5. Opposing Seasonal Trends for Polycyclic Aromatic Hydrocarbons and PM10_Health Risk and Sources in Southwest Mexico City

8/10/2019 5. Opposing Seasonal Trends for Polycyclic Aromatic Hydrocarbons and PM10_Health Risk and Sources in Southwest Mexico City

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Opposing seasonal trends for polycyclic aromatic hydrocarbons and PM10:Health risk and sources in southwest Mexico City

Omar Amador-Muoz a, S. Bazn-Torija a, S.A. Villa-Ferreiraa, Rafael Villalobos-Pietrinia,,Jos Luis Bravo-Cabrera a, Zenaida Munive-Coln a, Leonel Hernndez-Mena b,H. Saldarriaga-Norea c, M.A. Murillo-Tovar b

a Centro de Ciencias de la Atmsfera, Universidad Nacional Autnoma de Mxico, 04510 Distrito Federal, Mexicob Centro de Investigacin y Asistencia en Tecnologa y Diseo del Estado de Jalisco, Av. Normalistas 800, Guadalajara, Jalisco 44270, Mexicoc

Facultad de Ciencias Qumicas, Universidad Autnoma de Coahuila, Blvd. Venustiano Carranza y Jos Crdenas Valds S/N Col. Repblica Ote, C.P. 25280, Saltillo, Coahuila

a r t i c l e i n f o a b s t r a c t

Article history:

Received 20 February 2012Received in revised form 4 September 2012Accepted 2 October 2012

This study reports the measurement of polycyclic aromatic hydrocarbons (PAHs) in airborneparticles 10 m (PM10) during four years. Seasonal variation was observed for PM10 and PAH insouthwest Mexico City, with major mass concentrations during the dry season (NovemberApril). A non linear decreasing trend of PM10was observed during this period, while a linear

increase (in the four years) was obtained for benzo[a]pyrene (88 pg m3), phenanthrene(29 pg m3), fluoranthene (88 pg m3), and benzo[ghi]perylene (438 pg m3). Coronene alsoshowed an increasing trend but it was nonlinear. This suggests that air control strategiesimplemented by the government contributed to maintaining PM10 under the 24 h maximumlimit and resulted in a decreasing trend during this period. However, these strategies did not

result in controlling some organic constituents with mutagenic and/or carcinogenic properties asit is the case of benzo[a]pyrene. The annual average of this PAH exceeded the UK rec-ommendation. It was estimated a median (10th90th) lifetime health risk of 7.6 (3.417.2)additional cases of cancer per 10 million people in this zone exists and the health risk of PAH is

almost three times greater in dry seasons than it is in rainy seasons. Specific humidity,temperature and wind speed acted as cleaners for PM10 and PAH from the atmosphere. PAHdiagnostic ratios and correlation and principal component analyses suggest incomplete

combustion from gasoline and diesel engines as the main contributor to PAH found in southwestMexico City, where factor 1 grouped all PAH emitted from gasoline engines during first threeyears. During last year, factor 1 only grouped PAH markers of diesel engines. This suggests achange of emissionamounts betweengasoline and diesel combustion sources or a contribution of

other source(s) which changed the PAH profiles. During four years retene was always separatedfrom factors which grouped the rest of PAH, due to its wood combustion origin.

2012 Elsevier B.V. All rights reserved.

Keywords:

PM10Polycyclic aromatic hydrocarbons

Seasonal variation

Health riskSources

1. Introduction

The association between airborne particles and adverseeffects on human health has been extensively documented(WHO, 2006a; Pope et al., 2009). The magnitude of these effects

generally depends upon the size, the chemical composition, theamount of inhaled particles and exposure time. Therefore,abundance, temporal behavior, sources and chemical contentof particles are of concern to assess their human health riskresulting from atmospheric exposure. Studies of time series dataof hazardous chemicals associated with particles have beenexamined to observe the short-term acute effect of air pollutionon the health of individuals, especially on respiratory andcardiovascular systems (Samet et al., 2000; Samoli et al., 2008)

Atmospheric Research 122 (2013) 199212

Corresponding author. Tel.: + 52 55 56224077; fax: + 52 55 56160789.E-mail address:[email protected](R. Villalobos-Pietrini).

0169-8095/$

see front matter 2012 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.atmosres.2012.10.003

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Atmospheric Research

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http://dx.doi.org/10.1016/j.atmosres.2012.10.003http://dx.doi.org/10.1016/j.atmosres.2012.10.003http://dx.doi.org/10.1016/j.atmosres.2012.10.003mailto:[email protected]://dx.doi.org/10.1016/j.atmosres.2012.10.003http://www.sciencedirect.com/science/journal/01698095http://www.sciencedirect.com/science/journal/01698095http://dx.doi.org/10.1016/j.atmosres.2012.10.003mailto:[email protected]://dx.doi.org/10.1016/j.atmosres.2012.10.003


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and the long-term cumulative exposure to air pollution toobserve association with chronic (cause-specific) morbidity andmortality (Ballester et al., 2008). One of the main classes oforganic compounds extensively studied due to carcinogenic(IARC, 2010) and mutagenic (Kawanaka et al., 2004; Villalobos-Pietrini et al., 2007) properties has been the polycyclic aromatichydrocarbons (PAHs). PAHs generally occur as complex

mixtures emitted as a result of incomplete combustion pro-cesses. PAHs associated with PM10 have been examinedpreviously in Mexico City (Villalobos-Pietrini et al., 2006;Saldarriaga et al., 2008; Guzmn-Torres et al., 2009; Amador-Muoz et al., 2010; Mugica et al., 2010a, 2010b),butno one hasreported their temporal behavior in a long-term period, whichis an important element for the development of criteria forsetting air quality standards and guidelines for compositionspecific particle exposure, unlike those based solely on theparticle mass. The air quality standards for individual com-pounds are generally based upon an examination of the likelyeffects upon health (Maynard et al., 1997). Therefore, it isimportant to develop detailed and systematic PAH profiles to

control and limit concentrations by assessing public exposureto PAH and theassociated health risk (Fang et al., 2005). Aspartof risk assessment and managementguidelines associated withPAH, results from a detailed, four consecutive year study of thecomposition, abundance and sources of PAH associated withPM10 in southwest Mexico City are presented in this study,which has four objectives: (1) evaluation of the seasonalbehavior and trends of PM10and PAH mass concentrations;(2) determination of PAH health risk assessment; (3) determi-nation of the effects of different meteorological conditions onthe behavior of PM10 and PAH and the relationship withatmospheric criteria pollutants and; (4) identification of theirsources. The resultsare a continuation of a previous manuscript

published by Amador-Muoz et al. (2010). These data willcontinue to improve our understanding of the risk of PAH andparticulate matter in Mexico City, to provide a background foradatabase that will contribute to the support of a more realisticcriterion for limiting PM10as well the basis for regulation ofPAH in Mexico City, as it has been recently highlighted by theMexican government (SMA, 2011).

2. Materials and methods

2.1. Airborne particle sampling

The sampling zone was located in southwest Mexico City

7 m high on the roof of Centro de Ciencias de la Atmsfera ofUniversidad Nacional Autnoma de Mxico, surrounded bybuildings, green areas and residential and commercialdistricts with several traffic avenues (Fig. 1). Most of MexicoCity is from Northwestern to Southeastern; toward theSouth-Southwest is surrounded by commercial and residentialzones, and toward the West, by mountains. The main industrialzone is located to the North part of the Mexico Basin and it isnot near to the sampling site (~30 km). The sampling periodwas from January 1999 to December 2002. Each sample was a24 h exposure taken every 6th day, and in some seasons each3rd day, for a total of 310 samples. The airborne particles werecollected with a PM10high volume sampler (Andersen-GMW)

with a flow rate of 1.13 m

3

min

1

10% on glass fiber filters(20 cm25 cm, Gelman Sciences) baked to 180 C for at least

24 h, to remove adsorbed organics, after which they weretransferred to a chamber with relative humidity of b40% at2023 C for another 24 h for conditioning. Afterward, thefilters with particles were equilibrated in the chamber for anadditional 24 h. The concentration of particles (g m3) wasdetermined by differences in the filter weights, using a SartoriousGMBH balance, with a sensitivity of 0.1 mg, before and after 24 h

exposures, divided by the filtered air volume at standardconditions (25 C and 1 atm). Pre-sampling and post-samplingfilters were carefully checked to avoid errors in the particle massdetermination.

2.2. Solvent extracted organic matter

PAH on airborne particles have been determined by solvent-less techniques. In this study, we employed a solvent to extractthe organic matter. The solvent extracted organic matter(SEOM) in PM10 was prepared and analyzed as describedelsewhere (Villalobos-Pietrini et al., 2006) using an ultrasonicbath (Branson 3210) and methylene chloride (HPLC grade,

Chromanorm) for solvent extraction; thrice for 30 min withtemperature between 6 and 10 C. The organic extracts werefiltered, concentrated in a rotatory evaporator (Buchi) andstored in vials at 04 C until chromatographic analysis. SEOMair concentrations in g m3 were calculated at standardconditions (25 C and 1 atm).

2.3. Chemical analysis

The following PAHs were analyzed (selected ion masses inparentheses): phenanthrene (178, 179, 89 u), anthracene(178, 179, 89 u), fluoranthene (202, 203, 101 u), pyrene (202,203, 101 u), retene (219, 234 u), benzo[a]anthracene (228,

229, 114 u), chrysene (228, 229, 114 u), benzo[b

]fluoranthene(252, 253, 126 u), benzo[k]fluoranthene (252, 253, 126 u),benzo[e]pyrene (252, 253, 126 u), benzo[a]pyrene (252, 253,126 u), perylene (252, 253, 126 u), indeno[1,2,3-cd]pyrene(276, 277, 138 u), dibenzo[a,h]anthracene (278, 279, 139 u),benzo[ghi]perylene (276, 277, 138 u) and coronene (300, 301,150 u) (Chemservices, West Chester, PA, USA). A gas chro-matograph/mass spectrometer (6890/5973 N) (Agilent Tech-nologies, USA) with a quadrupole mass filter and anautosampler (model 7683) was used for the analyses. Onemicroliter of each extract was injected in splitless mode (45 s)at 300 C. A 30 m HP5-MS capillary column (0.25 mm i.d.,0.25 m film thickness) was used to separate the PAH in the

SEOM. The oven temperature program was operated at 80 Cfor 2 min and increased 5 C min1 to 300 C for 10 min. Highpurity helium was used as the carrier gas at a flow rate of1 mL min1. The mass spectrometer was operated inelectron impact mode (70 eV) and selected ion monitoring.Temperatures were: transfer line, 280 C; ion source,230 C; and quadrupole, 150 C. Three deuterated PAHs in2.5 ng L1 were used as internal standards (PAH-d;selected ion masses): [2H10]phenanthrene-d10 (Phen-d10;188, 189, 94 u), [2H12]chrysene-d12 (Chrys-d12; 240, 241,120 u) and [2H12]perylene-d12 (Per-d12; 264, 265, 132 u)(Chiron, Trondheim, Norway). Eight point calibrationcurves from randomly selected results (in triplicate at

each point) for all PAHs were prepared by applyingweighted least squares regression, ranging from 60 to

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4500 pg L1 (r>0.99, pb0.001). PAH air mass concen-trations in pg m3 were calculated at standard conditions(25 C and 1 atm).

2.4. Meteorological data and atmospheric pollutants

The values for temperature (C), relative humidity (%),wind speed (m s1) and wind direction (), plus data for thecriteria atmospheric pollutants O3, NO2, NOx, SO2 and CO, wereall obtained from Pedregal (http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2) which is the nearest monitoring station to our samplingsite supported by the monitoring network of the Mxico Citygovernment. The equipment and methodology is consult-ed in http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4. Specific humidity (SH) (g kg1), consid-ered as the mass (in grams) of water vapor per unit mass(in kilograms) of dry air, was calculated from the partial

water vapor pressure at saturation point and relativehumidity.

2.5. Quality control

The sampler was calibrated in accordance with the rulesestablished by theFederal Register (40 CFR Part 50, 1987)every three months. Standard reference material SRM1649a from the National Institute of Standards and Tech-

nology was used to evaluate the SEOM and PAH recoveryefficiency. The amount of urban dust used to evaluaterecoveries ranged between 50 and 150 mg, because themass of PM10 in southwest Mexico City was found in thisrange. Efficiency for PAH was found to be between 47.41.4% (anthracene) and 89.3 4.9% (benzo[ghi]perylene),with a relative uncertainty associated with the air concen-tration between 8.1% (phenanthrene) and 16.2% (perylene),while SEOM recovery was in the range of 9012.2%(Villalobos-Pietrini et al., 2006). The uncertainty was calcu-lated following theEurachem/CITAC Guide (2000). PAH infilter blanks and PAH values recoveries were used to correctand adjust, respectively, the PAH concentrations in the air.

Quantification limits were found between 7 (benzo[a]pyrene)and 130 pg m3 (perylene).

Fig. 1.Mapof Mexico.In a zoom image: PM10 sampling site in southwest Mexico City black dot.Urbanpopulation density dark grayarea, rural population density graydots and gray lines indicate the boundaries of Mexico City districts. Urban and rural population density is defined in http://www.inegi.org.mx/geo/contenidos/geoestadistica/catorcen.aspx.

201O. Amador-Muoz et al. / Atmospheric Research 122 (2013) 199212
http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4http://www.inegi.org.mx/geo/contenidos/geoestadistica/catorcen.aspxhttp://www.inegi.org.mx/geo/contenidos/geoestadistica/catorcen.aspxhttp://www.inegi.org.mx/geo/contenidos/geoestadistica/catorcen.aspxhttp://www.inegi.org.mx/geo/contenidos/geoestadistica/catorcen.aspxhttp://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=2&opcioninfoproductos=2


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2.6. Statistical analysis

In order to know the actual dependence between targetvariables, the seasonal cycle of the variables throughout theyear was eliminated. This was done by considering thedifference between the daily mass concentrations of the targetcomponent, and the monthly mean concentration of that

component (Amador-Muoz et al., 2011). Associations amongPM10, PAH and criteria atmospheric pollutants (CAPs) weredone using the daily mass concentration of the non-seasonaltime series variables and no trend. For CAPs, the calculationwas done based on their maximum daily concentration.

The trend of PM10 and PAH from 1999 to 2002 was evaluatedemploying the daily residual calculated for the differencesbetween PM10and PAH observations and the monthly meansfor PM10 and PAH concentrations, respectively, for the four years(Bravo et al., 2006).

The Statistica software v. 8.0 (Statsoft, USA) was employedfor statistical analysis. The MannWhitney U test was used tocompare medians among seasons and years. Spearman rank

order correlation was employed to observe the associationsamong PM10, PAHs, criteria atmospheric pollutants and meteo-rological parameters. Linear regressionswereevaluated withtheANOVA test and lack of fit analyses. Factor analysis employedprincipal component analysis (PCA) as extraction method toreduce the dimensionality of the data.

3. Results and discussion

3.1. Meteorological conditions

Our study was organized by dry and rainy seasons accordingto seasonal variability of relative humidity, and coincides withthe historical seasonal behavior observed byJuregui (2000) forthe Mexico City area. In addition, dry seasonswere sub-classifiedinto cold and warm periods according to the variation oftemperature. Table 1 shows the seasonal medians of specifichumidity, temperature and wind speed by site.

Lower values of specific humidity were observed in all dryseasons than in the rainy seasons (p0.01). Temperature

differences between the dry warm and the rainy seasonswere not meaningful (except between DW3 and R3), becausethe warmer months are included in both seasons. The dry coldseasons contained the coldest months during the study period,except for DC3 where higher temperatures were registered. Thewind speeds showed differences among seasons in the periodMay 2000October 2002 (except between DW3 and R3), with

higher rates in the rainy seasons, while differences were notobserved in the rest of the seasons. During the rainy seasons theprevailing wind direction was from northeast to southwest, butfor both dry seasons the direction was dependent on the period,but in general, the wind blew from north to south (Fig. 2).

3.2. Annual and seasonal behavior of PM10and PAH

During the entire period, the maximum permissible limitestablished, 120 g m3 in 24 h (NOM-025-SSA1-1993), wasnever exceeded. However, the annual arithmetic mean, limitedto 50 g m3, was exceeded during 1999, 2000 and 2002(Table 2). The average annual PM

10mass concentrations were

found to be similar to theaverage PM10 levelsof several cities inthe United States and Europethat range from 20 to 70g m3,but much less than Asian cities with concentrations of35220g m3 (WHO, 2006b).

The medians of PM10 (Fig. 3) were statistically differentbetween seasons (pb0.002), except for the following adjacentseasons: R1DC1, DC2DW3, DC3DW4 and R4DC4, andranged from 1.3 to 1.9 times greater concentrations in the dryseasons than in the rainy seasons. Table 3shows the annualmedian concentrations of individual PAHsgrouped by molecularweight.

Meanwhile adjoining annual medians for Phen, Flt, BaA,Chrys, BkF, BaP, DBahA and Cor did not show a meaningfulvariation from 1999 to 2002. Pyr and Ret showed the majorannual variabilityduring the four years (pb0.05). This suggestsdifferent emission amountsor emission sourcesfor the last twoPAHs with respect to the other PAHs throughout the wholeperiod. Pyrene has been considered a marker of diesel emissionsources (Miguel et al., 1998; Guoa et al., 2003), while reteneis amarker of softwood combustion generated from the burning of

Table 1

Seasonal medians (10th90th percentiles) of daily mean meteorological parameters. January 1999December 2002.

Season Abbreviation Period N Temperature,C Specific humidity,G kg1 Wind speed,M s1

Dry-Warm 1 DW1 JanApr 99 24 17.0 (13.420.5) 7.0(5.08.7) 1.7 (1.12.3)Rainy 1 R1 MayOct 99 28 17.8(14.620.1) 10.7(8.013.3) 1.7 (1.12.4)

Dry-Cold 1 DC1 Nov 99Jan 00 8 12.8(9.814.5) 6.8a (4.48.6) 1.5 (1.12.1)Dry-Warm 2 DW2 FebApr 00 12 15.9a (14.018.3) 5.7a (4.78.1) 1.7 (1.32.5)Rainy 2 R2 MayOct 00 28 16.0a (14.417.9) 11.1(9.012.6) 1.7(1.42.5)Dry-Cold 2 DC2 Nov 2000Jan 01 20 13.2(11.215.4) 7.6a (5.510.0) 0.8(0.31.3)Dry-Warm 3 DW3 FebApr 01 26 16.7(14.420.3) 7.0a (4.311.1) 1.1a (0.62.0)Rainy 3 R3 MayOct 01 47 18.9(16.720.7) 13.9(8.716.2) 1.2a (0.62.1)Dry-Cold 3 DC3 Nov 01Jan 02 17 16.7(16.118.3) 9.6a (6.810.9) 0.2(0.011.0)

Dry-Warm 4 DW4 FebApr 02 21 17.7 (14.020.2) 8.6a (5.610.4) 0.7(0.11.3)Rainy 4 R4 MayOct 02 37 15.6 (14.617.9) 11.1(8.012.6) 1.6(1.12.4)Dry-Cold 4 DC4b NovDec 02 2 16.0 (15.316.8) 10.1 (6.913.4) 1.9 (1.42.4)

N Number of sampling days. The values in bold italic format are meaningfully different between adjoining seasons (p0.01).a

Contiguous medians were not statistically different.b The meteorological station failed during most days of this season.

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vegetation (Ramdahl, 1983). Bravo et al. (2006) and Villalobos-Pietrini et al. (2006)described an increase of retene and PM10at the same sampling site as our study in 1998 and 1999,respectively. Retene was especiallyhigh during 1998due to thepresence of many fires around the sampling zone caused by

severe dry conditions of phenomenon

El Nio

. Be

P, I123cd

PandBghiP were also constant during the wholeperiod but witha meaningful increase in 2002 (pb0.05). Meanwhile Ant wasstatistically different between 2000 and 2001; BbF was onlydifferent between these years.

To observe PAH seasonal variability, our study classifiedPAH by summing Phen, Ant, Ret, Flt and Pyr as light PAH, whilethe sum of BaA, Chrys, BbF, BkF, BeP, BaP, Per, I123cdP, DBahA,BghiP and Cor, was classified as heavy PAH. The medians oflight PAHs were statistically different among seasons (pb0.05)(Fig. 4a), except for the adjacent seasons, R1DC1, DC1DW2,and DC2DW3, and ranged from 1.6 to 3.0 times greaterconcentrations in dry seasons than in rainy seasons. Heavy

PAHs were also different (p

0.02)(Fig.4b) except for R1

DC1,DC1DW2, and DW3R3, and ranged from 1.3 to 2.3 timesgreater concentrations in dry seasons than in rainy seasons.

PAH sum concentration between rainy and dry seasons weredifferent in the period 20002002 (pb0.05). However, thiswasnot true during 1999 between R1 and DC1 (p>0.05), dueprobably to high variability of PAH concentrations in DC1,which included the median of PAH in R1. These ratios were

similar to those found in Hong Kong (Della Sin et al., 2003), butmuch lower than found in several other cities of China (Kong etal., 2010).

The increase of heavy PAH in the dry cold season has alsobeen observedelsewhere (Guzmn-Torres et al., 2009; Amador-Muoz et al., 2010, 2011; Kong et al., 2010 and referencedtherein) due to lower temperatures during the dry cold season,resulting in lower boundary mixinglayersand a greater numberof thermal inversions compared to the dry warm and rainyseasons (SMA, 2002), as well as decreased photochemicaloxidation intensity (Park et al., 2002).

PAH concentrations in our study were lower than reportedpreviously by Villalobos-Pietrini et al. (2006) during 1998

(Jan

Dec), by Guzmn-Torres et al. (2009) during 2003(March), bySaldarriaga et al. (2008)during 2004 (FebApr)(for the majority of PAH), but of the same order for BaP andI123cdP and byMugica et al. (2010a)during three campaignsin the period 2005 (Feb) to 2006 (Jan). All these studiesdeveloped in the same sampling zone than our study. Howeverthey were higher than observed byMugica et al. (2010b)at asite north of Mexico City during three campaigns in the period2005 (Feb) to 2006 (Jan). It is important to highlight that PAHamounts reported by Villalobos-Pietrini et al. (2006) wereabnormal due to presence of phenomenon El Nio thatincreased their concentrations generated from biomassburning around the sampling zone. Although PAH compar-

ison among different studies is difficult due to differentanalytical methodologies employed, it seems an increase of

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

DW1

R1

DC1 DW2

R2

DC2 DW3

R3

DC3 DW4

R4

Frequency

Season

NE SW SE NW

Fig. 2.Predominant wind directions blowing from are indicated as frequency by season from January 1999 to December 2002. The meteorological station failedduring most days of DC4.

Table 2

Annual PM10mass concentrations in g m3.

Year N Median(10th90th percentiles)

Arithmetic mean(Standard deviation)

1999 58 62(3596) 66242000 68 56(2796) 58262001 89 48 (2876) 50192002 73 54 (3375) 5517

N Number of observations. Values in bold italic format are meaningfullydifferent between adjoining periods (pb0.05).

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PAHconcentrations along thetime.The most abundant PAHsin the four years were benzo[ghi]perylene (23.5 2.3%),coronene (14.92.0%), and indeno[1,2,3-cd]pyrene (11.80.9%), followed by benzo[e]pyrene (8.3 0.5%), benzo[b]

fluoranthene (7.61.3%) and benzo[k]fluoranthene (7.41.3%), indicating similar types of sources. Percentages inparenthesis indicate the contribution of the specific PAH tototal PAH.

DW1 R1 DC1 DW2 R2 DC2 DW3 R3 DC3 DW4 R4 DC4

Season

0

20

40

60

80

100

120

140

gm-3

Fig. 3.Median (middle squares) seasonal PM10mass concentrations at the site in southwest Mexico City from January 1999 to December 2002. Boxes

25

75% andwhiskers 10th90th percentiles, circles outliers, asterisks extremes values. For abbreviations, see text.

Table 3

Annual medians of PAH mass concentrations in PM10(10th90th percentile) (pg m3) from the sampling days of 1999 to 2002 at a site in southwest of Mexico

City.

PAH Abbreviation 1999a 2000b 2001c 2002d

Phenanthrene Phen 116 (39250) 122 (63270) 141 (87240) 135 (78239)Anthracene Ant 21 (1038) 17(744) 25(1640) 21 (1335)Fluoranthene Flt 230 (93514) 204 (95529) 260 (145511) 253 (126460)Pyrene Pyr 334 (120747) 290(135704) 387(225718) 322(163593)Retene Ret 134(26538) 5(4164)II 83(51258) 97 (49261)Benzo[a]anthracene BaA 143 (46361) 155 (61403) 165 (87334) 175 (82380)Chrysenee Chrys 212 (66518) 200 (94492) 187 (112435) 234 (111485)Benzo[b]fluoranthene BbF 588(1941 179) 417III (147963) 368III (199814) 505(3141030)Benzo[k]fluoranthene* BkF 454 (159896) 474 (2401025) 382 (236857) 440 (178930)Benzo[e]pyrene BeP 506 (1761052) 474 (235950) 461(290924) 601(3561009)

Benzo[a]pyrene BaP 240 (63649) 313 (129787) 274 (154725) 357 (187730)Perylene Per 33(092)IV 53III (21108)IV 48III (25108)IV 74(19142)V

Indeno[1,2,3-cd]pyrene I123cdP 734 (2301587) 700 (3061353) 623(3811388) 896(5061544)Dibenzo[a,h]anthracene DBahA 45 (1491) 43 (1689) 43 (1897) 46 (2795)

Benzo[ghi]perylene BghiP 1342 (4272793) 1289 (6312644) 1342(7462891) 1856(10582994)Coronene Cor 892 (2671850) 755 (3401624) 855 (4962024) 1077 (5642257)Light PAHI 926 (2932145) 686(3021595) 877(5311702) 836 (4681636)Heavy PAHI 5301 (157811,174) 4953 (239310,186) 4636(282910,148) 6206(358811,399)

Values in bold italic format are meaningfully different between adjoining years (p b0.05).a N=58.b N=69.c N=88.d N=73.e It is probably chrysene coelute with triphenylene and benzo[ k]fluoranthene with benzo[j]fluoranthene in the HP5-MS capillary column employed in this

study, sinceAmador-Muoz et al. (2011) found triphenylene and benzo[j]fluoranthene in a similar retention time than chrysene and benzo[k]fluoranthene,

respectively, at the study site in southwest Mexico City employing a DB-35MS.I The values were calculated taking into account the corresponding PAH sum in each sampling day by year.II Some values of retene were lower than quantification limit.III Contiguous medians were not statistically different.IV

All daily values of perylene were lower than quantification limit.V Some daily values of perylene were lower than quantification limit. Light and heavy PAH are described in text.

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3.3. PM10and PAH annual trends

The trend of mass concentrations for any atmospheric

pollutant is an important parameter to evaluate the controlstrategies implemented by the government to maintain good airquality.Fig. 5shows non-seasonal time series of PM10through-out 1999 to 2002, calculated to consider the daily values and themonthly means for the four years (Bravo et al., 2006). A non-linear decrease of PM10trends was observed as shown by theANOVA analysis on residuals of PM10values, where a lack of fitwas present (p=0.028).

Opposite to PM10nonlinear behavior, some PAHs showed asignificantlinear increase during the whole period as shown bythe absence of lack of fit (p>0.05) of non-seasonal values inthe linear regression model (Table 4).

Coronene showed a nonlinear increase (ANOVA test on

residuals, p= 0.024). Fig. 6 shows the concentration increasingfor benzo[a]pyrene which is taken as an example since it is a

known human carcinogen (IARC, 2010). The opposing trendsbetweenPM10 (nonlinear) and PAH (linear)suggeststhat,whilethe air quality programs reduce PM10 mass concentrations

(DDF, 1996), it was not true for some PAHs. This resultestablishes the importance of considering the chemical compo-sition in proposing a more realistic limit for the PM10 value. Thenegative PM10trend from 1999 to 2002 is consistent with thefinal report in 2002 (SMA, 2002) and 2003 (SEMARNAT, 2003)by the Mexico Government. The reduction of PM10 concentra-tions benefits the peoples' health as showed by Jahn et al.(2011) who estimated a reduction in premature deaths andmortality rates when PM10 was diminished under severalreduction scenarios. However, the increase of some PAHs canbe due to the increasing number of several primary incompletecombustion sources types or by increasing the emissionsamounts of those already present, leading to health conse-

quences such as chronic obstructive pulmonary disease andlung cancer (WHO-UNDP, 2001).


Season

0

2

4

6

8

10

12

14

16

18

ngm-3

ngm-

3


Season

0

2

4

6

8

10

12

14

16

18b

a

Fig. 4.Median (middle squares) seasonal of a. light PAH and b. heavy PAH mass concentrations at a site in southwest Mexico City from January 1999 to December2002. Boxes 2575% and whiskers 10th90th percentiles, circles outliers, asterisks extremes values. Abbreviations seeTable 3.



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3.4. PAH risk health assessment approach

BaP has been considered a good index for whole PAHcarcinogenicity(WHO, 1987) and as a markerof the carcinogenicpotency (EPAQS, 1999), and remains an important surrogate forPAH. The average annual (standard deviation) value of BaP inour study in 1999 was 0.310.22 ng m3; in 2000 it was0.390.27 ng m3; in 2001 it was 0.360.23 ng m3; and in2002 it was 0.400.21 ng m3. All means were higher than0.25 ng m3 of BaP, the annual average United Kingdom (UK)air quality standard (EPAQS, 1999) for ambient air, but lowerthan 1 ng m3, the annual average proposed by the EuropeanUnion (European Directive, 2004) for PM10.The UK standardand EU proposal are non-mandatory ambient air qualitystandards. Although PM10 during four years did not exceedthe established maximum limit of 120g m3 in 24 h ofexposure (NOM-025-SSA-1993), the increase of some organiccarcinogenic compounds, such as BaP, must be considered inthe risk assessment. The U.S. Environmental Protection Agency(USEPA) has not established a reference concentration or areference dose for polycyclic organic matter (USEPA, 1999a) orfor BaP (USEPA, 1999b) on which chronic effects (no cancer)are observed. Based on the guidelines proposed by TheCalifornia Environmental Protection Agency (CalEPA, 2009) in

the air toxics hot spots program guidance manual for prepara-tion of health risk assessments (OEHHA, 2003), it is estimatedthat a median (10th90th) cancer health risk of 7.6 (3.417.2)additional cases per 10 million population for the maximumexposed individual resident at the site in southwest Mexico Cityexists and is related to early-life susceptibility to carcinogens.These estimates considered a 70 year lifespan, the median, 10thand 90th percentile concentrations of seven carcinogenic PAHsums (BaA, Chrys, BaP, BbF, BkF, DBahA, and I123cdP) on PM10(determined from the four years of this study), and the potencyequivalent factors related to BaP of all PAHs to estimate thecancer slope factors (OEHHA, 2009). The calculation did notcover non-inhalation or multipathway exposure routes forhealth risk assessment, which would cause an increase in risk.These results are relevant since the estimated cancer risk is onlyassociated with these seven PAHs found in the study site insouthwest Mexico City which is a zone with the lowest PAHmass in Mexico City (Guzmn-Torres et al., 2009; Mugica et al.,2010a, 2010b; Amador-Muoz et al., 2011). That risk canincrease by exposure to other hazardous species such as metalswhich have also been found in PM10(Bez et al., 2007; Mugicaet al., 2009). Another usual mannerto estimate thehealth risk of

PAH is based on its Ba

P equivalent concentrations (Ba

Peq),employing the toxic equivalent factor (TEF) which representsthe relative carcinogenic potency of the corresponding PAH, assuggested by Nisbet and LaGoy's TEFs (Nisbet and Lagoy, 1992).BaPeq were calculated by multiplying each of the individualseven carcinogenic PAH concentrationsby its corresponding TEFvalues. Seasonal median BaPeq range from 0.352 ng m3

(0.1251.086 ng m3) in R1 (MayOct 1999) to 1.150 ng m3

(0.8781.560 ng m3) in DC4 (NovDec 2002), indicatingaround three times greater risk in dry seasons compared torainyseasons from 1999 to 2002. BaPeq values in ourstudy weresimilar to those found in Zaragoza, Spain (Calln et al., 2011) orin So Paulo, Brazil (Vasconcellos et al., 2011), but less than that

found in several cities of China (Kong et al., 2010), taken asexamples.

36000 36200 36400 36600 36800 37000 37200 37400 37600 37800

Julian Date

-60

-40

-20

0

20

40

60

80

PM10residuals

,

gm-3

Fig. 5.Times series and trend of non-seasonal values of PM10mass concentration from January 1999 to December 2002. ANOVA test on PM10residuals, p=0.028.Dashed line is only to facilitate the trend visualization but it is not linear.

Table 4

Linear increase of total PAH mass concentration from January 1999 toDecember 2002. p-Level values for linear slopes and lack of fit with ANOVAanalyses are indicated.

PAH Total massconcentrationincreasepg m3

p (slopes) p (lack of fit)

Phenanthrene 29 b0.04 0.31Fluoranthene 88 b0.01 0.28

Benzo[a]pyrene 88 b0.02 0.78

Benzo[ghi]perylene 438 b0.002 0.18



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3.5. Correlation analysis

3.5.1. PM10and PAH vs. meteorological parameters

The associations among PM10, PAH and meteorologicalparameters were evaluated with raw data from the four years(N=288). They were negative in all cases. Fig. 7a shows thestrong dependency between PM10and specific humidity (SH)(R=0.47, pb0.05), however dependency with temperature

was notsignificant and quite weak with wind speed (R=

0.24,pb0.05). In the case of PAH, the meteorological parametersindicated quite weak but significant associations (pb0.05). Thecorrelations with specific humidity ranged between R=0.20(anthracene) and R=0.57 (fluoranthene).Fig. 7b showsan example of SH vs. BaP correlation. With temperature,PAHs showed significant association between R =0.13(phenanthrene) and R =0.30 (benzo[a]pyrene), whilewith wind speed associations were between R=0.31(dibenzo[a,h]anthracene) and R=0.42 (benzo[ghi]perylene).Wind speed is an importantcontrolling factor on concentrationsof air pollutants (Motelay-Massei et al., 2003; Akyz and abuk,2009).

Higher mass concentrations of PM10 (Fig. 3) and heavyPAH (Fig. 4b) in the dry periods with respect to the rainyseason are favored by lower water vapor content in theatmosphere in dry seasons as shown in Table 1. A mean of65.49.4% of PM10 mass and 58.016.8% of heavy PAHwere removed from the atmosphere (difference in massesbetween the dry and the rainy seasons, divided by the massin the dry season) when higher water vapor content was inthe atmosphere. These values were greater than the 52.16.7% of particle mass and 43.916.9% of heavy PAH removedfrom the atmosphere during 19981999 in the same zone asour study (Amador-Muoz et al., 2010). However, thetemperature in the rainy season was higher than in the dry

season, and the volatilization effect of PAH must be consideredin the deposition percentage.Masclet et al. (1988)described a

60% decrease in PAH concentration due to precipitation, whichwas quite similar to our result. Lower PAH concentrationsduring rainfall events have been observed elsewhere (Simcik,2004; Tan et al., 2006).

3.5.2. Association between PM10, PAH and criteria atmospheric

pollutants (CAPs)

Spearman correlation analyses among PM10 and PAHversus

CAPs were developed for the whole period, using non-seasonalvalues and no trend (Amador-Muoz et al., 2011). Theassociations among PM10 and individual PAHs were significant(pb0.05) and positive but quite weak, which ranged fromR= 0.2 (Coronene) to R= 0.43 (Chrysene), except with retene,where no significant correlation was observed. PM10 wasmeaningfully correlated with all CAPs (Table 5), with strongerassociations with O3 and NO2, and weaker associations withlight and heavy PAH. This probably suggests more abundanceof secondary than primary species in the PM10 chemicalcomposition in southwest Mexico City. This zone has beenconsidered as a receptor source (Guzmn-Torres et al., 2009)with more oxidized organic species compared to the rest of

MCMA (Amador-Muoz et al., 2011). Some PAHs (Phen, Flt,Chrys, BbF, BkF, and BeP) showed weak meaningful correla-tions (R~ 0.15)with SO2 (industrial emission marker), and onlyFlt, Chrys and BkF (R~ 0.15) correlated with O3 (photochemicalproduct). However, higher associations (pb0.05) were ob-tained with primary pollutants: with CO they ranged fromR=0.14 (DBahA) to R=0.34 (BghiP); with NOx from R=0.21 (Flt) to R=0.38 (BghiP); and with NO2from R=0.17(DBahA) to R= 0.34 (BeP). CO is a marker of incompletegasoline combustion (Fernndez-Bremauntz and Ashmore,1995) and NOxand NO2are emitted from vehicle combustion,power plants, fossil-fuel burning industries (Rijnders et al.,2001) and gas stoves in indoor environments (CEOHAATS,

1996). This suggests that PAH are more related with primarycombustionsources. Retene did not show significant correlation

36000 36200 36400 36600 36800 37000 37200 37400 37600 37800

Julian Date

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

BaPresiduals,

ngm-3

Fig. 6.Times series and trend of non-seasonal values of benzo[a]pyrene BaP concentration from January 1999 to December 2002. ANOVA test on BaP residuals,p=0.78.



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with any CAP, indicating different sources from therest of PAHsas previously established (Bravo et al., 2006; Villalobos-Pietriniet al., 2006).Table 5reviews the association of CAPs with PM10

and light and heavy PAH.

3.6. PAH sources

Diagnostic ratios among PAHs (Sienra et al., 2005; Tsapakis

and Stephanou, 2005; Vasilakos et al., 2007) and principalcomponent analysis (PCA) (Ravindra et al., 2008; Pietrograndeet al., 2011; Amador-Muoz et al., 2011) have been a convenientapproach to identifying possible emission sources. However,thefirst approximation needs to be used with caution because PAHprofiles in receptor sites can be different from those in thesourcessites (Zhang et al., 2005), since they can be altered dueto the chemical reaction with other atmospheric pollutants,such as NOx and O3 (Robinson et al., 2006). In addition,degradation that may occur during the sampling process canalso modify the atmospheric PAHs levels and thus the ratiosbetween PAHs (Tsapakisand Stephanou, 2003). Differenteffectson PAH due to the physicochemical properties of the paired

PAHs

which are commonly applied

are not identical(Masclet et al., 1986; Kamens et al., 1988). It was done as an

0 2 4 6 8 10 12 14 16 18

Specific humidity, g kg-1

0

20

40

60

80

100

120

140

160

gm-3

0 2 4 6 8 10 12 14 16 18

Specific humidity, g kg-1

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

ngm-3

Fig. 7. Association between (a)specific humidity vs.PM10,R =0.47, pb0.05, and(b) specific humidity vs.BaP, R=0.38, pb0.05. N= 288January 1999December 2002.

Table 5

Spearman correlation coefficients between PM10(g m3), light and heavy

PAH (ng m3) and gaseous atmospheric criteria pollutants (CAPs) (ppm) at

the study site in southwest Mexico City. N=287.

Pollutant PM10 Light PAH Heavy PAH

O3 0.57 0.08 0.07SO2 0.34 0.07 0.07

CO 0.33 0.20 0.31NOx 0.28 0.21 0.36 NO2 0.54 0.20 0.33

PM10 1.00 0.27 0.30

Statistically significant correlation Spearman coefficients (pb

0.05) areindicated in bold and italic form.



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approximation employing the ratios indicated inTable 6. Theseratios were selected due to consistency among different studiesand with our results. The ratios in our study were quite constantas observed by percentiles, indicating no changes in the maincombustion sources over four years. However, from theTable 6it would be difficult to differentiate diesel emission from woodcombustion based only on one proposed diagnostic ratio

[(I123cdP/(I123cdP+BghiP)] or gasoline engines from naturalgas based only on [Flt/(Flt+Pyr)]. However, source identifi-cation can be improved using various other ratios proposed inTable 6, performingtheir relative comparison and supported byother statistical tools as PCA.

PCA is a method which reduces the dimensionality of dataseries while retaining the original information as much aspossible. Variables with similar emission source, chemicalproperties or chemical interactions are grouped into factors.However, these factors indicate more than one possiblecause. In this study, each factor was associated with a sourcecharacterized by their(s) PAH marker(s). The analysis wasdone for each year by employing correlation matrix with a

normalized Varimax rotation and eigenvalues equal to 1.0.Results are showed inTable 7. Supplemental material shows allthe factors loadings (Table S1) and the eigenvalues and theexplained variance for each factor (Table S2) by year.

The sources for PAH in the study site in southwest MexicoCity from 1999 to 2002 were dominated by gasoline and dieselengines. Benzo[ghi]perylene has been described as a tracer forgasoline-powered vehicles, while light PAHs have been foundpredominantly in diesel vehicle emissions (Miguel et al., 1998;Zielinska et al., 2004; Sienra et al., 2005) as well as in coalcombustion (Kong et al., 2010 and referenced therein).However from1999 to 2001, gasoline engines emission profileswere quite constant, since the majority of heavy PAH was

grouped in factor 1, explaining between 64 and 73% of thetotalvariance. Meanwhile in 2002, heavy PAH were observed infactor 2 which explained less variance (11%). This suggests achange of emission amounts between gasoline and dieselcombustion sources, or, to the contribution of other source(s)

which changed the PAH profiles. As observed inTable 7, themain sources in southwest Mexico City are gasoline and dieselcombustion and to a lesser extent wood combustion. Two ormore diagnostic PAH ratios observed inTable 6also suggestto these sources as their main air PAH emitters. This is inaccordance with previous observations (Villalobos-Pietriniet al., 2006). In addition, diagnostic PAH ratios suggest natural

gas combustion as another important source (Table 6). Retenewas separated from the majority of PAH, and grouped withthose factors with less explained variance (in all years),because its origin is from wood combustion (Ramdahl, 1983).Although PM10was grouped in factor 1 in 1999 and 2002, thestronger association with O3and NO2(r>0.5) and the quiteweak correlation with PAH (light and heavy) (rb0.4) (Table 5)suggests different emission sources from gasoline, diesel orwood combustion. This conclusion is in accordance with thegreater abundance of secondary than primary organic speciesin the PM10chemical composition as established previously.Similar observations for particles less than 2.5 m were madebyAmador-Muoz et al. (2011)for the same sampling zone.

4. Conclusions

Seasonal variation was observed for PM10and PAH from1999 to 2002 in southwest Mexico City, with major massconcentrations during the dry season (NovemberApril).Benzo[ghi]perylene was the most abundant PAH, followedby coronene and indeno[1,2,3-cd]pyrene. Meteorologicalparameters act as good cleaners from the atmosphere forboth PM10 and PAH. While a negative nonlinear trend forPM10 was observed for the period as a whole, a significantlinear increase was observed for phenanthrene, fluoranthene,benzo[ghi]perylene (BghiP) and benzo[a]pyrene (BaP). The

last two PAHs represent a relevant importance due to BghiPbeing the most abundant PAH found in our study and in otherprevious campaigns carried out in Mexico City, while BaP isconsidered a good marker of carcinogenic potency. Theannual average of BaP exceeded the UK recommendation.

Table 6

Diagnostic ratios of PM-bound PAH at a site in southwest Mexico City.

Diagnostic ratios/Sources Diesel engines Gasoline engines Natural gas combustion Wood combustion This studyMedians (10th90th)I

I123cdP/(I123cdP+BghiP) 0.350.7 a,b,k 0.18c 0.32h 0.62n 0.33 (0.300.37)BaP/BghiP 0.30.4d 0.300.44k 0.21 (0.150.30)Flt/(Flt+Pyr) >0.5 a,b 0.4k,0.44l,0.350.51m 0.49h 0.51h 0.43 (0.370.46)Ant/(Ant+ Phen) 0.35e 0.5e 0.12h 0.16h 0.14 (0.100.19)

BaA/(BaA +Chrys) 0.68 f,g 0.49 f,g 0.39h 0.43 i,j 0.43 (0.380.48)

Bold values are included in the range of ratios obtained in this study. For abbreviations, see Table 3.a Ravindra et al. (2006).b Ravindra et al. (2008).c Sienra et al. (2005).d Bourotte et al. (2005).e Guoa et al. (2003).f Vasilakos et al. (2007).g Khalili et al. (1995).h Galarneau (2008).i Manti et al. (2005).j Akyz and abuk (2008).k Kavouras et al. (2001).l Rogge et al. (1993).m Sicre et al. (1987).n

Grimmer et al. (1983).I Medians and 10 and 90 percentiles were calculated considering all daily values from 1999 to 2002.

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Cancer health risk assessment based on seven carcinogenicPAHs and BaP equivalent concentrations were estimated forthe health risk assessment of PAH, however, more studies arenecessary to get more precise estimation. Diagnostic PAHratios and principal component analysis revealed gasoline anddiesel vehicular emissions as major sources for PAH com-pounds in Mexico City, but burning vegetation around thesampling site was considered as well, as confirmed by thepresence of retene. During 2002, PCA indicated a change ofemission amounts between gasoline and diesel combustionsources or a contribution of other source(s) which changed the

PAH profiles. These results will contribute to the considerationin more detail of the limiting value for PM10in Mexico City, aconsideration not based only on mass concentrations.

Supplementary data to this article can be found online athttp://dx.doi.org/10.1016/j.atmosres.2012.10.003.

Acknowledgments

We would like thank Agustin Eguiarte, Brenda Liz Valle,Jos Ramn Hernndez, Wilfrido Gutirrez, Manuel Garca,Delibes Romn and Sal Armendariz for their technicalassistance. Claudio Amescua and Pietro Villalobos for their

writing assistance. Winston Smith of Peace Corps Mexico forthe revision of this manuscript. We also thank ArmandoRetama and Olivia Rivera of the Atmospheric MonitoringNetworkof the Distrito Federal Government, Mxico forthe dataon atmospheric pollutants and meteorological parameters. Thisstudy was funded by project 34340-T of the Consejo Nacional deCiencia y Tecnologa (CONACyT), and partially by projectsIN213100 andIN116810-3 of thePrograma de Apoyo a Proyectosde Investigacin e Inovacin Tecnolgica (PAPIIT), UNAM.

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Table 7

Main sources identified with factor analysis for each year, based on variables grouped in each factor. In parenthesis is the range of factors loadings.

Year Factor 1 Factor 2 Factor 3 Total EV, %

1999a Gasoline engines andother source(s) Diesel engines andwood combustion

PM10(0.8);Phen, Flt (0.6);Individual heavyPAH(0.71.0)

Ant, Pyr (0.8);Ret (0.9)

% EV 73 8 812000b Gasoline engines Wood combustionand other source(s) Diesel engines

Individual heavy PAH(0.70.9) Ret (0.9); PM10(0.7) Phen, Ant, Flt, Pyr (0.70.8)% EV 73 9 7 882001c Gasoline engines Diesel engines andwood combustion Other source(s)

Individual heavy PAH(except Chrys) (0.60.9) Phen, Ant, Flt, Pyr (0.70.8); Chrys (0.6);Ret (0.7) PM10(0.9)% EV 64 8 7 792002d Diesel engines and other source(s) Gasoline engines Wood combustion

PM10 (0.8);Phen, Pyr (0.6);Flt (0.8), Chrys (0.7) Individual heavy PAH

(except BaA, Chrys and Cor) (0.60.9)

Ant (0.8);Ret, BaA (0.6);Cor (0.7)

% EV 57 11 6 75

The factor analysis was done employing the daily non-seasonal and no trend concentrations of PM 10and of individual PAH. The years from 1999 to 2001 did notconsider perylene since it values were lower than quantification limits. % EV Percentage of explained variance. Individual heavy PAH include: BaA, Chrys, BbF,

BkF, BeP, BaP, Per, I123cdP, DBahA, BghiP and Cor. For PAH abbreviations, seeTable 3.a N=58.b N=69.c N=88.d N=73.

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