Chemical characterization of fine organic aerosol for source apportionment at Monterrey , Mexico

Introduction Conclusions References

n-alkanes indicate a major contribution of anthropogenic and mixed sources during the fall and the spring, respectively.Hopanes levels confirmed the contribution from gasoline and diesel engines.In addition, the contribution of gasoline and diesel vehicle exhaust was confirmed and identified by the PAH concentrations in PM 2.5 .Diagnostic ratios of PAH showed emissions from burning coal, wood, biomass, and other fossil fuels.The total PAH and elemental carbon (EC) were correlated (r 2 = 0.39-0.70)across the monitoring periods, reinforcing that motor vehicles are the major contributors of PAH.Cholesterol levels remained constant during the spring and fall, showing evidence of the contribution of meat cooking operations, while the isolated concentrations of levoglucosan suggested occasional biomass burning events.Finally, source attribution results obtained using the CMB model indicate that emissions from motor vehicle exhausts are the most important, accounting for the 64 % of the PM 2.5 .The vegetative detritus and biomass burning had the smallest contribution (2.2 % of the PM 2.5 ).To our

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 PM 2.5 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 PM 2.5 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 triterpanes), and polycyclic aromatic hydrocarbons (PAHs).Levoglucosan is a pyrolisis decomposition and combustion product of cellulose; therefore, it can be used as a tracer for biomass burning sources (Fraser et al., 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 fossil fuel incomplete 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).Addi-Figures
Another feature used to identify the origin of fine OA are the diagnostic ratios between some organic compounds.Such is the case of n-alkanes, carboxylic acids, and PAHs.For the n-alkanes and carboxylic acids, the odd-to even-carbon ratio is an indicator used to determine whether fine OA come from biogenic or anthropogenic emission sources (Tsapakis et al., 2002).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).In spite of recent research interest on organic molecular markers for source apportionment, the application of this approach started in the 80s (Simoneit, 1985(Simoneit, , 1986;;Eatough et al., 1989;Simoneit and Mazurek, 1989) and continued in the 90s with sparse efforts (Simoneit et al., 1990(Simoneit et al., , 1991;;Schauer et al., 1996;Simoneit, 1999;Schauer and Cass, 2000).
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 PM 2.5 mass in this urban Introduction

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Full center (Mancilla et al., 2015).Previously, Martínez et al. (2012) estimated an OA fraction of ∼ 40 % of the PM 2.5 for the MMA.According to a recent tunnel study, PM 2.5 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 the 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 its typical industries of the region make of the MMA unique and different from other Mexican cities.

Sampling site
The MMA is composed of 12 municipalities that overall cover an area of 6680 km 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 PM 2.5 concentrations recorded in 2009 by the SIMA network.Details about this analysis can be found elsewhere (Mancilla et al., 2015).

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 twelve-hour samples were taken to obtain information for daytime and nighttime periods.The daytime sampling was performed from 06:00 a.m. to 06:00 p.m. LT, while nighttime samples were collected from 06:00 p.m. to 06:00 a.m. 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 PM 2.5 inlet (TE-6001-2.5, Tisch Environmental Inc), operating at a flow of 1.13 m 3 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 three meters 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 in 8 ×10 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 Introduction

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Full used.After sampling, loaded 8 × 10 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
According to Brown et al. (2002), at least 400 µg of OC is necessary for a detailed organic speciation by GC/MS.For the first campaign (Spring 2011), analyses for each 12 h sample were carry out as discussed by Mancilla et al. (2015).OC in the samples of this first campaign varied from 3897 to 12 310 µg per filter.Even though the samples collected for the spring of 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 (the fall of 2011, the spring of 2012, and the fall of 2012) were on average up to 35 % lower than OC concentrations of the first campaign.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 to pool sample filters into weekday and weekend composites for the last three campaigns.Sample groups for composites varied from two to six sample filters with a total of OC varying from 5019 to 62 536 µg per composite.
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.The solvents used were optima grade (high purity 99.9 %, Fisher Scientific), and included isopropanol (IPA), dichloromethane (DCM), and methanol (MeOH).The chemical reagents and authentic deuterated standards (Sigma Aldrich) were used.In addition, the glassware and laboratory tools were cleaned using detergent and water, rinsed with deionized water and isopropanol, and baked overnight at 450 • C. MeOH was used in the preparation of some stock solutions.Organic molecular markers were Introduction

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Full determined by (GC/MS).The preparation of each individual filter or composite described in Table 2 was conducted over a clean surface covered properly with aluminum foil.Stored sample jars containing the loaded filters were thawed until they equilibrated with the room temperature.Then, the filters were removed from the sample jars using clean tweezers.Next, filters were cut into pieces and placed into prebaked glass jars.Finally, filters were spiked with 50 µL of the following deuterated internal standards: n-hexadecane-d34, n-hexatriacontane-d74, n-eicosane-d42, ntriacontane-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 (the spring of 2011 campaign) or sample composite (rest of the campaigns) was extracted three times with DCM.During each extraction, enough DCM was added to cover the filters, and then the jars were subjected to ultrasonic agitation for 20 min using a sonicator (Bransonic ® , model 5510R-DTH).The extracts were combined into a new pre-baked jar and then concentrated by evaporation under a gentle flow 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.All fractions were put into vials and kept in a freezer until they were analyzed.Two fractions of each concentrated extract (250 µL extract) were used for chemical derivatizations.One fraction was methylated using diazomethane (CH 2 N 2 ) 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 Diazomethane (CH 2 N 2 ) solution was combined in a vial with 50 µL of extract.For the silylation, 50 µL of BSTFA+TMCS (molar ratio 99 : 1) Introduction

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Full was combined with 50 µL sample extract.Then, the mixture was allowed to react for 3 h at 65 • C. Filter extracts were analyzed on an Agilent 6890N Gas Chromatograph (Agilent Technologies) coupled with an Agilent 5973N Mass Selective Detector (MSD) (Agilent Technologies).Separation was carried out using a non-polar 30 m × 250 µm × 0.25 µm HP-5MS Agilent ultra-inert capillary column coated with 5 % phenyl methyl siloxane.Injections of 2 µL aliquots were performed using an Agilent 7683 Autosampler (Agilent Technologies) in splitless mode and using helium (ultra-high purity, 99.999 %) as the carrier gas.The MSD was operated in ion scan mode and ions were produced using an electron impact (EI) ion source.The GC/MS operating conditions included an initial hold of 5 min at 65 • C, followed by a temperature ramp rate of 10 • C min −1 that was held constant for 25 min until the GC oven reached 300 • C. The quantification and identification of organic compounds was based on comparisons with authentic standards, retention times, literature mass spectra, and fragmentation patterns.A detailed description of the extraction and analysis procedures can be found elsewhere (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 follows: and for n-alkanoic acids (even to odd ratio), as: 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 one) indicate that they are emitted from anthropogenic sources (Gogou et al., 1996;Alves et al., 2001;Gelencsér et al., 2004).
Another useful indicator that is used to specify the origin of the emissions is the carbon number with maximum concentration (C max ).Hydrocarbons and carboxylic acids of high molecular weight (> C 25 ) are emitted from biogenic sources, while those with lower molecular weight (≤ C 25 ) are mainly emitted from fossil fuel combustion processes (Alves et al., 2001;Young and Wang, 2002;Gelencsér et al., 2004).

Diagnostic ratios of PAHs
Other indicators that have been used as markers of different source emissions of OA are the PAHs diagnostic ratios (DRs) (Dvorská et al., 2011;Katsoyiannis et al., 2011).The DRs calculated in this study are shown in Table 3.The values li sted in this table can be found elsewhere (Ravindra et al., 2008;Tobiszewski and Namie ṥnik, 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: (3) Introduction

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Full where x i j is the measured concentration of species j in sample i , f kj is the concentration of species j in the emissions of source k, g i k is the contribution of source k to sample i , and e i j 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 over determined set of mass balance equations (Eq.3).CMB takes into account the known uncertainties in the ambient measurements and the source emission data to minimize the chi-square (χ 2 ) goodness-of-fit parameter for each sample i : where σ xj is the standard deviation of the concentration of species j , σ gj k is the standard deviation of the g j k , and m the total number of species.The U.S. EPA-CMB8.2software has been successfully used to apportion source contributions to ambient PM 2.5 (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).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 Secondly, Martinez et al. (2012) based 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 profile 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 diesel vehicle fleet is composed by heavy duty vehicle 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, a source profile for natural gas combustion and fuel oil combustion were taken from Rogge et al. (1993cRogge et al. ( , 1997)), respectively.Finally, source profiles for meat cooking operations, vegetative detritus, and biomass burning were taken from Schauer et al. (1999Schauer et al. ( , 2001a) ) and Rogge et al. (1993b), 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.A detailed description of 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-Introduction

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Full 3 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.Most of the PM 2.5 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 PM 2.5 for spring and fall, respectively.In the spring, the daytime carbonaceous fraction was 1.6-1.8times higher than the corresponding nighttime fraction, whereas during the fall, it was 1.1-1.2times higher.The average OC / EC ratios ranged from 7.4 to 12.6 during this study.A detailed information and analysis of the carbonaceous aerosol for this study can be found in Mancilla et al. (2015).
All the samples collected during this study were analyzed for eight organic compound classes except those samples collected in the spring of 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 the last three campaigns.For the eight resolved compound classes in the last three campaigns (Fig. 2), the n-alkanoic acids were the most abundant, followed by n-alkanes, wood smoke markers, and levoglucosan/alkenoic acids.The PAHs and triterpanes hydrocarbons were less abundant.The same tendency was observed in the spring of 2011, Introduction

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Full 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 the spring of 2011 accounted for 0.49 ± 0.52 % and 0.46 ± 0.41 % of the ambient OC, respectively.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).

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 (> C 20 ) as n-alkanes (Simoneit and Mazurek, 1982).In this study, the n-alkanes in the range of C 17 -C 33 were detected.For the samples obtained for the first year campaigns, 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 PM 2.5 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 the electric energy consumption with regard to the temperature; during the cold seasons the government removes the subsidy to the domestic electric energy due to the demand on 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 contri-Introduction

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Full bution of anthropogenic and biogenic emissions 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 ≤ C 25 (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 C max was found at C 27 , C 29 , or C 31 (Fig. 3).These carbons' 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).
For the second sampling year, the behavior of n-alkanes was the opposite of the first year.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 the previous year, but the OC levels were higher during the fall 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 the previous year.In the fall of 2012, the warmer temperatures might have promoted less utilization of some fuels compared to the previous year in which lower temperatures could have promoted their utilization, increasing the n-alkanes' concentrations in that fall.The average CPI values of n-alkanes in the spring 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 the fall 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 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 hopanes levels were ∼ 35 % lower at daytime Introduction

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Full 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 OC samples.For both sampling years, the average daytime and nighttime concentrations of PAHs were 1.4-5.9 and 1.4-2.4times higher in the fall than in the spring, respectively.This is consistent with the high contribution of the OC to the PM 2.5 during the falls.Independently 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.As illustrated by 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 Namie ṥnik, 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 due to 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, it can be determined the source origin (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 17982 Introduction

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Full 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 source for the fall of 2011 and the spring of 2012, and mixedsources for the fall of 2012.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).In this study, the average total concentrations of the quantified PAHs (TPAHs) were 2.42±2.45ng m −3 (range: 0.65-8.31ng m −3 ) and 4.11±2.62ng m −3 (range: 1.42-11.97ng m −3 ) during 2011 and 2012, respectively, whilst those quantified by González-Santiago ( 2009) at two different sites in the MMA were 1.30±1.64ng m −3 (range: 0.05-6.93ng m −3 ) and 1.70 ± 1.88 ng m −3 (range: 0.07-9.14ng 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 six PAHs, while in this study were identified twelve.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 Introduction

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Full 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 2 ) of 0.56-0.60(p < 0.1) 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 total PAH (TPAH) concentrations were compared with the EC and OC levels.TPAH concentrations measured in the MMA exhibited fair daytime correlations with EC (r 2 = 0.60-0.67;p < 0.1), but low correlations with OC (r 2 = 0.11-0.66;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.With regard to OC, the weak correlation was an indicator that other non-combustion sources exist.

Carboxylic 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 C 10 to C 32 were identified 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 two 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).Introduction

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Full 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 second sampling 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 < C 20 are derived in part from microbial sources while those > C 20 are from vascular plant waxes (Guo et al., 2003;Yue and Fraser, 2004;Simoneit et al., 2004).Figure 5 clearly shows influence of long chain (> C 20 ) 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 n-alkenoic acids only included the oleic and elaidic acids.The concentrations of oleic acid ranged from 0.96 to 15.38 ng m −3 , while the concentrations of elaidic acid ranged from 2.11 to 13.35 ng m −3 .The ratio of octadecanoic acid (C18:0) to oleic acid (C18:1) has been used as an indicator of the atmospheric chemical processing (aging) of aerosols, since the unsaturated acids are susceptible to atmospheric oxidation from gaseous precursors (Brown et al., 2002;Yue and Fraser, 2004).In this study, the average ratios of C18:0 / C18:1 were 5.0 (range: 1.5-9.4)during the day and 3.8 (range: 2.5-4.9) at night for the spring, while 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 oleic acid producing high C18:0 / C18:1 ratios.By contrast, the stagnation of aerosols may reflect the impact of high oxidant concentrations in an urban atmosphere producing low C18:0 / C18:1 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.As expected, the lowest and highest ratios of C18:0 / C18:1 obtained for the MMA Introduction

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Full 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 C18:0 / C18:1 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 product of pinene, which is emitted from plants, particularly conifers (Plewka et al., 2006;Sheesley et al., 2004).The pinonic acid and pinic acid exhibited higher concentrations in the spring than in the fall (Table S1), indicating biogenic emissions from softwood sources.

Meat cooking and biomass burning tracers
The major tracers for meat cooking particles are the steroids, while for biomass burning are the anhydrosaccharides and methoxyphenols.All of these organic tracers were intermittent in in only 80 % of the total OA samples/composites collected during this study.
There was not a 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 (Robinson et al., 2006a).
Anhydrosaccharides are the tracers from burning cellulose and hemicelluloses, whereas the methoxyphenols the 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 OC samples indicates that biomass burning is impacting the MMA to some extent (Table S1).The levoglu-Introduction

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Full cosan concentrations varied by sampling dates, ranging from not 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 fall seasons, the levoglucosan levels can be associated with local biomass burning due to mild temperatures exhibited 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 ratios 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 the Mexico City; the EC concentrations were up to two times higher than those calculated 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 Introduction

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Full 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.
The resin acids such as dehydroabietic acid, pimaric acid, and isopimaric acid are secondary tracers from biomass burning (Schauer et al., 2001a).Dehydroabietic acid was the most abundant resin acid, while pimaric acid and isopimaric acid were less abundant (Table S1), indicating softwood burning (e.g., conifer wood) during the spring and fall campaigns in the MMA.

Source apportionment
CMB was applied using the quantification of individual organic compounds found in the collected PM 2.5 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 r 2 values ranging between 0.58 and 0.85 and chi-squared (χ 2 ) values between 2.97 and 8.85.Similar values for r 2 and χ 2 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 is 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 17988 Introduction

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Full samples fed to CMB in this study, 18 samples exhibited low percent mass explained values (ranging from 20 to 77 %), whilst 14 samples had values around 100 %.These results are in line with the relative high and low OC / EC ratios obtained for spring and fall, respectively Mancilla et al. (2015).The rest of the 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.
The average contribution of each emission category to the identified PM 2.5 mass is shown in Fig. 6.The unidentified mass was on average 35 ± 24 % of the measured PM 2.5 concentrations.This value is 1.5 times greater than the ∼ 23 % of secondary organic aerosol contribution to the total PM 2.5 mass concentration (SOC/PM 2.5 ) 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 unidentified mass 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 the 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 PM 2.5 emissions.The vegetative detritus and biomass burning were the less emitted with only 2.2 % of the identified PM 2.5 emissions.Here is important to point out the potential of industrial sources due to prior 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 (INEGI, 2005).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 it was dis-Introduction

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Full carded from the CMB, but the fuel oil did it.The combustion of natural gas emits low amount 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 PM 2.5 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 sources (e.g., fossil fuel combustion) were dominant while biogenic (e.g.plant waxes, microbial origin) emission sources contribute at least some times 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 PM 2.5 .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 low, but noticeable contribution of cooking operations and biomass burning.Low levoglucosan concentrations suggest low episodic or transport effects of emissions of biomass burning on PM 2.5 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 one hand, the identified octadecanoic acid (C18:0) and oleic acid (C18:1) along with other secondary organic markers point out the SOA formation in the MMA atmosphere.The average ratios of C18:0 / C18:1 (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.Introduction

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Full The emissions from vehicle exhausts are the most important, accounting for the 64 % of the identified PM 2.5 emissions.By contrast, the vegetative detritus and biomass burning were the less contributors with barely 2.2 % of the identified PM 2.5 emissions.
Finally, the outcomes of a comparison with other studies were that the MMA exhibits similar concentrations patterns of the organic molecular markers identified in this study (available in the Supplement).
The Supplement related to this article is available online at doi:10.5194/acpd-15-17967-2015-supplement.Introduction

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Full  Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

2(
SEDESOL et al., 2007), as shown in Fig. 1.The MMA has a vehicular fleet of 1.7 million vehicles (INEGI, 2010) and 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.In addition, the MMA has a network of air quality monitoring stations (Sistema Integral de Monitoreo Ambiental, SIMA).For this study, PM 2.5 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.The sampling site is affected mainly Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 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 gasolineand diesel-powered vehicles, vegetative detritus and biomass burning in Mexico City.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | homohopane, and 22R+S 17a(H),21b(H)-30-bishomohopane), five polycyclic aromatic hydrocarbons (benzo[a]anthracene, benzofluoranthenes, benzo[a]pyrene, indeno[123cd]pyrene and benzo[ghi]perylene), two saturated fatty acids (C16:0 and C18:0), cholesterol and levoglucosan.

Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .
Figure 1.Location and municipalities of the Monterrey Metropolitan Area (MMA); the sampling site was set up in the downtown.

Table 1 .
Monitoring experiments conducted for this study.

Table 2 .
Characteristics of individual and composite samples for each monitoring campaign.