Introduction
Fine organic aerosol (OA) has a major role in environmental and human health
impacts (Peng et al., 2009). Some researchers have recently estimated that
fine OA constitutes 23–38 % of the PM2.5 mass in urban areas (Qin
et al., 2006; Viana et al., 2006; Duan et al., 2007; Upadhyay et al., 2011).
In addition, OA along with elemental carbon (EC) can account for up to
31–57 % of the PM2.5 mass (Duan et al., 2007; Upadhyay et al., 2011;
Martínez et al., 2012).
Atmospheric fine OA is a complex mixture of hundreds of organic compounds
that are directly emitted or are generated by atmospheric chemical
processes. Many of these organic compounds are toxic or carcinogenic
(Spurny, 2000; Pope et al., 2002), but can be useful as markers to identify
the source of the aerosols being measured at a specific site. Organic
markers that have been used in the past include levoglucosan, cholesterol,
nicotine, n-alkanes, hopanes (pentacyclic triterpenes), and polycyclic
aromatic hydrocarbons (PAHs). Levoglucosan is a pyrolysis decomposition and
combustion product of cellulose; therefore, it can be used as a tracer for
biomass burning sources (Fraser and Lakshmanan, 2000; Robinson et al., 2006a; Alves
et al., 2011; Gonçalves et al., 2011). Cholesterol and nicotine are good
markers for meat-cooking operations (Rogge et al., 1991; Schauer et al.,
2001b; Robinson et al., 2006b) and cigarette smoke (Eatough et al., 1989;
Hildemann et al., 1991; Rogge et al., 1994; Kavouras et al., 1998),
respectively. Hopanes are biomarkers of fuel oil combustion, coal combustion,
and lubricants, and are useful to identify engine emissions (Rogge et al.,
1993a; Oros and Simoneit, 2000; Simoneit et al., 2004; Schnelle-Kreis et
al., 2005). PAHs are semi-volatile compounds formed from incomplete fossil fuel
combustion processes (Rogge et al., 1993a; Marr et al., 2004;
Sklorz et al., 2007). Finally, n-alkanes are indicators of fossil fuel
utilization and biogenic emissions (Simoneit et al., 2004; Young and Wang,
2002). Additional details about specific organic markers and their emission
sources can be found elsewhere (Simoneit et al., 1991; Simoneit, 1999; Lin
et al., 2010; Blanchard et al., 2014).
The diagnostic ratios between homologues in series of biomolecules
are another feature used to identify the origin of fine OA are. For n-alkanes and
alkanoic acids, the odd- and even-carbon preferences are indicators of
biogenic sources (Tsapakis et al., 2002). A lack of carbon preference is
indicative of fossil sources. For the case of PAHs, some ratios can be used
to identify emissions from fossil fuel combustion (Zhang et al., 2005).
Source apportionment studies based on organic molecular markers have
accomplished a better understanding of the emission sources in urban areas.
This approach considers two main principles: (1) that organic molecular
markers are present in relatively high concentrations in emissions from a
specific source and in lower concentrations in the remaining sources, and
(2) that they react slowly enough in the atmosphere to be conserved during
transport from the source to the observation/receptor site (Schauer et al.,
1996; Lin et al., 2010). The use of organic molecular markers in the last
decade has proven to be a powerful method to identify and attribute emission
sources in urban areas (Alves et al., 2001; Fraser et al., 2003; Abas et
al., 2004; Kalaitzoglou et al., 2004; Zheng et al., 2005; Feng et al., 2006;
Huang et al., 2006; Li et al., 2006; Park et al., 2006; Alves et al., 2007;
Chow et al., 2007; Ke et al., 2007; Stone et al., 2008; Amador-Muñoz et
al., 2010; Yin et al., 2010; Pietrogrande et al., 2011; Perrone et al.,
2012; Giri et al., 2013; Villalobos et al., 2015; Watson et al., 2015; Zheng
et al., 2015). In spite of recent research interest on organic molecular
markers for source apportionment, the application of this approach started
in the 1980s (Simoneit, 1985, 1986; Eatough et al., 1989; Simoneit
and Mazurek, 1989) and continued in the 1990s (Simoneit et al., 1990, 1991; Schauer et al., 1996; Simoneit, 1999; Schauer and Cass, 2000),
especially with the development of organic source profiles for primary
emission sources (Rogge et al., 1991, 1993a, b, c, 1994, 1997; Fraser
et al., 1999; Schauer et al., 1999) and alternative receptor models (Paatero
1997; Wold et al., 2001). Furthermore, improved source apportionment methods
have been developed (Chen et al., 2011; Kelly et al., 2013; Watson et al.,
2015), while other methods have been suggested to be dropped as receptor
models (Hopke, 2015). More detailed information about source apportionment
methods can be found elsewhere (Reff et al., 2007; Viana et al., 2008; Lin
et al., 2010; Nozière et al., 2015).
For the Monterrey metropolitan area (MMA), the third largest urban center of
Mexico, there is a growing concern to determine the emission sources of fine
OA. It was recently determined that fine OA accounts for 36–71 % of
PM2.5 mass in this urban center (Mancilla et al., 2015). Previously,
Martínez et al. (2012) estimated an OA fraction of ∼ 40 % of the
PM2.5 for the MMA. According to a recent tunnel study, PM2.5
emissions from gasoline-powered vehicles (one of the major emission sources
in the MMA) contain as much as 55 % of carbonaceous material (Mancilla and
Mendoza, 2012). To date, only one study has addressed the chemical
characterization of fine OA in the MMA, but it focused exclusively on the
levels of PAHs (González-Santiago, 2009). More importantly, the present
study would be the second of this kind in Mexico and the first one for the
MMA; previously, Stone et al. (2008) reported an evaluation of molecular
organic markers for source apportionment at Mexico City. This city has a
temperate and wet climate, while the MMA has a dry and extreme climate with
scarce rains. It is well known that climate conditions can affect the air
quality in urban areas. Extreme climates, including high temperatures, could
increase the concentrations of air pollutants. For example, warm and dry
climates promote photochemical reactions in the atmosphere producing
secondary OA. The unique geography and the changeable climate as well as the
typical industries of the region make the MMA unique and different from
other Mexican cities.
Location and municipalities of the Monterrey metropolitan area
(MMA); the sampling site was set up in the downtown.
Methodology
Sampling site
The MMA has a population of 4.2 million inhabitants (INEGI, 2011) and is
considered the largest urban area in northeastern Mexico and the
third-largest urban center in the country. The MMA is composed of 12
municipalities that overall cover an area of 6680 km2 (SEDESOL et al.,
2007), as shown in Fig. 1. The MMA has a vehicular fleet of 1.7 million
vehicles (INEGI, 2010) with a composition of approximately 73 %
gasoline-powered vehicles (passenger cars), 25 % diesel-powered vehicles
(buses and trucks) and 3 % motorcycles. In addition, industrial activity in the MMA is dominated by manufacturing industries, construction and
electricity, transport, restaurants, and other local services. The MMA has a
network of air quality monitoring stations (Sistema Integral de Monitoreo Ambiental, SIMA). For this study, PM2.5
samples were collected at only one site placed in the facilities of the
downtown monitoring station of the SIMA network (25∘40′32′′ N,
100∘20′18′′ W), 556 m.a.s.l. (meters above sea level). The sampling site is
affected mainly by traffic and emissions from a wide range of industrial
activities (e.g., steel and cement production). The vegetation around the
sampling site includes dispersed and scarce grass, shrubs, and street tree
systems in the immediate vicinity as well as in the periphery. The sampling
site selection was based on coefficients of divergence (COD) analysis using
the 24 h average PM2.5 concentrations recorded in 2009 by the SIMA
network. Details about this analysis can be found elsewhere (Mancilla et
al., 2015).
Monitoring experiments conducted for this study.
Campaign
Period
Sampling days
Samples
Field blanks
Spring 2011
28 May to 11 June
7∗
14
2
Fall 2011
22 October to 3 November
7∗
14
2
Spring 2012
6 to 19 June
14
27
2
Fall 2012
13 to 26 October
14
56
4
∗ Non-consecutive days.
Sampling periods and instruments
The samples were collected during the spring and fall of 2011 and 2012
(Table 1). For every sampling day, two consecutive 12 h samples were
taken to obtain information for daytime and nighttime periods. The daytime
sampling was performed from 06:00 to 18:00 (local time), while
nighttime samples were collected from 18:00 to 06:00 the next day.
For the spring and fall 2011 campaigns, sampling periods were performed on
alternate days. For example, for the spring of 2011, the first sampling day
was on 28 May, the second on 30 May, and so on. For the campaigns of 2012,
both were conducted for 14 consecutive sampling days.
Carbonaceous aerosol samples were collected using high-volume filter-based
instruments with a PM2.5 inlet (TE-6001-2.5, Tisch Environmental Inc.),
operating at a flow of 1.13 m3 min-1. The flow rates for the high-volume
samplers were calibrated at the start and end of each monitoring campaign.
For each high-volume sampler, the calibration was carried out using a
calibration orifice (NIST Traceable Calibration Certificate). The samplers
were mounted on the rooftop of the monitoring station 3 m above the
ground. One high-volume sampler was used for each campaign, except for the
campaign of fall 2012, in which two high-volume samplers were deployed and
operated simultaneously to collect pairs of samples for each diurnal and
nighttime sampling period. Fine particles were collected on 8 in.×10 in. quartz microfiber filters (Whatman QMA). Filters were previously
pre-fired for 8 h at 600 ∘C in a furnace to remove residual
carbon and stored in baked aluminum foil within sealed plastic bags
(Ziploc®) until they were used. After sampling, loaded
8 in.×10 in. filters were stored in tall 8 oz. glass jars (VWR,
IR221-0250). Loaded filters were placed in a cooler with blue ice for
immediate transport from the sampling site to the laboratory. All loaded
filters were stored in a freezer at -20 ∘C to prevent the
evaporation of volatile compounds until they were analyzed. A total of 111 samples and 10 field blanks were collected throughout the study.
Ambient measurements
For spring 2011, analyses for each 12 h sample were carried out as
discussed by Mancilla et al. (2015). Even though the samples collected for
spring 2011 exhibited high levels of OC, some concentrations for different
organic compounds of interest were low (0.03 to 0.16 ng m-3). In
addition, the OC concentrations for the last three campaigns (fall 2011,
spring 2012, and fall 2012) were on average up to 35 % lower than OC
concentrations of spring 2011. Based on these findings, composites were
formed for the last three campaigns to ensure higher levels of collected
mass used to identify the organic molecular markers. Weekday–weekend and
daytime–nighttime differences of fine OC levels were investigated and
considered in order to pool sample filters into weekday and weekend composites for
the last three campaigns (Mancilla et al., 2015). Each composite included
only daytime or nighttime samples collected during weekdays (Mon–Thu) or
weekend (Fri–Sun). Sample groups for composites varied from two to six
sample filters. Thereafter, the number of individual samples (or filters)
was reduced from 111 to 43 representative samples. The composites made for
this study are described in Table 2.
Characteristics of individual and composite samples for each
monitoring campaign. Values for PM2.5 are the averages obtained from the SIMA network. OC
concentration values are the average values reported by Mancilla et al. (2015). SD represents standard deviation, I indicates that the dates
included were analyzed individually, C indicates that the dates included
were pooled to form a composite, D represents daytime sampling, N represents
nighttime sampling, 1 refers to weekday sampling, and 2 refers to weekend
sampling.
Campaign
Description
Dates included
PM2.5
OC
(µg m-3)
SD
(µg m-3)
SD
Spring 2011
ID1
30 May, 1, 9, 11 Jun
20.3
8.7
8.2
2.7
ID2
28 May, 3, 5 Jun
22.5
15.1
10.7
5.7
IN1
30 May, 1, 9, 11 Jun
25.1
10.2
6.3
1.5
IN2
28 May, 3, 5 Jun
31.7
22.6
8.5
4.3
Fall 2011
CD1
24 Oct, 1 Nov
18.5
3.2
8.7
1.6
CD1
26 Oct, 3 Nov
18.1
12.4
8.2
5.4
CN1
24 Oct, 1 Nov
13.5
1.6
4.7
0.3
CN1
26 Oct, 3 Nov
12.9
9.4
5.5
2.5
CD2
22, 28, 30 Oct
20.8
11.6
9.3
3.3
CN2
22, 28, 30 Oct
15.1
6.1
6.7
2.1
Spring 2012
CD1
11, 12 Jun
17.1
3.7
7.6
2.5
CD1
13, 14 Jun
19.3
1.7
6.2
0.6
CD1
18, 19 Jun
12.6
0.7
5.1
0.9
CD1
6, 7 Jun
18.3
2.0
8.8
1.0
CN1
11, 12 Jun
20.3
0.5
4.3
0.8
CN1
13, 14 Jun
15.2
0.1
3.3
0.1
CN1
18, 6, 7 Jun
9.3
1.5
4.0
0.8
CD2
8, 9, 10 Jun
18.4
3.7
8.3
0.7
CD2
15, 16, 17 Jun
10.7
2.2
4.6
0.6
CN2
8, 9, 10 Jun
18.8
6.6
5.3
1.0
CN2
15, 16, 17 Jun
9.3
3.1
–
2.8
0.6 –
Fall 2012
CD2
13, 14, 27 Oct
15.8
2.9
9.3
1.3
CN2
13, 14, 28 Oct
8.9
2.7
6.8
1.1
CD1
15, 16 Oct
17.6
4.1
10.1
3.1
CN1
15, 16 Oct
23.4
11.4
11.4
3.9
CD1
17, 18 Oct
17.6
11.9
13.7
6.2
CN1
17, 18 Oct
13.4
2.4
8.7
1.9
CD2
19, 20, 21 Oct
29.7
5.5
10.9
2.1
CN2
19, 20, 21 Oct
23.1
1.1
6.6
2.1
CD1
22, 23 Oct
23.6
3.9
8.1
0.2
CN1
22, 23 Oct
13.7
2.2
4.5
0.3
CD1
24, 25 Oct
13.9
1.6
9.3
3.9
CN1
24, 25 Oct
10.8
1.0
5.3
0.6
Solvent-extractable molecular markers were quantified using gas
chromatography mass spectrometry (GC/MS) using dichloromethane (DCM) and
methanol (MeOH) (high purity, 99.9 %, Fisher Scientific). Filters were
spiked with 50 µL of the following deuterated internal standards
(Sigma Aldrich): n-hexadecane-d34, n-hexatriacontane-d74, n-eicosane-d42,
n-triacontane-d62, vanillin-d3, benzophenone-d5, chrysene-d12,
dibenz(a,h)anthracene-d14, naphthalene-d8, pyrene-d10, benzo(e)pyrene-d12,
coronene-d12, decanoic acid-d19, palmitic acid-d31, stearic acid-d35,
levoglucosan-13c6, and cholesterol-d6. Each individual filter or sample
composite was extracted three times with DCM. During each extraction, enough
DCM was added and then ultrasonic agitation was applied for 20 min using a
sonicator (Bransonic®, model 5510R-DTH). The extracts were
combined and then concentrated by evaporation under a gentle flow of
ultra-high-purity nitrogen until the extract reached a volume of ∼ 5 mL. The extracts were filtered through a pre-fired quartz filter,
subsequently reduced in volume to 250 µL, and then separated into
three fractions. One fraction was a direct portion of the 250 µL
extract for direct analysis by GC/MS, and the other two fractions were used
for chemical derivatizations. One fraction was methylated using diazomethane
(CH2N2) to convert carboxylic acids to their respective methyl
esters. Another fraction was silylated using a combination of BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) and TMCS (trimethylchlorosilane) to
convert sterols and sugars to their respective trimethylsilyl esters. For
methylation, 50 µL of a CH2N2 solution was combined in a
vial with 50 µL of extract. For the silylation, 50 µL of
BSTFA+TMCS (molar ratio 99:1) was combined with 50 µL sample
extract. Then, the mixture was allowed to react for 3 h at 65 ∘C. The
quantification and identification of organic compounds was based on
comparisons with authentic standards, retention times, literature mass
spectra, and fragmentation patterns using HP ChemStation. A detailed
description of the extraction and analysis procedures as well as the
chromatograph and column used can be found in Brown et al. (2002).
Molecular diagnostic ratios
To investigate the origin of fine organic aerosols, the following diagnostic
ratios were used.
Carbon preference index (CPI)
The CPI is an indicator of the measure of odd- or even-carbon homologues
series of organic compounds within a sample. Based on several studies (Abas
and Simoneit, 1996; Tsapakis et al., 2002; Harrad et al., 2003), the CPI for
n-alkanes (odd to even ratio) was calculated as
CPI=∑C17toC33∑C16toC32,
and that for n-alkanoic acids (even to odd ratio) as
CPI=∑C10toC32∑C11toC31.
The CPI is an important indicator that is used to determine whether
emissions come from natural or anthropogenic sources. For both n-alkanes and
n-alkanoic acids, values of CPI > 1 indicate that hydrocarbons and
carboxylic acids are emitted from natural sources. In contrast, values of
CPI ≤ 1 (or close to 1) indicate that they are emitted from
anthropogenic sources (Gogou et al., 1996; Alves et al., 2001;
Gelencsér, 2004).
Another useful indicator that is used to specify the origin of the emissions
is the carbon number with maximum concentration (Cmax). Hydrocarbons and
carboxylic acids of high molecular weight (> C25) are
emitted from biogenic sources, while those with lower molecular weight (≤ C25) are mainly emitted from fossil fuel combustion processes (Alves
et al., 2001; Young and Wang, 2002; Gelencsér, 2004).
PAH diagnostic ratios for different source categories. The PAH abbreviations are IP: indeno(123cd)pyrene; BgP:
benzo(ghi)perylene;
BAA: benz(a)anthracene; CRY: chrysene; FLT: fluoranthene; PYR: pyrene; BeP:
benzo(e)pyrene; BaP: benzo(a)pyrene.
Diagnostic ratio
Value
Source
Reference
IP / (IP + BgP)
< 0.20
Petrogenic
Katsoyiannis et al. (2011)
> 0.20
Pyrogenic
Katsoyiannis et al. (2011)
0.20–0.50
Petroleum combustion
Yunker et al. (2002)
> 0.50
Coal, grass, and wood combustion
Yunker et al. (2002)
BAA / (BAA + CRY)
< 0.20
Petrogenic
Katsoyiannis et al. (2011)
0.20–0.35
Coal combustion
Akyüz and Cabuk (2010)
> 0.35
Pyrogenic, vehicle emissions
Katsoyiannis et al. (2011)
FLT / (FLT + PYR)
< 0.40
Petrogenic
Katsoyiannis et al. (2011)
> 0.40
Pyrogenic
Katsoyiannis et al. (2011)
0.40–0.50
Fuel combustion
Katsoyiannis et al. (2011)
> 0.50
Diesel emissions
Ravindra et al. (2008)
(BaP + BeP) / BgP
> 0.60
Traffic
Katsoyiannis et al. (2011)
< 0.60
Non-traffic
Katsoyiannis et al. (2011)
Diagnostic ratios of PAHs
Other indicators that have been used as markers of different source
emissions of OA are the PAH diagnostic ratios (DRs) (Dvorská et al.,
2011; Katsoyiannis et al., 2011). The DRs calculated in this study are shown
in Table 3. The values listed in this table can be found elsewhere (Ravindra
et al., 2008; Tobiszewski and Namieśnik, 2012).
Chemical mass balance model
The CMB is a single-sample receptor model that can be stated in terms of the
contribution from p independent sources to all chemical species as follows:
xij=∑k=1pgikfkj+eij,
where xij is the measured concentration of species j in sample i,
fkj is the concentration of species j in the emissions of source k,
gik is the contribution of source k to sample i, and eij is the model
error. This model considers a prior knowledge of the source profiles and
that the components of the source emissions do not undergo changes during
their transport from the source to the receptor. CMB provides an effective
variance-weighted least-squares solution to the overdetermined set of mass
balance equations (Eq. 1). CMB takes into account the known uncertainties in
the ambient measurements and the source emission data to minimize the
chi-square (χ2) goodness-of-fit parameter for each sample i:
χ2=∑j=1mxj-∑k=1pgjkfkσxj2+∑k=1pσgjkfk,
where σxj is the standard deviation of the concentration of
species j, σgjk is the standard deviation of the gjk, and is
m the total number of species. The US EPA-CMB8.2 software has been
successfully used to apportion source contributions to ambient PM2.5
(Ke et al., 2007; Stone et al., 2008; Watson et al., 2008; Kleeman et al.,
2009; Schneidemesser et al., 2009; Yin et al., 2010; Perrone et al., 2012;
Villalobos et al., 2015) and was also used in the current study. More
details about CMB can be found elsewhere (e.g., Henry et al., 1984; Watson
et al., 2008).
Source profiles
The source profiles used in this work were taken from the most comprehensive
studies available. The selection of the source profiles was based on
previous source apportionment studies carried out for Mexican urban areas.
Firstly, Stone et al. (2008) used CMB with organic molecular markers
profiles to estimate contributions from gasoline- and diesel-powered
vehicles, vegetative detritus, and biomass burning in Mexico City. Secondly,
Martinez et al. (2012) based their factor analysis on trace elements
identified primary sources such as industrial sources, motor vehicle exhaust
and biomass burning in the MMA. Finally, from these studies, seven primary
source profiles were selected.
The source profiles for gasoline- and diesel-powered vehicles were taken
from Schauer et al. (2002) and Fraser et al. (2002), respectively. In the
MMA, the gasoline vehicle fleet seems to be rather well maintained and of a
recent model year, while the diesel vehicle fleet is composed of heavy-duty
vehicles such as trucks and buses. Therefore, the profiles reported for
catalyst-equipped gasoline-powered motor vehicles emissions and heavy-duty
trucks from dynamometer tests were used.
From evidence of industrial sources in Mexican urban atmospheres, source
profiles for natural gas combustion and fuel oil combustion were taken from
Rogge et al. (1993c) and Rogge et al. (1997), respectively.
Finally, source profiles for meat-cooking operations, vegetative detritus,
and biomass burning were taken from Schauer et al. (1999), Rogge et al. (1993b), and Schauer et al. (2001a), respectively. Most traditional
restaurants activities of the region of study include meat charbroiling
operations. For biomass burning, given the existence of softwood and
hardwood sources in the region (Zurita, 2009), the softwood pine and
hardwood oak profiles were used in this study. These last profiles were used
separately because they are highly collinear. Regarding vegetative detritus,
a source profile was included based on the contributions determined by Stone
et al. (2008) in Mexico City along with the fact that the MMA is surrounded
by rural areas with vast green covers. Therefore, it is possible to have an
impact from transport of biogenic emissions.
For individual organic compound quantification, an uncertainty of ±20 % of the measured concentration was used for all ambient samples and
source profiles (Schauer et al., 2000; SRM 1649a, 2007; SRM 1649b, 2009;
Fraser et al., 2003; Schantz et al., 2005). A detailed description of the
source profiles and settings used to perform the CMB in this study can be
found in Fraser et al. (2003). For the current application, the fitting
species for CMB included 20 organic compounds along with EC and overall OC.
The molecular markers included seven n-alkanes (C27-C33), four petroleum
biomarkers (17a(H),21b(H)-29-norhopane, 17a(H),21b(H)-hopane, 22R + S
17a(H),21b(H)-30-homohopane, and 22R + S 17a(H),21b(H)-30-bishomohopane),
five polycyclic aromatic hydrocarbons (benzo[a]anthracene,
benzofluoranthenes, benzo[a]pyrene, indeno[123-cd]pyrene and
benzo[ghi]perylene), two saturated fatty acids (C16:0 and C18:0),
cholesterol, and levoglucosan.
Results and discussion
Resolved organic aerosols
The results for the chemical characterization of the fine organic aerosol
for the MMA are summarized in Table S1. In this and other sections, averaged
values for concentrations and other parameters are given ± 1
standard deviation. Most of the PM2.5 daytime concentrations were
20 % higher than nighttime concentrations. The concentrations of OC and EC
were on average 32 % higher during the daytime than the nighttime. In
addition, the OC and EC accounted together for 28–49 and 46–55 % of
the PM2.5 for spring and fall, respectively. In the spring, the daytime
carbonaceous fraction was 1.6–1.8 times higher than the corresponding
nighttime fraction, whereas during the fall it was 1.1–1.2 times higher.
The average OC / EC ratios ranged from 7.4 to 12.6 during this study. Detailed
information and analysis of the carbonaceous aerosol for this study can be
found in Mancilla et al. (2015).
Concentrations of the resolved organic compound classes in the
MMA. For (a) the n-alkanoic and alkenoic acids and wood smoke tracer were not
included in the chemical analysis. For (b), (c) and (d) the n-alkanoic acids
are divided by a factor of 10.
All samples collected during this study were analyzed for eight organic
compound classes except those samples collected in the spring of the 2011
campaign, for which carboxylic acids, wood smoke markers, and nitro-PAH
compounds were not included for the chemical analysis. Therefore, the
overall contribution of the resolved organic compounds to OC in the spring
of 2011 is not directly comparable to that of the last three campaigns. The
data for seven of the eight resolved compound classes are shown in Fig. 2.
For the last three campaigns, the n-alkanoic acids were the most abundant,
followed by n-alkanes, wood smoke markers, and levoglucosan/alkenoic acids.
The PAHs and triterpene hydrocarbons were less abundant. The same tendency
was observed in the spring of 2011, except that the n-alkanes were the most
abundant. The concentrations of nitro-PAH were neglected because their
levels were below the detection limit of the method.
The daytime and nighttime concentrations of the resolved organics for
spring 2011 accounted for 0.49 ± 0.52 and 0.46 ± 0.41 % of
the ambient OC, respectively. These contributions were much lower than the
following three campaigns due to the fact that some compounds classes were
not included. For spring 2012 and all fall campaigns, the total daytime
concentrations of the resolved organics accounted for 7.58 ± 4.89
and 2.64 ± 1.82 to 4.67 ± 1.76 %, respectively, while the
total nighttime concentrations accounted for 11.0 ± 6.3 and
3.2 ± 2.4 to 8.0 ± 4.0 %, respectively. These observations
are consistent with the findings that, typically, around 84 % of the fine
OC is either non-extractable or will not elute from the GC column (Schauer
and Cass, 2000). In the following sections the organic composition of the
fine organic aerosols will be analyzed using several diagnostic ratios to
identify the primary emission sources. Then the relative contribution of
each primary source to the PM2.5 will be calculated by using the CMB
receptor model.
n-Alkanes and hopanes
The n-alkanes have two main sources: petroleum product utilization and
natural vegetation waxes. The latter source consists of the longer-chain
plant lipids (> C20) such as n-alkanes (Simoneit and Mazurek,
1982). In this study, the n-alkanes in the range of C17–C33 were
detected. For the samples collected for spring 2011 and fall 2011, the
average daytime and nighttime concentrations of n-alkanes were 1.6 and 2.3
times higher for the fall than the spring, respectively. This is consistent
with the high contribution of the OC to PM2.5 and the lowest OC / EC
ratios exhibited during the fall (Mancilla et al., 2015). In addition, the
average temperature in the fall was 18.7–22.1 ∘C vs.
27.8–29.4 ∘C in the spring. Low temperatures typically promote
the utilization of petroleum products. In Mexico, the government sets the
tariff rates for electric energy consumption with regard to the
temperature; during the cold seasons the government removes the subsidy on
domestic electric energy due to the demand for fossil fuels in those
seasons, and the sampling years of this study were not the exception (SENER,
2013). The average CPI values of n-alkanes in the spring were 1.5 ± 0.3
(range: 1.1–1.9) in the daytime and 1.7 ± 0.5 (range: 1.1–2.6) in the
nighttime, while the CPI values in the fall were 1.0 ± 0.3 (range:
0.7–1.2) in the daytime and 0.9 ± 0.1 (range: 0.7–1.0) in the
nighttime. The CPI values in the spring suggest the mixed contribution of
anthropogenic and biogenic emission sources, whereas those values in the
fall indicated a dominance of anthropogenic emissions. In addition, the
contribution of anthropogenic emission sources is confirmed by the presence
of petroleum biomarkers (hopanes: range of 0.06 to 2.36 ng m-3) and
n-alkanes ≤ C25 (Fig. 3). For the fall, the average daytime and
nighttime concentrations of hopanes were 2.3 and 4.2 times higher than in
the spring, respectively. Similarly, the presence of biogenic emissions due
to Cmax was found at C27, C29, or C31 (Fig. 3).
This carbon number dominance and trace levels of hopanes are characteristics of
plant wax emissions and urban traffic emissions, respectively (Standley and
Simoneit, 1987; Cass, 1998; Simoneit et al., 2004).
Carbon number distribution of n-alkanes in the Monterrey
metropolitan area (MMA) for (a) spring 2011, (b) fall 2011, (c) spring
2012, and (d) fall 2012. The black line represents the daytime
concentrations,
while the dotted line represents the nighttime concentrations.
For spring 2012 and fall 2012, the behavior of n-alkanes was the opposite
of 2011. The average daytime and nighttime concentrations of n-alkanes were
1.5 and 2.0 times higher in the spring than in the fall, respectively. The
EC levels remained similar to those of 2011, but the OC levels were higher
during fall 2012, increasing the OC / EC ratios. Some of these ratios
exhibited high peaks suggesting a contribution from primary emission sources
with elevated OC / EC ratios like biomass burning (Mancilla et al., 2015). The
average temperature in the fall was 23.5–26.4 ∘C vs.
28.0–30.7 ∘C in the spring. The average fall temperatures were
not consistent with the average of 2011. In the fall of 2012, the warmer
temperatures might have promoted less utilization of some fuels compared to 2011 in which lower temperatures could have promoted their utilization,
increasing the n-alkanes' concentrations in fall 2011. The average CPI values
of n-alkanes in spring 2012 were 0.9 ± 0.1 (range: 0.8–1.1) in the
daytime and 1.2 ± 0.1 (range: 1.2–1.3) in the nighttime, while the CPI
values in fall 2012 were 1.3 ± 0.1 (range: 1.0–1.4) in the daytime
and 1.5 ± 0.2 (range: 1.3–1.6) in the nighttime. From these CPI
values, it appears that biogenic emissions are relevant in all sampling
periods. For spring 2012 daytime, the emissions appear to be heavily
dominated by anthropogenic emissions due to the low CPI value exhibited. The
presence of petroleum biomarkers supports the relative contribution of
anthropogenic emissions. However, in 2012 the hopane levels were ∼ 35 % lower at daytime and ∼ 43 % lower at nighttime than those in
the previous year. The low hopane levels (range of 0.10 to 1.49 ng m-3)
highlight the possible presence of biogenic emissions (Fig. 2).
PAHs
Twelve PAH compounds – fluoranthene (FLT), acephenanthrylene (ACE), pyrene
(PYR), benzo(a)anthracene (BAA), chrysene (CRY), benzo(k)fluoranthene + benzo(b)fluoranthene (BFA), benzo(a)pyrene + benzo(e)pyrene (BaP + BeP),
perylene (PER), indeno(123cd)pyrene (IP), benzo(ghi)perylene (BgP),
dibenz(ah)anthracene (DaA), and coronene (Cor) – were identified in the MMA
fine samples. For both sampling years, the average daytime and nighttime
concentrations of PAHs were 1.4–5.9 and 1.4–2.4 times higher in the fall
than in the spring, respectively. This is consistent with the high
contribution of the OC to the PM2.5 during the falls. Independent of
the season, the daytime concentrations were 1.3–1.6 times higher than
nighttime concentrations. These seasonal differences are consistent with the
results of Guo et al. (2003) and Li et al. (2006), where PAH levels were
2–4 and 14.4 times higher in the cold seasons than in the warm seasons,
respectively.
Average diagnostic ratios of PAHs in MMA. D represents daytime and N represents nighttime.
Season
Period
IP / (IP + BgP)
BAA / (BAA + CRY)
FLT / (FLT + PYR)
(BaP + BeP) / BgP
Spring 2011
D
0.41 ± 0.05
0.34 ± 0.28
0.50 ± 0.03
0.19 ± 0.24
N
0.46 ± 0.02
0.62 ± 0.30
0.49 ± 0.10
0.07 ± 0.06
Fall 2011
D
0.35 ± 0.10
0.50 ± 0.03
0.55 ± 0.12
2.67 ± 0.75
N
0.51 ± 0.26
0.17 ± 0.29
0.66 ± 0.25
4.63 ± 4.49
Spring 2012
D
0.33 ± 0.13
0.01 ± 0.003
0.72 ± 0.20
4.48 ± 2.19
N
0.47 ± 0.34
0.06 ± 0.12
0.89 ± 0.02
10.40 ± 2.45
Fall 2012
D
0.34 ± 0.04
0.35 ± 0.07
0.60 ± 0.06
0.55 ± 0.08
N
0.36 ± 0.02
0.40 ± 0.09
0.68 ± 0.05
0.52 ± 0.13
As illustrated in Fig. 4, the high-molecular-weight (HMW) PAHs were the most
abundant for the MMA. The presence of HMW PAHs such as BaP + BeP, IP, and
BgP is an indication of gasoline-powered vehicle emissions (Katsoyiannis et al., 2011; Tobiszewski and Namieśnik, 2012). In addition, a possible
contribution of diesel-powered vehicles is indicated by the low
concentrations of the low-molecular-weight (LMW) PAHs such as FLT, PYR, and
CRY. To identify the emission sources of PAH, diagnostic ratios were
calculated (Table 4). These ratios should be used with caution because PAHs
are emitted from a variety of emission sources, particularly combustion
sources, and their profiles can be modified due to their reactivity
(Tsapakis et al., 2002). From these ratios, the source could be determined
(e.g., pyrogenic and petrogenic sources). Then, these qualitative
conclusions will be considered for the source apportionment to estimate the
relative contribution of primary emission sources. The average ratios of
IP / (IP + BgP) indicate that ambient PAHs in the MMA originated from gasoline
and diesel combustion, whereas the ratios of BAA / (BAA + CRY) show the
presence of petrogenic sources as well as vehicle emissions. To complement
these results, the ratios of (BaP + BeP) / BgP identified a marked
contribution of non-traffic sources for the spring of 2011, traffic sources
for fall 2011 and spring 2012, and mixed sources for fall 2012. The presence of gas-phase PAHs was not evaluated and thus no information
on gas–particle partitioning of these semivolatile species is available.
Thus, only diagnostic ratios for HMW PAHs were calculated for this study
because those PAHs exhibit low volatility (Kavouras et al., 1999).
Mass concentration distribution of PAHs in the Monterrey
metropolitan area (MMA) for (a) spring 2011, (b) fall 2011, (c) spring
2012,
and (d) fall 2012. Coronene was included in all monitoring campaigns except
in spring 2011.
In this study, the average total concentrations of the quantified PAHs
(TPAHs) were 2.42 ± 2.45 ng m-3 (range: 0.65–8.31
and 4.11 ± 2.62 ng m-3 (range: 1.42–11.97 ng m-3) during
2011 and 2012, respectively, whilst those quantified by
González-Santiago (2009) at two different sites in the MMA were
1.30 ± 1.64 ng m-3 (range: 0.05–6.93) and 1.70 ± 1.88 ng m-3 (range: 0.07–9.14 ng m-3). The lowest concentrations
were obtained during the spring because its average temperature was
statistically higher than during the fall seasons (p< 0.05). The
volatility of PAH increases with temperature; as a result low concentrations
are obtained in comparison with fall and winter seasons. For this study the
concentrations of PAH were lower during the spring than concentrations
during fall; this pattern was exhibited during the two sampling years.
González-Santiago (2009) identified only 6 PAHs, while in this study
were 12 identified. In the current study, the total concentrations
calculated for the six common PAHs were from 3 to 8 times higher than those
estimated by González-Santiago (2009). Similar concentrations (between
0.04 and 1.78 ng m-3) were also reported for six individual
PAHs in urban samples collected in Mexico City (Stone et al., 2008). Of the
same PAHs identified among these studies, their levels were in the same
concentration range. However, the TPAH levels in the MMA compared to those
calculated by Marr et al. (2006) (20–100 ng m-3) in Mexico City were
found at appreciably lower concentrations. In addition, Marr et al. (2006)
suggest that vehicles are the major source of PAHs. They demonstrated that
PAHs and carbon monoxide (CO) concentrations are well correlated in Mexico
City and, given that 99 % of CO emissions are emitted by motor vehicles,
this source is a major contribution of PAH emissions. For the current study,
daytime correlations (r) of +0.76 (p > 0.05) were found between
TPAH and CO, reinforcing the conclusion that motor vehicles are one major
source of PAH emissions for the MMA. The concentrations of CO during this
study were obtained from the SIMA network.
The TPAH concentrations were compared with the EC and OC levels.
TPAH concentrations measured in the MMA exhibited fair daytime correlations
with EC (r=+0.79; p > 0.05), but low correlations with OC
(r=+0.57; p > 0.05). According to Marr et al. (2004), the strong
correlation between TPAH and EC indicated the relative contribution of
diesel-powered vehicle exhaust, while weak correlations may be due to the
low concentrations of EC determined during this study (Mancilla et al.,
2015). Furthermore, the weak correlation between TPAH and EC suggests the
presence of emission sources with an elevated OC / EC.
n-Alkanoic acids
The carboxylic acids or n-alkanoic acids are mainly derived from biogenic
emissions (Rogge et al., 1993b). However, these acids have also been
identified in several primary sources such as cooking operations (Rogge et
al., 1991; Schauer et al., 2001b) and fossil fuel combustion (Schauer et
al., 2002). The n-alkanoic acids from C10 to C32 were quantified
only for the three last monitoring campaigns. The n-alkanoic acids were the
most abundant, accounting for 69 ± 16 % at daytime and 78 ± 11 % of the total resolved organics at nighttime for both spring and fall.
The daytime and nighttime concentration levels were 2 times higher in the
spring than in the fall. As can be seen in Fig. 5, the n-alkanoic acids
measured in the MMA were dominated by hexadecanoic acid (palmitic acid) and
octadecanoic acid (stearic acid). This dominance is consistent with
measurements in other locations (Fraser et al., 2002; Simoneit 2004; Li et
al., 2006).
Carbon number distribution of n-alkanoic acids in the Monterrey
metropolitan area (MMA) for (a) fall 2001, (b) spring 2012, and (c) fall
2012. The black line represents the daytime concentrations while the dot
line represents the nighttime concentrations.
The average CPI values of n-alkanoic acids in the fall of 2011 were
4.3 ± 1.0 (range: 3.3–5.3) during the day and 5.0 ± 0.4 (range:
4.6–5.4) at night. For the 2012 year the CPI values in the spring were
3.6 ± 0.6 (range: 2.9–4.5) during the day and 4.7 ± 0.8 (range:
3.9–5.8) at night, while the CPI values in the fall were 4.7 ± 0.3
(range: 4.3–5.1) during the day and 5.3 ± 1.1 (range: 4.0–6.8) at
night. These elevated CPI values indicated the significant influence of
biogenic sources such as microbial and plant wax sources. The n-alkanoic
acids < C20 are derived in part from microbial sources, while
those > C20 are from vascular plant waxes (Guo et al., 2003;
Yue and Fraser, 2004; Simoneit et al., 2004). Figure 5 clearly shows the influence
of long-chain (> C20) plant wax particles for the MMA. The
CPI values in this study were consistent with those obtained by Wang and
Kawamura (2005) (CPI: 5.3–10) and Yue and Fraser (2004) (CPI: 3.2–11.2).
Regardless of the elevated CPI values obtained for the MMA, the values were
not as high as those reported by the other mentioned studies due to a
scarcity of green vegetation covers in the MMA.
The alkenoic acids only included cis-9-octadecenoic acid (oleic acid) and
trans-9-octadecenoic acid (elaidic acid). The concentrations of
cis-9-octadecenoic acid ranged from 0.96 to 15.38 ng m-3,
while the concentrations of trans-9-octadecenoic acid ranged from 2.11 to 13.35 ng m-3. The ratio of octadecanoic acid to
cis-9-octadecenoic acid has been used as an indicator of the atmospheric
chemical processing (aging) of aerosols, since the unsaturated acids are
susceptible to atmospheric oxidation (Brown et al., 2002; Yue and Fraser,
2004). In this study, the average ratios were 5.0 (range: 1.5–9.4) during
the day and 3.8 (range: 2.5–4.9) at night for the spring, vs. 20.3
(range: 4.7–38.6) during the day and 21.0 (range: 10.5–29.1) at night for
the fall. The transport of aerosols from local and rural sources can lead to
the loss of cis-9-octadecenoic acid producing high ratios of octadecanoic acid to
cis-9-octadecenoic acid. Similarly, air mass stagnation may lead to
high oxidant concentrations in an urban atmosphere producing high ratios
(Brown et al., 2002; Yue and Fraser, 2004). Therefore, these ratios suggest
that the ambient organic aerosols for the MMA were aged and might be
produced from transport and atmospheric oxidation. Air circulation patterns
(HYSPLIT backward trajectories) during these monitoring campaigns suggested
long-range transport from the northeast and southeast (Mancilla et al.,
2015). As expected, the lowest and highest octadecanoic acid to
cis-9-octadecenoic acid ratios obtained for the MMA were consistent with the
highest OC / EC ratios estimated for the MMA for the same campaign; high OC / EC
ratios identified transport and stagnation scenarios for the spring and
fall, respectively (Mancilla et al., 2015). In addition, these results are
in line with those reported by Brown et al. (2002) and Yue and Fraser (2004), who obtained ratios of 5–11 and 1.0–21.5, respectively.
In addition, a minor biogenic contribution can be identified by the presence
of terpenoic acids such as cis-pinonic acid and pinic acid. These acids are
known to be a secondary, particle-phase products of pinene, which is emitted
from plants, particularly conifers (Plewka et al., 2006; Sheesley et al.,
2004). Both pinonic and pinic acid exhibited higher concentrations in
the spring than in the fall (Table S1 in the Supplement), indicating biogenic emissions from
softwood sources.
Meat-cooking and biomass burning tracers
The major tracers for meat-cooking particles are the steroids, while those for
biomass burning are the anhydrosaccharides and methoxyphenols. All of these
organic tracers were intermittent in only 80 % of the total OA
samples/composites collected during this study.
There was no clear trend between spring and fall samples during the two
sampling years for steroids (Table S1). However, the evidence of cooking
operations' impact on the MMA was confirmed by the presence of cholesterol
and stigmasterol along with hexadecanoic acid, octadecanoic acid, and oleic
acid. Although cholesterol is considered a good marker for meat cooking,
studies have reported unexpectedly high levels of cholesterol from
non-cooking-related sources such as soil and prescribed burns (Sheesley et
al., 2004; Lee et al., 2005; Robinson et al., 2006a).
Anhydrosaccharides are tracers from burning cellulose and hemicelluloses,
whereas the methoxyphenols are a tracer from burning of lignin (Giri et al.,
2013). Levoglucosan, a combustion and pyrolysis product of cellulose, is the
main biomarker used to track biomass burning emissions (Schauer et al.,
2001a). Levoglucosan was not detected in all collected samples. The
levoglucosan found in the samples indicates that biomass burning is
impacting the MMA to some extent (Table S1). The levoglucosan concentrations
varied by sampling dates, ranging from non-detectable levels to 54 ng m-3 for spring. In contrast, levoglucosan was detected in all fall
samples, ranging from 0.14 to 28 ng m-3. The intermittent peaks of
levoglucosan concentrations during the springtime can be explained by the
fact that northeastern Mexico's atmosphere is highly influenced by forest
wildfires and prescribed agricultural burnings during the spring (Mendoza et
al., 2005); this is in line with the idea that high OC / EC ratios obtained,
in a parallel study, were influenced in part by regional transport emissions
(Mancilla et al., 2015). In the case of the fall seasons, the levoglucosan
levels can be associated with local biomass burning due to mild temperatures
encountered during these seasons; a local contribution can be associated
with high OC / EC ratios and stagnation conditions determined for this period
(Mancilla et al., 2015). The high OC / EC rations during spring may have a
contribution from primary sources with elevated OC / EC ratios. The low and
variable levoglucosan concentrations in this study indicate that
wood/vegetation smoke episodes were occasional at the urban site. Average
levoglucosan concentrations of 112.9 and 151.3 ng m-3 were
reported at urban and peripheral sites for Mexico City, respectively (Stone
et al., 2008). Based on an average concentration of levoglucosan, Mexico
City exhibited from 5 to 7 times higher levels than the MMA. These results
are consistent with the concentrations of OC and EC obtained in Mexico City;
the EC concentrations were up to 2 times higher than those observed in the
MMA, whereas the OC concentrations were from 2 to 6 times higher. A study
conducted in Houston, TX, during August–September reported elevated
concentrations of levoglucosan: up to 234 ng m-3 (Yue and Fraser,
2004). Similarly, a study conducted for 14 cities in China during summer and
winter also reported elevated levoglucosan concentrations of 259 ng m-3 (Wang et al., 2006). However, in those studies the vegetation
around the sampling sites included a vast number of parks and woody shrubs,
suggesting a major biomass burning contribution contrary to the MMA. Apart
from those studies, Zheng et al. (2002) reported elevated levoglucosan
concentrations of 166–307 ng m-3 for urban areas of similar
surroundings to the MMA. In this case, it is also possible that the
levoglucosan emissions reported come from industries that have implemented
biomass burning processes for energy generation. As can be seen from the
previous comparison, the contribution of biomass burning is minor for the
MMA in comparison with other urban locations. In addition, biomass burning
contributions are inconsistent with those reported in similar locations to
the MMA.
Resin acids such as dehydroabietic acid, pimaric acid, and isopimaric
acid (Table S1) are secondary tracers from biomass burning (Schauer et al.,
2001a). Dehydroabietic acid was the most abundant resin acid, ranging from
1.94 to 4.39 and 1.95 to 3.69 ng m-3 for spring and fall,
respectively. Then, pimaric acid ranged from not detectable levels to 0.09 and from 0.15 to 0.35 ng m-3 for spring and fall,
respectively. Finally, isopimaric acid ranged from not detectable levels to
0.03 ng m-3 and from 0.06 to 0.12 ng m-3 for spring and fall,
respectively. The results for resin acids are in line with those obtained
for levoglucosan. These results support the low impact from biomass burning
emissions in the MMA, especially from softwood burning (e.g., conifer wood)
during the spring and fall campaigns. In addition, the higher concentrations
of resin acids in fall than in spring are associated with photochemical
activity due to stagnation events in fall.
Source apportionment
CMB was applied using the quantification of individual organic compounds
found in the collected PM2.5 samples. The relative contributions for
gasoline-powered vehicles, diesel-powered vehicles, natural gas combustion,
fuel oil combustion, meat-cooking operations, vegetative detritus, and
biomass burning were estimated. From the source categories selected, the
ones corresponding to natural gas and fuel oil combustion were not
determined as significant for some ambient samples. These two sources were
determined to have contributions that were not statistically different from
zero or were slightly negative and thus were excluded from the model. Model
performance was determined by r2 values ranging between 0.58 and 0.85
and chi-squared (χ2) values between 2.97 and 8.85. Similar values
for r2 and χ2 have been obtained in Fraser et al. (2003) and Schneidemesser et al. (2009). The latter study used composites to
perform the CMB. Another performance metric calculated by EPA-CMB8.2 is the
percent mass explained. Theoretically, values ranging from 80 to 120 %
are acceptable. This ideally can occur when ambient data are not impacted
heavily by SOA because CMB is only able to account accurately for primary
sources. In spite of this limitation, CMB results with low percent mass
explained values have been reported by some studies. In these cases, the
high levels of unexplained mass have been associated with secondary
production (Fraser et al., 2003; Zheng et al., 2005). For the 43 ambient
samples fed to CMB in this study, 18 samples exhibited low percent mass
explained values (ranging from 20 to 77 %), whereas 14 samples had
values around 100 %. These results are in line with the relatively high and
low OC / EC ratios obtained for spring and fall, respectively (Mancilla et
al., 2015). Finally, 11 samples were discarded due to poor performance
parameters calculated. These samples were not exclusively from a particular
monitoring campaign; there were samples from both springs and falls. A
detailed description of the CMB performance and relative contributions for
each sample can be found in Table S2.
Contributions and uncertainty of primary sources to seasonal
average ambient PM2.5 for daytime and nighttime in the MMA (in
µg m-3).
Source category
Spring 2011
Fall 2011
Spring 2012
Fall 2012
Daytime
Nighttime
Daytime
Nighttime
Daytime
Nighttime
Daytime
Nighttime
Gasoline-powered vehicles
2.37 ± 0.56
2.46 ± 0.46
3.70 ± 0.78
2.24 ± 0.51
1.43 ± 0.41
∗
7.51 ± 1.27
3.19 ± 0.54
Diesel-powered vehicles
7.34 ± 0.86
3.81 ± 0.53
13.67 ± 1.59
13.10 ± 1.55
2.93 ± 0.41
∗
13.85 ± 1.58
5.15 ± 0.62
Vegetative detritus
0.22 ± 0.04
0.22 ± 0.04
0.13 ± 0.03
0.27 ± 0.05
0.40 ± 0.06
∗
0.42 ± 0.07
0.31 ± 0.05
Meat-cooking operations
8.24 ± 1.54
11.13 ± 1.85
3.26 ± 0.71
3.86 ± 0.71
9.74 ± 1.20
∗
3.22 ± 0.53
3.37 ± 0.47
Natural gas combustion
0.01 ± 0.01
N.I.
0.05 ± 0.03
0.03 ± 0.01
0.01 ± 0.01
∗
0.10 ± 0.02
0.04 ± 0.01
Biomass burning
0.20 ± 0.05
0.17 ± 0.05
0.17 ± 0.05
0.07 ± 0.02
0.16 ± 0.04
∗
0.01 ± 0.01
0.01 ± 0.01
Fuel oil combustion
N.I.
N.I.
4.18 ± 3.55
3.60 ± 1.20
N.I.
∗
N.I.
0.22 ± 0.48
N.I. means not important. ∗ Samples were discarded due to poor CMB
performance.
The average contributions of primary sources are shown in Table 5. The
vehicle exhaust and meat-cooking operation emissions were the highest for
all monitoring campaigns. When examining the seasonal variation, the
gasoline- and diesel-powered vehicles in falls were up to 5 times higher
than in springs, when cold weather increases the demand for petroleum
products due to low temperatures. The opposite occurred for meat-cooking
operations, their spring emissions were 3 times higher than in fall
seasons. The natural gas combustion, vegetative detritus, and biomass burning
emissions were very low and more constant throughout the springs and falls.
With regard to the daytime and nighttime variations, the vehicle exhaust
were much higher during daytime when traffic is heavier. For the
meat-cooking operations, the emissions were somewhat higher during nighttime
during spring and more constant between daytime and nighttime during fall.
For the rest of the sources, the daytime and nighttime emissions were
relatively constant. No similar studies have been conducted in the MMA;
this is the first source apportionment study based on molecular organic
markers for this region. However, these results are similar to those
obtained for the MMA using a factor analysis based on trace elements
(Martinez et al., 2012) and those for Mexico City based on molecular organic
markers (Stone et al., 2008).
CMB contributions to the (a) average identified ambient PM2.5
in the MMA and to the (b) overall PM2.5 including the unidentified mass of
the measured PM2.5 concentrations.
The average contribution of each emission category to the identified
PM2.5 mass is shown in Fig. 6. The unidentified mass was on average
35 ± 24 % of the measured PM2.5 concentrations. This value is
1.5 times greater than the ∼ 23 % of secondary organic aerosol
contribution to the total PM2.5 mass concentration (SOC / PM2.5)
estimated in Mancilla et al. (2015) for the MMA. The average secondary
contribution used for this comparison was based on the minimum OC / EC ratios
observed and reported in Mancilla et al. (2015). These ratios may take into
account primary sources with elevated values of OC / EC ratios such as biomass
burning and kitchen operations as well as fossil fuel combustion sources.
The mass not identified by CMB may include secondary organic and inorganic
aerosol and trace elements. Thus, the levels of unidentified mass resolved
by CMB are reasonable given that the secondary aerosol estimated in Mancilla
et al. (2015) was in fact only SOA. Therefore, the 12 % of difference
between 35 and 23 % might be attributed to secondary inorganic aerosol
and other chemical species. As indicated in Fig. 6, the emissions from motor
vehicle exhausts (gasoline and diesel) are the most important, accounting
for the 64 % of the identified PM2.5 emissions, followed by
meat-cooking operations (31 %) and industries (2.8 %). Vegetative
detritus and biomass burning were the least emitted, with only 2.2 % of the
identified PM2.5 emissions. The relatively high contribution of the
meat-cooking operations was expected given the high traditional restaurant
activity in the MMA, which contributes 16 % of the local gross
domestic product. With regard to biomass burning, several studies have
demonstrated that Mexico City has a large contribution of biomass burning
emissions due to forest fires (Moffet et al., 2008; Stone et al., 2008;
Yokelson et al., 2007). However, the MMA can be affected by other types of
biomass burning (e.g., shrub and grassland fires, agricultural waste and
garbage burning) that may be ignored. Therefore, the contribution of
biomass burning in the MMA might be higher because the source profile used
for the CMB was only for wood combustion instead of using a source profile
for other types of biomass burning (Simoneit et al., 2005). In addition, it
is important to point out the potential of industrial sources that appeared
in previous studies conducted in the MMA and the rest of the country. The
MMA is the third largest urban center of the country, with approximately
9700 industries (SIEM, 2016). In the MMA the main emissions from
industrial sources come from the combustion of natural gas; low emissions
come from the use of fuel oil. In this study the natural gas profile did not
fit well and was discarded from the CMB, but the fuel oil profile did. The
combustion of natural gas emits a low quantity of particles; therefore, its
contribution to the airborne particles is not significant.
Conclusions
Spring and fall sampling campaigns were performed in 2011 and 2012 at one
representative site to conduct a chemical characterization of the fine OC in
PM2.5 in the MMA. The identified organic compound classes represented a
low fraction of the ambient OC: 0.5 % for spring 2011 and 2.6 to 11 %
for the last three campaigns. The average CPI values derived from the
n-alkanes (0.9–1.7) and n-alkanoic acids (2.9–6.8) demonstrated that
anthropogenic emission sources (e.g., fossil fuel combustion) were dominant, while biogenic
(e.g., plant waxes, microbial origin) emission sources contribute at least
sometimes to the fine OA in the MMA.
The PAH diagnostic ratios indicate that gasoline- and diesel-powered
vehicles are the main emission sources of this class of organic compounds in
PM2.5. However, other pyrogenic sources such as coal, grass, and wood
combustion were also identified as contributors to the fine OA. The
quantified levels of cholesterol and levoglucosan confirm the high and low
contribution of cooking operations and biomass burning, respectively. Low
levoglucosan concentrations suggest low episodic or transport effects of
emissions of biomass burning on PM2.5 in the MMA.
In a parallel study, significant SOA formation was found in the MMA. The
chemical speciation of the OC confirmed the aging of primary emissions and
the SOA from biogenic volatile organic compounds. On the one hand, the
identified octadecanoic acid and cis-9-octadecenoic acid along with other
secondary organic markers point out the SOA formation in the MMA atmosphere.
The average ratios of octadecanoic acid to cis-9-octadecenoic acid (3.8–21)
indicate aging of the fine OA due to photochemical activity and transport.
On the other hand, the presence of the cis-pinonic and pinic acids confirmed
the SOA derived from biogenic sources. This is in line with the transport
and stagnation events that predominated during spring and fall,
respectively.
The emissions from vehicle exhausts are the most important, accounting for
64 % of the identified PM2.5 emissions. By contrast, vegetative
detritus and biomass burning were the lowest contributors, with barely
2.2 % of the identified PM2.5 emissions.
Finally, a comparison with other studies indicates that the MMA exhibits
similar concentrations patterns of the organic molecular markers identified
in this study.