-24.2%) and a
decrease of the coal combustion and evaporative emissions.
Carcinogenic and mutagenic potential of PAH
An estimation of the potential carcinogenic and mutagenic risks of exposure to PAH was also
performed by calculating the inhalation lifetime cancer risk based on BaP-TEQ (LCRBaPTEQ) and BaP-MEQ (LCR-BaPMEQ) for all these episodes (Figure S3, Supplementary
Information). It was observed that both, the exceedances of the PM2.5 and the LATBaP
exceeded or they were very close to the UR (see the LCR-BaPMEQ), involving these last
episodes a higher risk for human health. When a distinction between the warm and cold
season was performed, it was observed that episodes exceeding the limit value of PM2.5 and
the LATBaP during the warm season, presented a considerable risk for human health (Figure
4). According to the US-EPA (2001), it is believed that one in a million (10-6) over an average
lifetime of 70 years is the risk level acceptable or inconsequential, and a one in ten thousand
level (10-4) is considered serious. This risk was higher by considering the mutagenic PAH
potential rather than the carcinogenic potential (Figure 4b). In the case of the LATBaP, the
LCR-BaPMEQ also exceeded the WHO unit risk during the cold season. Therefore, a
different behavior was deduced according to the exceedances of PM2.5 and the LATBaP as a
function of the season. Whereas in both cases during the warm season, a potential risk was
obtained, during the cold season, only the LATBaP presented a considerable risk for human
health exceeding the LCR-BaPMEQ the WHO unit risk (8.7x10-5 per 1 ng/m3 of BaP).
This study allowed corroborating the highest risk for human health associated with the
exceedances of PM2.5 and the lower assessment threshold of BaP. Despite there were not
episodes exceeding the current limit value of BaP=1.0 ng/m3, by only considering episodes of
BaP higher than 0.4 ng/m3, a considerable risk for human health was estimated. Only one
episode was obtained during the warm season associated with road work but a higher number
of exceedances of the LAT of BaP were produced during the cold season involving a LCRBaPMEQ higher than the WHO unit risk. Results shown here also supported previous results
obtained by our research group (Callén et al., 2014) for PM10-bound PAH remarking the
negative impact of the cold season and the higher contribution of the stationary emissions
over human health in Zaragoza.
This work also contributed to improve knowledge regarding studies reporting lung cancer risk
assessments attributable to PAH inhalation exposure in Spain (Ramirez et al., 2011). Because
only the inhalation exposures and PAH in the particle phase have been considered in this
work, it is expected that this potential risk could be increased. The average estimated excess
inhalation cancer risk for the average samples indicated a risk of 1.9 cases per 100000 people
increasing to 2.5 cases when the mutagenic character was considered (Figure 5). This risk
increased to 8.2 (10 cases for the mutagenic) and 6.8 (9) cases, respectively for the episodes
of lower assessment threshold of BaP and exceedances of the limit value of PM2.5.
Conclusions
PM2.5-bound PAH were determined in a sub-urban area in Spain during one year period
2011-2012. Seasonal variations between the warm and cold season were obtained for most of
the PAH obtaining higher concentrations during the cold season. No seasonal behavior was
obtained for the PM2.5.
PMF was used as receptor model to apportion PAH pollution sources associated to the
airborne PM2.5. This receptor model allowed distinguishing four pollution sources related to
coal combustion, vehicular emissions, stationary emissions and evaporative/unburned fuel,
explaining 98% of the total PAH. Episodes of potential negative impact for human health
were studied according to the lower, the upper assessment threshold of PM2.5, the
exceedances of PM2.5 and the lower assessment threshold of BaP. With the exception of the
episode of operational work obtained during the warm season, in general, the cold season
showed a higher negative potential for human health for all episodes, especially the ones of
LATBaP, presenting a lifetime cancer risk close to 10-4 whereas the exceedances of PM2.5
did not reach the WHO unit risk.
ACKNOWLEDGMENTS
The authors would like to thank the Industry and Innovation department of Gobierno de
Aragón (DGA), the Fondo Social Europeo “Construyendo Europa desde Aragón” and the
Ministry of Science and Innovation (Spain) through the project CGL2009-14113-C02-01 for
partial financial support. J.M. López would also like to thank the MICIIN for his Ramón and
Cajal contract.The authors also gratefully acknowledge the NOAA Air Resources Laboratory
(ARL) for the HYSPLIT model used in this publication.
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Manuscript title: Source apportionment of atmospheric PM2.5-bound polycyclic aaromatic
hydrocarbons by a PMF receptor model. Assessment of potential risk for human health.
Authors: M.S. Callén, A. Iturmendi, J.M. López
Ms. Ref. No.: ENVPOL-D-14-00748
Journal: Environmental Pollution
Graphical abstract
Excess cancer risk
per 100000 people
12
10
8
6
4
2
0
Average all samples Exceedances PM2.5 Lower Assessment
Threshold BaP
TABLES
Table 1. Summary of the average PM2.5 (g/m3), individual, total PAH (ng/m3) concentrations and
meteorological conditions (Pressure: mm Hg, Temperature=ºC) for the campaign performed in
Zaragoza (Season 1 (warm season)= 28 samples, 2 (cold season)= 33 samples)(SD= standard
deviation).
All samples
Warm season
Cold season
Average
SD
Average
SD
Average
SD
PM2.5
13.05
6.56
13.8
6.57
12.41
6.59
Phe
0.07
0.07
0.04
0.03
0.10
0.08
An
0.01
0.01
0.01
0.01
0.01
0.01
2+2/4MePhe
0.03
0.03
0.03
0.03
0.04
0.03
9MePhe
0.07
0.08
0.09
0.12
0.06
0.01
1MePhe
0.05
0.04
0.04
0.02
0.06
0.05
DiMePhe
0.10
0.28
0.17
0.41
0.04
0.04
Flt
0.21
0.23
0.10
0.11
0.30
0.27
Py
0.20
0.21
0.10
0.10
0.29
0.25
BaA
0.13
0.19
0.08
0.22
0.18
0.16
Chry
0.23
0.31
0.15
0.37
0.30
0.24
BbF
0.21
0.28
0.12
0.31
0.30
0.24
BkF
0.06
0.08
0.03
0.07
0.09
0.08
BjF
0.13
0.15
0.06
0.14
0.18
0.15
BeP
0.16
0.2
0.09
0.21
0.22
0.18
BaP
0.12
0.17
0.06
0.15
0.17
0.17
IcdP+DahA
0.09
0.11
0.04
0.10
0.13
0.11
BghiP
0.16
0.19
0.08
0.16
0.24
0.19
Cor
0.08
0.09
0.04
0.07
0.12
0.09
Total PAH
2.14
2.18
1.31
1.92
2.84
2.17
Pressure
992.2
5.8
990.1
5.3
994.0
5.68
Temperature
13.1
7.3
18.3
6.7
8.7
4.1
Table 2.
PM2.5 concentrations (g/m3) and total PAH concentrations (ng/m3) around the world.
World location
European countries
Non-European
countries
European countries
Non-European
countries
ΣPAH
Country
Spain
Spain
Italy
Spain
Greece
Germany
Italy
Greece
Turkey
Location
Monagrega, Montseny
Madrid, Barcelona
Venice
Barcelona
Heraklion
Munich
Venice
Athens
Zonguldak province
PM2.5
12-17
34, 35
14.1
16.3
14.7
14.3
13.7
17.7
28.1
Period
1999-2000;2002-2006
1999-2000; 2002-2006
Summer 2009-2010
14/01/2009-14/01/2010
18/02/2009- 16/02/2010
27/10/2008- 05/11/2009
March-October 2007
June 2003 to December 2008
January–December 2007
China
Taiwan
54.03
March 2004-January 2005
Japan
Yokohama University
20.58
2007-2008
Urban site
Khan et al. 2010
Mexico
Mexico city
45.21
University of Azcapotzalco
Mugica-Alvarez et al. 2012
India
Spain
Agra
Madrid
104.9,91.2
15.36
November 24-December 14,
2007
May 2006-March 2008
January-November 2008
Urban, rural site
Suburb
Kulsherestha et al. 2009
Barrado et al., 2012
Spain
Portugal
Basque country
Elche
Alicante
Oporto
25.4
26.7
29.0
Urban-industrial
Urban
Industrial
Urban
Villar-Vidal 2014
Varea et al. 2011
Varea et al. 2011
Slezakova et al. 2013
USA
Atlanta
Urban-suburban highways
Li et al., 2009
2006-2011
2006-2007
2006-2007
December 2007 and January
2008
May-September 2004
Type of location
Rural background
Traffic, urban industrial
0.14 a
88.4b
(RB/UB/ST) 1/8/11
(RB/UB/ST) 0/12/8
(RB/UB/ST) 5/6/9
Harbor office building
Suburban area
Centre for coal mining with the
associated steel industry
Authors
Querol et al., 2008
Querol et al., 2008
Masiol et al., 2012
Eeftens et al. 2012
Eeftens et al. 2012
Eeftens et al. 2012
Contini et al., 2011
Pateraki et al. 2012
Akyüz, H. Cabuk, 2009
Fang et al., 2006
0.106c(winter)
0.065c (summer)
1.05d
1.085e
1.151e
9.11f
0.38-0.98g
Atlanta
November-December 2004
2.12-6.85g
Urban-suburban highways
Li et al., 2009
Miyun
9-10 March 2004
5.81h
Suburban
Hou et al. 2006
Yulin
11 March-15 April 2004
78.77h
Residential and heavy traffic regions Hou et al. 2006
China
Fuzhou
24.92
September 17–23, 2007
4.30i
Zhang et al. 2013
China
Fuzhou
36.56
January 17–23, 2008
6.99i
Zhang et al. 2013
China
Shenyang, Anshan,
2004-2005
75.32-1900.89j
Kong et al., 2010
Fushun , Jinzhou and
Dalian
RB=regional background, UB = urban background and ST =street site.
a= ΣPAH (BaA,Chry, BbF, BkF, BeP, BaP, IcdP, DahA, BghiP)
b=
Σ16 PAHs (Nap, Acy, Ace, Flu, Phe, An, Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, IcdP, BghiP)
c=
ΣPAH (Nap, Ace, Flu, Phe, An,Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, BghiP)
d=
ΣPAH (Flt, BbF, BkF, BaP, BghiP, IcdP)
e= ΣPAH (Phe, Flt, Py, BaA, Chry, BbF, BkF, BaP, BghiP, IcdP)
f=
ΣPAH (Dibenzo[a,l]pyrene (D[a,l]P) (Nap), Acy, Ace, Flu, Phe, An, Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, BghiP, IcdP)
g= ΣPAH (Nap, Acy, Ace, Flu, Phe, An, Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, IcdP, BghiP, 2-methylnapthalene, 1-methylnapthalene, biphenyl, dibenzothiophene, 3-Methylphenanthrene, 2-Methylphenanthrene, 9Methylphenanthrene, 1-Methylphenanthrene, retene, benzo(c)phenanthrene, perylene, BeP)
h= ΣPAH (Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, IcdP, BghiP)
i=
ΣPAH (Nap, Ace, Flu, Phe, An, Flt, Py, BaA, Chry, BbF, BkF, BaP, DahA, BghiP, IcdP)
j= ΣPAH (Nap, Ace, Phe, An, Flu, Py, BaA,Chry, BbF, BkF, BaP, DahA, BghiP, IcdP)
China
26
a)
b)
= petrol station
= airport
I.P.= Industrial park
WWT= Water treatment plant
Paper fabric
Figure 1.
Location of the sampling site in Zaragoza (ZGZ sampling site). The main
highway: A-2 and some roads: Z-30, Z-40 as well as different industrial
parks are remarked. I.P.= Industrial parks, WWT= water treatment plant.
27
8
7
6
5
4
3
2
1
0
28
Figure 2.
20-Jan-12
25-Jan-12
16-Feb-12
20-Mar-12
23-Mar-12
28-Mar-12
19-Apr-12
19-May-12
20-Jan-12
25-Jan-12
16-Feb-12
20-Mar-12
23-Mar-12
28-Mar-12
19-Apr-12
19-May-12
24-Nov-11
21-Nov-11
16-Nov-11
9-Nov-11
10-Oct-11
19-Sep-11
25-Jul-11
14-Jul-11
11-Jul-11
6-Jul-11
17-Jan-12
Evap./Unburned fuel
17-Jan-12
Station. Emis.
17-Dec-11
b)
17-Dec-11
24-Nov-11
21-Nov-11
16-Nov-11
9-Nov-11
10-Oct-11
19-Sep-11
25-Jul-11
14-Jul-11
11-Jul-11
6-Jul-11
27-Jun-11
8
7
6
5
4
3
2
1
0
27-Jun-11
0.180
0.160
0.140
0.120
0.100
0.080
0.060
0.040
0.020
0.000
Coal comb.
Vehicu. Emiss.
Station. Emis.
Evap./unburned fuel
a)
Coal combustion
Vehic. Emis.
c)
Plots of a) factor profiles (ng/m3) and b), c) individual
contribution (ng/m3) of each factor obtained by the PMF
model.
b)
a)
c)
Figure 3.
29
Source apportionment of total PAH (ng/m3, %) by PMF model for the a) average of all samples (N=61), b) warm season
(N=28) and c) cold season (N=33).
100
1.80E-04
90
1.60E-04
80
1.40E-04
70
1.20E-04
%
60
1.00E-04
50
8.00E-05
40
6.00E-05
30
20
4.00E-05
10
2.00E-05
0
0.00E+00
Warm
Cold
All samples
Warm
Cold
LATPM2.5
Warm
Cold
UATPM2.5
Warm
Cold
Exc. PM2.5
Warm
Cold
LATBaP
a)
100
1.80E-04
90
1.60E-04
80
1.40E-04
70
1.20E-04
%
60
1.00E-04
50
8.00E-05
40
6.00E-05
30
20
4.00E-05
10
2.00E-05
0
0.00E+00
Warm
Cold
All samples
Warm
Cold
LATPM2.5
Warm
Cold
UATPM2.5
Warm
Cold
Exc. PM2.5
Warm
Cold
LATBaP
b)
Figure 4.
30
Contribution (%) of the four sources obtained by the PMF model as a function of
the season for the average all samples, the episodes exceeding the lower
assessment threshold of PM2.5 (LATPM2.5), the upper assessment threshold of
PM2.5 (UATPM2.5), the limit values of PM2.5 (Exc. PM2.5) and the lower
assessment threshold of BaP (LATBaP). An assessment of the lifetime cancer
risk LCR from the BaP-TEQ and BaP-MEQ was also performed for those
episodes (right scale). The WHO Unit Risk (UR)=8.7*10-5 (ng/m3)-1 was also
remarked.
12
Excess cancer risk per 100000
based on BaP-TEQ
10
8
Excess cancer risk per 100000
based on BaP-MEQ
6
4
2
0
Average
Figure 5.
31
LATPM2.5
UATPM2.5
Exc. PM2.5
LATBaP
Estimated number of excess inhalation cancer cases per one-hundred
thousand people based on the BaP carcinogenic (BaP-TEQ) and the
mutagenic (BaP-MEQ) equivalent for all samples (average), the episodes
exceeding the lower assessment threshold of PM2.5 (LATPM2.5), the
upper assessment threshold of PM2.5 (UATPM2.5), the limit values of
PM2.5 (Exc. PM2.5) and the lower assessment threshold of BaP
(LATBaP).