AtmosphericPollutionResearch2(2011)409Ͳ417
Atmospheric Pollution Research
www.atmospolres.com
Levels,compositionandsourceapportionmentofruralbackground PM10inwesternMexico(stateofColima) ArturoA.Campos–Ramos1,AntonioAragon–Pina1,AndresAlastuey2,IgnacioGalindo–Estrada3, XavierQuerol2 1
InstitutodeMetalurgia,UniversidadAutónomadeSanLuisPotosí;AvenidaSierraLeona#550,Col.LomasSegundaSección.C.P.78180.SanLuisPotosí, SanLuisPotosí,Mexico 2 InstituteofEnvironmentalAssessmentandWaterResearch(IDAEA)CSIC,C/JordiGirona,18,08034Barcelona,Spain 3 CentroUniversitarioenInvestigacionesdeCienciasAmbientales,UniversidaddeColima.CarreteraColimaͲCoquimatlanKm9.5,Mexico
ABSTRACT
ThevariabilityoflevelsandcompositionofPM10wereinvestigated(October 2006toMay 2007)inasemi–rural classified site located in the central region of the state of Colima (Western Mexico), away from the direct 3 influence of pollution sources. Mean PM10 levels reached relatively high values (48μg/m ), due mainly to 3 3 3 enhanced loads of crustal material (15μg/m ), carbonaceous aerosols (10μg/m ) and sulfate (9μg/m ). Dust resuspension,volcanic,industrial,agricultural,farming,androadtrafficemissions,aswellasregionalpollution wereidentifiedasthemaindriversofthisvariability.Furthermore,meteorologicalfactorsatlocal,regionaland synoptic scales influenced such PM10 variability. A differentiation was found between the cold period, with 3 3 average PM10 levels reaching 42μg/m , and the warm period, with 56μg/m . The first study period was 3 Ͳ2 characterized by high levels of SO4 (19μg/m ) arising from both anthropogenic and volcanic emissions, 3 whereas the second one is characterized by a high contribution from crustal material (22μg/m ) from soil resuspension and volcanic ash. Principal component analysis (PCA) showed that the highest contribution to PM10levelsarisesfromtheregionalpollution(49%),followedbytheindustrialemissions,fueloilcombustion, road traffic and secondary sulfate (18,17,10and6%, respectively). This shows a large anthropogenic load in rural background PM pollution, consequently, we propose to build an air quality monitoring network in rural andurbanareasofthisregion. ©Author(s)2011.ThisworkisdistributedundertheCreativeCommonsAttribution3.0License.
Keywords: PM10 Sourcesapportionmentanalysis Soildust Volcanicdust
ArticleHistory: Received:10May2010 Revised:08April2011 Accepted:26April2011
CorrespondingAuthor: AndresAlastuey Tel:+34Ͳ934006100 Fax:+34Ͳ932045904 EͲmail:
[email protected]
doi:10.5094/APR.2011.046
1.Introduction Atmospheric particles having a diameter of less than 10μm (PM10)consistofsolidand/orliquidparticlesthatareincorporated into the atmosphere by natural and anthropogenic emission sources. Atmospheric processes and multi–source origin tend to generate a complex mixture of aerosol components of different chemicalandphysicalcharacteristics. Primary particles are directly released into the atmosphere. Secondaryparticlesareformedwithintheatmospherebygas–to– particleconversionfromgaseousprecursorssuchassulfurdioxide (SO2), nitrogendioxide (NO2), ammonia (NH3) and volatile organic compounds.Moreover,somestudieshaveshownthatsuspended airparticlesareassociatedwithdifferenttypesofsources,andthat their origin (metallurgical, foundry, steel, chemical industry or vehicle emissions) can be traced based on their microscopic morphologyandchemicalcomposition(Aragonetal.,2006). It is widely accepted that high concentrations of ambient air particles cause adverse health effects on humans (Dockery et al., 1996;DonaldsonandMacNee,1998;Donaldsonetal.,2000;Pope etal.,2002;WHO,2005),suchasasthmaproblemsinchildren(Lee etal.,2006).Furthermore,anumberofecologicaleffects(negative andpositive)causedbythepresenceofatmosphericparticleshave beenreported(Grantzetal.,2003).
The state of Colima (Western Mexico, Figure1), one of the smaller states of the country with only 0.3% of the total area (5455km2), has shown an important population growth (650555inhabitants in 2010) and economic development in last few years mainly related to the expansion of the industrial, agricultural and cattle breeding sectors (INEGI, 2005). As a consequence, Colima’s air quality has been progressively influencedbyawidevarietyofPMemissionsourcesresultingina complex mixture of aerosols. Furthermore, Colima’s air quality is frequentlyinfluencedbythevolcanicemissionsfromtheVolcande FuegodeColima. As stated above two of the main economic activities performed in the state of Colima are agriculture and cattle breeding. These might emit a wide variety of primary and secondary PM. The indiscriminate use of fertilizers and the frequent occurrence of controlled burning of sugar cane crops performed previously to harvest in the period from October to May highly contribute to increase atmospheric emission of pollutants. In USA regions with high cattle breeding (such as Eastern North Carolina), high concentrations of secondary inorganic aerosols (SIA) related to the conversion of gaseous precursors from farming and the use of animal manure as fertilizers have been found. Thus, in Eastern North Carolina, the meanconcentrationofSIA(SO42–,NO3–,NH4+)accountedfor 3 8μg/m in PM2.5 (Walter et al., 2006). A strong correlation between NH4+ and SO42– in farming regions has also been determined(Kellyetal.,2005).
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
410
Figure1.(a)MapofstateColima,showingthelocationofthesamplingsiteandthemainpollutionsources;(b)vegetationtypespredominating agriculturalareas.(a)Thermoelectriccentralandmetallurgicalindustry;(b)mining(Ironoreextraction);(c)sugarindustry.
The information on PM10 levels and composition in the WesternMexicoisscarce.Galindoetal.(1999)reportedhighlevels of Si, S, Cl–, Ca, and Ti, among others. Miranda et al. (2000) identified an association of the elements S, Cl–, Cu, and Zn. A recent study by Campos–Ramos et al. (2009) was devoted to the mineralogical characterization of PM10 in the study area by using scanning electron microscopy (SEM). All these studies related the presenceoftheseelementsinambientairtotheVolcandeFuego eruptions. FurtherresearchisneededtointerpretthevariabilityofPM10 levels and components and to identify the main emission sources influencingambientairPM10levelsinordertodefinecosteffective emission abatement strategies. The present study is aimed to collect and integrate data on the levels and composition of rural backgroundofPM10andtoquantifythecontributionofthemajor PM10 sources taking into account the diverse anthropogenic and naturalsourceslocalizedrelativelyclosetoColimaCity. Colima has a warm climate, with an average annual temperature of 25°C, a mean relative humidity of 65% and an average annual rainfall of 900mm (INEGI, 2005). Winds from the SW predominate with a frequency of 53%. However, there are some recirculation episodes at the regional level characterizedby theeffectsofSW–NEseabreeze(Galindoetal.,1999)whichmay favorthetransporttheparticleemissionsfromsourceslocatedin thissector,andtheageingandformationofsecondaryaerosolsby interactionbetweenparticulateandgaseouspollutantsofdifferent origins. However, since it is located on the western region of the country, is also subject to the influence of anticyclonic systems
generated in the Pacific Ocean. These systems provide a great atmosphericstability,inhibitingtheverticalmixingofair. AmongthemainpollutionsourcesistheVolcandeFuegode Colima, located 35km northeast from the city. This is historically themostactivevolcanoinMexicoandoneofthemostactiveones inLatinAmerica(Galindoetal.,1999).Studiesperformedinurban and rural areas close to the volcano have found an increase in respiratory illnesses which are associated with the presence of particulate matter within a size between 2.5ʅm and 10ʅm (Mirandaetal.,2004). Major anthropogenic emission sources of atmospheric pollutantsintheareaaredepictedinFigure1A,(a)thermoelectric centralandmetallurgicalindustry,(b)mining(Ironoreextraction), and(c)sugarindustrylocatedsouthwest,northwestandnortheast fromCUICA,respectively.Alltheseindustrialactivitiesusefueloil for energy production. It is also important to mention that the majority of the territory has a great abundance of vegetation types, predominating seasonal and irrigated agricultural areas (Figure 1B) and an important factor that favors the presence of secondary inorganic component that have not been evaluated so far.
2.Methodology 2.1.PMsamplingandanalysis ThemonitoringstationislocatedintheUniversityCenterfor Environmental Sciences (CUICA, 103°48´10.6´´W, 19°12´48.9´´N, 500meter above sea level), 12km away from the city of Colima
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
thecentralregionofthestateofColima,westerncentralMexico, approximately 50km distant from the Pacific Ocean coast (Figure1). The station is located in a rural area, away from the directinfluenceofindustrialandtrafficemissions. SamplingwascarriedoutfromOctober2006toJanuary2007 and March to May2007 at the CUICA rural background site. In order to differentiate the sampling periods these are referred as coldandwarmperiodsrespectively.However,giventheclimatein thearea,temperaturesarefairlyconstantintheregion,andthere is not a clear differentiation as regards for temperature between the two periods, with averages of 23°C and 78% RH for October2006toJanuary2007and27°Cand74%RHforMarchto May2007. Twenty four hour PM10 samples were collected by means of 3 high–volume samplers (Tisch Enviromental, 68m /h) on quartz fiber filters (Schleicher and Schuell QF20). During the first period sampling was performed at a ratio of one day out off six; during the second period the frequency of sampling was slightly increased. After discarding a few samples owing to electrical problems, the final number of valid samples was 43, with 25 samplesinthecoldperiodand18inthewarmperiod. Filters were stabilized before and after sampling at 23±2°C and 40±5% relative humidity. Bulk PM10 concentrations were determinedbygravimetry,usingstandardprocedures(NOM–035– SEMARNAT–1993). Thereafter, a fraction of each filter of approximately 150cm2 was acid digested (HF:HNO3:HClO4, with a mixtureof2.5:1.25:1.25ml,respectively,keptat90°CinaTeflon reactor for 6h, driven to dryness and re–dissolved with 1.25ml HNO3tomakeupavolumeof25mlwithwater)forthechemical analysis using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP–AES:,IRIS Advantage TJA Solutions, THERMO) for the determination of the major and some trace elements (Ca, Al, Fe, Mg, Na, K, Ti, Sr and Mn, among others), and Inductively CoupledPlasmaMassSpectrometry(ICPͲMS,XSeriesII,THERMO) forthetraceelements(As,Ba,Cd,Cr,Cu,Mn,Ni,Pb,Sb,Se,Sr,Ti, V,andZn,amongothers). Forqualitycontroloftheanalyticalprocedureasmallamount (15mg)oftheNIST–1633b(flyash)referencematerialloadedona similarfractionofblankquartzmicrofiberfilterwasalsoanalyzed. Concentrations of major and trace elements are in the range of those usually found at ambient air concentrations. Blank concentrations were subtracted. Analytical errors determined were <10% for most elements, with the exception of P and K (<15%). Anotherfractionoffilterofabout75cm2waterleachedwith de–ionized water (30g of Milli–Q grade water) to extract the soluble fraction. The solution obtained was analyzed by ion chromatographyfordeterminationofCl–,SO42–,andNO3–),andby colorimetry–FlowInjectionAnalysis(FIA)forNH4+. A third portion of filter of around 1cm2 was used to determine total carbon content using a LECO elemental anayser (Queroletal.,2004).SiO2andCO32–wereindirectlydeterminedon 2– the basis of empirical factors (SiO2=2.5×Al2O3 and CO3 =1.5xCa+2.5xMg) (see Querol et al., 2001). This ratio was previously determined for dust samples collected in Spain and could differ from the actual ratio in Colima, not determined. Sea salt sulfate (SO42–ss) concentration was determined from the Na concentration according to the Na/SO42– molar ratio observed in seawater(Drever,1982).Thedifferencebetweentotalsulfateand marinesulfateisnamedasnonseasaltsulfate(SO42–nss).Thenon mineralcarbon(Cnm)wasestimatedbysubtractingtheCassociated tothecarbonatesfromthetotalcarbon.Then,OM+EClevelswere estimatedbyapplyingafactorof1.6totheconcentrationsofCnm (Turpinetal.,2003).
411
PM10componentsweregroupedas:crustalmaterial (є Al2O3, SiO2, CO3, Ca, Fe, Mg, K), secondary inorganic aerosols (SIA, є SO42–nss, NO3–, NH4+), sea salt (є Na, Cl, SO42– ss), and total carbon(Ct).ThepercentageofPM10massdeterminedwithrespect thetotalPM10masswas>80%,beingtherestattributedtowater. Itshouldbenotedthatthisdeterminationofpercentagesissimilar to the usually obtained in other areas by using this methodology (75–85%), suggesting that this method is applicable to this area andthatSiO2/Al2O3 ratiousedisprobablyverysimilartothelocal ratio. 2.2.Sourceapportionmentanalysis Inordertoidentifydifferentsourcecontributionstoambient PM10 levels, a Varimax rotated factor analysis (Thurston and Spengler, 1985a) was performed. Factor analysis model aims to describe the input ambient data as a function of source profiles andsourcecontributions,asshownintheequationX=GF,where Xistheconcentrationmatrixforthedifferentcomponents,Gisthe source contribution matrix, and F is the source profile matrix. Quantification of the source contributions is then performed by multi–linear regression analysis (MLRA), using absolute score factors as source tracers. This methodology is described in depth byThurstonandSpengler(1985b). Different source apportionment methods have been widely appliedforaerosolparticulatematterresearch.Acompilationand discussionaboutapplicationofthesemethodsisprovidedbyViana et al. (2008). Recently, a special issue on Application of Receptor Models has been published in Air Pollution Research (Hopke and Cohen,2011). InthisstudyFA–MLRAwasappliedwiththesoftwarepackage STATISTICA4.2toasetof43PM10samples,usingthecomplete(27 elements) chemical data series of concentrations of PM10 as independentvariables.Numberofsamplesavailablewasrelatively low given that a number of samples lower than 50 is not recommended(Henryetal.,1984;ThurstonandSpengler,1985b). 2.3.Meteorologicalanalysis Theoriginoftheairmassesaffectingthestudyareaduringthe sampling period was determined by calculating back–trajectories using the NOAA HYSPLIT4 model (Draxler and Rolph, 2003). Five– day back–trajectories were calculated daily at 12:00GMT, with a 6h interval. According to the topography of the state of Colima, thestartingheightoftheback–trajectorieswassetat500ma.s.l. According to their origin the following five scenarios were considered:1) RE: regional episodes with a relatively low dispersionofpollutants;2)WOP:airmassesfromthePacificocean transportedoverwestMexico;3)NUS:airmassesformnorthUSA passingthroughthenorthofMexico;4)EMG:airmassesfromthe Gulf of Mexico transported over the east of the country; 5) PNS southeast air masses from the Caribbean. Additionally, aerosol mapsfromtheMarineMeteorologyDivisionoftheNavalResearch Laboratory (NAAPS) were employed to identify the episodes with the contribution of anthropogenic material from other zones, mainly from the central portion of the Mexican Republic and the PacificOcean. Data of wild fires occurring during the sampling period were obtained from the CUICA database (Galindo and Dominguez, 2002),andthenumberofdailyexhalationsofVolcandeFuegode ColimawasobtainedfromtheregisteroftheCivilProtectionState System(http://www.ucol.mx/volcan/boletines/index.htm).
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
412
3.ResultsandDiscussion 3.1.PM10levels Average PM10 levels determined gravimetrically for the 43dailysamplescollectedinthestudyperiodwere48±16.2μg/m3. PM10concentrationswererelativelylowduringthecolderperiod, 3 with average PM10 levels of 42±17μg/m , and higher during the warmer period, 56±11μg/m3. Around 50% of the samples exceeded the maximum daily guideline value (50μg/m3) established by the World Health Organization (WHO, 2005). By consideringonlyduringthewarmperiodthispercentageincreases, witharound75%ofthesamplingdaysexceedingthe24hvalue. According the HYSPLIT back trajectory analysis, the following type of air masses were identified according their atmospheric transport patterns: RE, Regional; WOP, Western–Ocean Pacific; NEUS, Northeast–United States; EMG, East–Gulf of Mexico; PNS, Peninsula–South. The regional scenario represents low advective conditionswithlocal–regionalairmassorigin. As shown in Table 1, during the cold period RE air masses prevailed (40% of days) over WOP and NEUS (28 and 24%), PNS (8%) and EMG (not detected). In the warmer period, NEUS days dominated (39%) over RE and EMG (28 and 22%), PNS (11%) and WOP(notdetected).LikewiseduringNEUSepisodes,thetransport oftheemissionsfromthenortheastsectorofthestateofColima, suchasthosefromtheVolcandeFuegoandagriculturalactivities arefavored. As regard to PM10 recorded for the different episodes, the highest values were obtained for the RE scenarios in the two periods (56and59μg/m3), indicating that probably the main source of PM pollution has a local–regional origin. Additionally, thesehighPM10levelsmaybefavoredbylow–gradientbarometric scenario that favors the stagnation of air masses at ground levels and the formation of SIA in the cold period, and by the resuspensionofmineraldustfromsoilinthewarmperiod.During the warm period PM10 levels were similarly high for all air mass transport scenarios (53–59μg/m3), whereas in the cold period 3 WOPandPNSyieldedclearlylowerlevels(35and31μg/m ). Itisimportanttonotethatduringthisstudycyclonicsystems generatedonthePacificOceanmainlyduringthecoldperiodwere observed,generatingWOPwindsthatmightfavortheincomingof tropical marine air with light humidity content. This scenario induces low PM10 levels in Colima due to scavenging processes caused by an increased rainfall. On the other hand, in the warm periodthestateisaffectedbytheincomingofwarmandhumidair from PNS favoring the transport and dust resuspension from the rockysubstrate. Galindoetal.(1998)evidencedtheinfluenceofseabreezein the region caused by the prevalence of south–southwest wind direction in diurnal hours (8:00to20:00hGMT), and north– northeastinnocturnalhours(20:00to8:00hGMT). 3.2.PM10speciation ThesumoftheconcentrationsofPM10speciesdeterminedby thepreviouslydescribedmethodologyaccounts,asanaverage,for 84.5%ofthetotalPM10mass,theremainingunaccountedfraction
being attributed to the presence of water molecules in the PM10 sampleandcomponents. Average,maximumandminimumconcentrationsofmajorand trace elements in PM10 during the sampling period are shown in Table 2 and the distribution of PM10 components during the two periods is depicted in Figure 2. Below are summarized the levels obtainedforthemajorgroupsofPM10componentsanalyzed. (1)Mineralmatter:ThesumoftheaverageconcentrationsofMg, K, Fe, Ca, Al2O3, SiO2 and CO3–2 contributes to 32% of PM10 (15μg/m3),asaverageforthewholestudyperiod,showingaclear seasonalvariation.Thus,meancrustalloadaccountsfor10μg/m3 3 (25% of PM10) during the cold period, and for 22μg/m (39%) during warm period, reaching to daily values of up to 32 μg/m3. These values are relatively high when compared with available data from other urban and rural sites in Mexico, (Querol et al., 2008; Vega et al., 2009; Vega et al., 2010), with mean concenͲ trationofmineralmatterrangingfrom10to15μg/m3,accounting for 25% of PM10). Mineral load is also high when compared with other rural sites, such as Montseny (Spain), with average concentrationsof4μg/m3(24%)inPM10(Peyetal.,2009). The high crustal contributions measured, especially in the warm period, may be due to enhanced resuspension of soil and industrial dusts and volcanic ash and to the higher emission of silicate particles from agricultural fires (Campos et al., 2009). It is worthy to note that due to the high crustal load, K in PM10 is mostly associated to feldspars and clay minerals (illite), and not with biomass burning. This is supported by the high K/Al correlation (R2=0.8) and the low K intercept in the regression equation.Actually,afractionofNashouldalsobeattributedtothe mineralcomponentgivendehighNa/Alcorrelation(R2=0.6). (2) The secondary inorganic aerosols (SIA, sum of the concenͲ trationsofSO4–2nss,NH4+andNO3–accountfor30and21%ofPM10 for the cold and warm periods, respectively, showing very similar concentrations at the two periods (12.5and 11.6μgSIA/m3). Average concentrations for the whole period for specific components were 9.3, 2.1 and 1.0μg/m3, for SO4–2nss, NH4+ and – –2 NO3 ,respectively(Table3).TheconcentrationsofSO4 nssmaybe considered as high if compared with usual concentrations 3 measuredatothersitesinMexico,rangingfrom3–6μg/m (Querol etal.,2008;Vegaetal.,2009;Vegaetal.,2010).Bycontrastlevels of nitrate are relatively low (3–6μg/m3). As shown in Figure 3 there is not a clear seasonal variation for SIA components. Thus, + averageconcentrationsofNH4 weresimilarinthecoldandwarm 3 periods (2μg/m ), showing daily peaks of up to 6μg/m3 in the October–November 2006, and January 2007 simultaneously with high levels of sulfate. Levels of nitrate are slightly higher in the warmperiod,withpeaksofupto6μg/m3usuallyrecordedduring wildfireepisodes.Bycontrast,levelsofsulfatearerelativelyhigher in the cold period, showing a more marked variability with daily 3 levelsreachingtovaluesupto19μg/m .AsshowninFigure3,the sulfate peaks are not always related to the volcanic and fire episodes. Origin of these high levels of sulfate may be related to meso–scale air mass transport or emissions from anthropogenic sourcesatregionalandorlocalscales.
Table1.FrequencyofairmasstransportscenariosandmeanPM10levelsrecordedforeachoftheseepisodesduringthecoldandwarmperiodsatCUICA station(RE:Regional,WOP:WesternͲOceanPacific,NEUS:NortheastͲUnitedStates,EMG:EastͲGulfofMexico,PNS:Peninsula–South) ColdPeriod RE WOP NEUS EMG Frequency(%) 40 28 24 ND 3 PM10(μg/m ) 56 35 46 ND ND:Notdetected
PNS 8 31
RE 28 59
WarmPeriod WOP NEUS EMG PNS ND 39 22 11 ND 53 56 56
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
Table 2. Average, maximum and minimum concentrations of major and trace elements in PM10 during the sampling period (October 2006 to May 2007)atthemonitoringstationCUICA N=43 PM10
Average Max 3 (μg/m ) 48.0±16.2 78.5
14.9
P
74.8±32.5
184.3
24.6
OM+EC Al2O3 SiO2 2Ͳ CO3 Ca Fe K Mg Na 2Ͳ SO4 ss 2Ͳ SO4 nss Ͳ NO3 Ͳ Cl + NH4
10.2±3.1 2.6±1.6 7.9±4.8 1.8±1.1 1.2±0.7 0.8±0.4 0.5±0.3 0.3±0.2 0.9±0.5 0.2±0.1 9.0±4.1 1.0±1.0 0.4±0.3 2.0±14
2.7 Ti 0.4 V 1.3 Zn 0.2 Cu 0.2 Ba 0.2 Mn 0.1 Sr <0.1 Ni 0.2 Pb 0.1 Cr 3.4 As 0.1 Sb <0.1 Cd 0.2 Se Mo
70.3±41.5 36.5±28.2 29.8±21.6 25.7±59.3 21.6±30.4 18.4±10.1 8.8±4.6 7.1±5.6 4.2±3.3 1.4±1.5 1.1±2.3 0.9±0.5 0.2±0.2 0.4±0.3 2.4±1.6
159.6 126.6 106.0 398.0 116.0 37.0 18.2 31.1 14.8 7.5 15.8 2.7 0.9 1.4 5.3
11.1 6.2 4.5 4.0 0.2 3.7 1.7 1.0 1.2 0.3 0.3 0.3 0.1 0.1 0.3
16.4 5.9 17.8 6.6 4.4 1.6 1.2 0.7 2.2 0.5 19.1 5.9 1.2 6.8
Min
Average
Max 3 (ng/m )
Min
Coldperiod 42,0μgPM10/m3
Undeter;8,6; 20% Metals;0,3;1%
Mineral;10,3; 24%
Seaspray;1,0; 2%
OM+EC;9,4; 22%
SIA;12,5;31%
Warmperiod
Undeter;8,8; 16%
56,4μgPM10/m3
Metals;0,4;1% Seaspray;2,2; 4%
Mineral;22,1; 38%
SIA;11,6;21%
OM+EC;11,3; 20% Figure 2. Composition of PM10 the CUICA station in the warm and cold periods.
Table 3. Number of events of volcanic exhalations and fires observed aroundtheCUICAstation Month/year November/06 January/07 March/07 April/07 May/07 UR:Unrecorded
Volcanicexhalations 3 23 12 UR 5
Fires UR UR 2 8 20
413
Giventhelowconcentrationsofnitrate,ammoniumisusually related to sulfate, with a clear excess of sulfate, deducing the presence of partially neutralized ammonium sulfate. When considering all the daily samples, correlation of sulfate and ammonium is R2=0.42 (Figure 4a); if concurrent samples with volcanicexhalationsarediscarded,thecorrelationincreasesupto 2 R =0.78 (Figure 4b). Then, it is evidenced that during volcanic eruptionsthereisanimportant contributionof sulfurcompounds to the atmosphere in the study area. Campos et al. (2009) identified by SEM the presence of barium sulfate particles, which wereattributedtovolcanicemissions.Nevertheless,thepresence of sulfuric acid particles formed form oxidation of SO2 volcanic exhalationsshouldbeconsidered. (3)SeasaltwascalculatedasthesumofNa+,Cl–andSO42–sswith anaverageconcentrationforthewholeperiodof1.5μg/m3(3.2% ofPM10).Arelativelyhighconcentrationwasobservedinthewarm 3 period(2.2μg/m ),probablyduetoeffectofenhancedseabreeze circulationspattern(Galindoetal.,1999).Thisseasaltcontribution musttobeconsideredasthemaximumpossiblecontributiongiven thelowcorrelationdeterminedbetweenthelevelsofNaandthose of Cl–. This low correlation may be partially attributed to the interactionbetweenNaClaerosolsandnitricphases(Harrisonand Pio,1983),resultinginthedepletionofCl–.Nevertheless,giventhe high correlation between Na and Al (R2=0.6), it is deduced the existence of a fraction of crustal sodium, usually present in feldspars,suchasalbite,inadditiontothemarineone.Moreover, presence of Cl– may be related to emissions of agricultural fires (Murindietal.,2004;Haysetal.,2005). (4)Organicmatterandelementalcarbon(OM+EC),calculatedfrom nonmineralcarbon,accountsfor21%ofPM10(10μg/m3)showing a low variation along the study period, with a slightly higher concentration during the warm period (11μg/m3). In the study areacarbonaceousaerosolsmaybeemittedbyavarietyofsources suchasroadtrafficorsugarcaneburningepisodes,morefrequent form November to March after the harvest. In addition to these primary sources the formation of secondary organic aerosols should also be considered, that is probably enhanced during the warmerperiod. (5)Traceelements:Thecontributionoftraceelementswassimilar inthetwoperiods(0.3and0.4μg/m3).Theelementswithelevated average concentration (>20ng/m3) were P, Ti, V, Zn, Cu and Ba. LowlevelsofAs,SbandCdweremeasured,althoughthelevelof mosttraceelementsarerelativelyhighwhencomparedwithother rural background PM10 data using a similar methodology, for example Montseny, Spain (Pey et al., 2009) and Chaumont, Switzerland(Hueglinetal.,2005).Furthermore,levelsofNiandV are relatively high when compared with urban zones such as MexicoCity(Queroletal.,2008,byusingasimilarmethodology), Barcelona Metropolitan area (Querol et al., 2001) and Tocopilla, Chile(Jorquera,2009).Thisdifferentialpatternmayarisefromthe widespreadcombustionoffuel–oilinthestateofColima. 3.3.Sourceapportionment It is important to note that the number of possible factors from Principal Component Analysis are limited by the variance of theoriginalelements.Theanalysisyieldedtotheidentificationof thefactorsbelowthataccountedforacumulativevarianceof82% (Table4). Factor 1. This factor refers to “regional sources” (45% of the variance) and it is composed of crustal material resuspended soil dust and volcanic ashes (Fe, Ti, Al, Si), agricultural dust and the occurrence of forest fires (OM+EC, K, P, Sr, Fe, NO3–). Previous studies on the characterization of PM emission sources from Colimastate(Campos–Ramosetal.,2009),reportedthatFe,Tiand
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
414
40
Cold period
SO42- nss
Mineral 30
Warm period FV
WF
WF
µg/m
3
FV
FV
FV
20 10 0
3
µg/m
31-05-07
26-05-07
Warm period NO3-
FV
22-05-07
18-05-07
25-04-07
19-04-07
27-03-07
Cold period
8
23-03-07
18-03-07
17-01-07
11-01-07
25-11-06
22-11-06
18-11-06
15-11-06
13-11-06
09-11-06
02-11-06
12-10-06
10-10-06
06-10-06
02-10-06
10
FV
FV
NH4+
WF
WF
6 4
FV
2 0 31-05-07
26-05-07
22-05-07
18-05-07
25-04-07
19-04-07
27-03-07
23-03-07
18-03-07
17-01-07
Ͳ
11-01-07
25-11-06
22-11-06
Ͳ
18-11-06
15-11-06
13-11-06
09-11-06
02-11-06
12-10-06
10-10-06
06-10-06
02-10-06
2Ͳ
Ͳ
Figure3.LevelsofSIA(SO4 nss,NH4 Cl andNO3 )inPM10forthecoldandwarmperiods.Episodes simultaneouswithVolcandeFuegoexhalations(FV)andwildfires(WF)indicated.
A
y = 0,58x + 3,35 R2 = 0,42
300 200
+
3
NH4 (neq/m )
400
100 0 0
100
200
SO4
300
2-
nss
400
500
3
(neq/m )
400 300 200 y = 0,95x - 51,56 R2 = 0,78
+
3
NH4 (neq/m )
B
100 0 0
50
100
150
SO4
200 2-
250
nss (neq/m
2Ͳ
+
3
300
350
)
3
Figure4.CorrelationbetweenlevelsofSO4 nssandNH4 (inneq/m )byconsideringall thesamples(a)anddiscardingsamplessimultaneouswithvolcanicepisodes(b).
400
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
Table 4. Results of the factor analysis carried out with the daily concentrations of PM10 components. Weightings for each factor after Varimaxnormalization PM10 OM+EC Al Ca Fe K Mg Na Ba Cu Mn P Sr V As Se Cd Pb Ti Ni Zn Sb Zr 2_ SO4 ss 2_ SO4 nss _ NO3 _ Cl + NH4 Expl.Va Prp.Tot Eigenv Total
Factor1 0.81 0.70 0.97 0.56 0.90 0.92 0.99 0.87 0.29 Ͳ0.07 0.86 0.92 0.91 0.09 Ͳ0.07 0.18 0.27 0.10 0.96 0.14 0.07 0.16 0.62 0.87 Ͳ0.05 0.67 0.57 Ͳ0.01
Factor2 0.16 0.16 0.03 0.73 0.27 Ͳ0.08 0.06 Ͳ0.01 0.32 0.94 0.40 Ͳ0.05 0.27 Ͳ0.08 0.95 0.52 0.71 0.22 0.14 0.08 0.26 0.28 Ͳ0.18 0.00 Ͳ0.10 0.04 0.26 Ͳ0.01
Factor3 0.33 0.16 0.00 Ͳ0.11 0.05 0.14 0.03 0.03 0.36 Ͳ0.03 0.04 Ͳ0.04 Ͳ0.01 0.91 Ͳ0.03 Ͳ0.01 0.20 Ͳ0.11 Ͳ0.01 0.91 0.23 Ͳ0.12 0.03 0.02 0.71 0.07 0.26 0.30
Factor4 0.26 0.40 0.09 0.22 0.24 0.06 Ͳ0.05 Ͳ0.10 0.66 0.19 0.24 0.09 0.23 Ͳ0.14 0.22 0.43 0.48 0.71 0.11 0.11 0.83 0.75 0.33 Ͳ0.09 0.23 0.10 Ͳ0.38 0.06
Factor5 Ͳ0.15 0.12 0.08 0.15 0.02 0.14 Ͳ0.04 Ͳ0.30 0.06 0.03 0.05 0.07 0.06 Ͳ0.15 0.00 0.01 Ͳ0.13 Ͳ0.22 0.07 Ͳ0.04 0.12 Ͳ0.05 0.02 Ͳ0.30 Ͳ0.53 0.01 0.07 Ͳ0.84
11.34 0.40 12.69 45.32
3.93 0.14 4.64 16.57
2.76 0.10 3.05 10.90
3.54 0.13 1.57 5.60
1.39 0.05 1.01 3.61
Source
Regional
Industrial
FuelͲoil
Factor 4. This factor is constituted mainly by Pb, Zn, Ba, Sb and OM+EC(6%ofthevariance).ThesePMcomponentsaretracersof primary road traffic emissions (Schauer et al., 2006), probably relatedtoafreewaywithcontinuoustransitofvehiclesislocated at200mfromthemonitoringsite. Factor5.Representstheformationofsecondarysulfatefavoredby transport at the regional level. This is constituted mainly by NH4+ andnonseasaltSO42– (4%ofthevariance).Themajoroccurrence of (NH4)2SO4 was deduced during the entire sampling period by meansofionbalances.Theoriginofthiscomponentisrelatedto the high SO2 concentrations from sporadic volcanic exhalations, fossilfuelcombustionandindustrialemissions;andhighpotential NH3fromintenseagriculturalandfarmingactivities. Afteridentifyingthemainparticulatematteremissionsources affecting the sampling area, their mass contributions were determined based on a multiple linear regression analysis. The modeled and experimental results for PM10 levels showed a very 2 goodfit,withR of0.90. Figure 5 shows that the average regional contribution accounts for 49% of the PM10 mass (23μg/m3) owing to the high contribution of crustal material. The contribution of the industry reachedto6%ofthePM10mass(3μg/m3).Additionally,bothfuel– 3 oil combustion and road traffic contribute each with8 μg/m , accountingfor17–18%ofthePM10mass.Finally,thecontribution ofsecondarysulfateis10%ofthePM10mass(5μg/m3). Secondary; 4,6; 10%
Road traffic; 7,8; 17% Regional; 23,2; 49%
Fue-oil; 8,3; 18%
Road
Secondary
K, Na, Si, Al, O rich particles were related to the ilmenite and feldspar discrete particles, from volcanic ash, but these components are also typical from other crustal material, such as dust. Furthermore, carbonaceous Si, Al, Fe rich particles from burning of cultivation of cane were also frequently identified. Sodiumwasalsopresentinthisfactorwithalessercontribution.It isimportanttomentionthatCl–wasnotpresentinthisfactor.This maybecausedbythepossiblecrustalorigin(Na–feldpars)ofmot bulk (soluble + insoluble) Na or by the mixed source contribution (marine,volcanicandbiomassburning)ofCl–(Taranetal.,2002). Factor 2. This factor contains metal and metalloid elements, such asCu,As,Cd,CaandSe(17%ofthevariance)typicallyrelatedto anthropogenic emissions from thermoelectric and metal– metallurgical processes. The transport of such emissions to the studyareaisfavoredmainlybythesouthwesternwindsinwinter andspringperiods.Therearenomajorindustrialestatesemitting theseelementsnearbythemonitoringstation,sotheirpresenceis related to the transport at the regional level favored by the incoming air masses from the Pacific Ocean, but also a major volcanicorigincouldnotbediscarded. Factor3.ThisfactorisconstitutedbyNi,VandnonseasaltSO42– (11% of the variance), related to widespread fuel–oil combustion at regional level in diverse industries, such as mining, sugar cane processing,metallurgyandpowergeneration.
415
Industry; 3; 6% Figure 5. Average contribution of PM10 from main emission sources determinedbyPCA:regional,industrial,fuel–oil,roadtrafficandsecondary sulfateformation.
4.Conclusions The present study allowed the measurement and interpretation of the variability of PM10 and its different componentsinaruralbackgroundsite(CUICA)atColima,Mexico. Average PM10 levels reached to relatively high values (48μg/m3). Relatively high levels of crustal material (15μg/m3) carbonaceous aerosols (10μg/m3) and sulfate (9μg/m3) account for the increasedPM10levelsforaruralbackgroundsite. Dust resuspension, volcanic, industrial, agricultural, farming and road traffic emissions, as well as regional pollution were identified as the main drivers of this variability. Furthermore, meteorological factors at local, regional and synoptic scales influencedsuchPM10variability. A differentiation was found between the cold period, with average PM10 levels reaching to 42μg/m3, and the warm period, with 56μg/m3. The first study period was characterized by high levels of SO42–nss (19μg/m3) arising from both anthropogenic and
416
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
volcanicemissions,whereasthesecondischaracterizedbyahigh 3 contribution from crustal material (22μg/m ) from soil resuspensionandvolcanicash. Theoccurrenceofmosttraceelements(Pb,As,Cu,Zn,Cd,V, Ni, Cr, Sb,) arise from regional and long distance transport of diverseanthropogenicemissions.Theelementswithrelativelyhigh levelswhencomparedwithotherrural,urbanandindustrialareas wereNiandV.Thesourceapportionmentanalysisshowedthatthe highest contribution to PM10 levels arises from the regional pollution (49 %) followed by the industrial emissions, fuel oil combustion,roadtrafficandsecondarysulfate(18,17,10and6%, respectively). The results from the present study show a large anthropogenic load in rural background PM pollution, consequently, we propose to build am air quality monitoring networkinruralandurbanareasofthisregion.
Acknowledgments We thank Mexico’s Consejo Nacional de Ciencia y Tecnologia (CONACYT) the fellowship No. 177848 and SEMARNAT–CONACYT support through the project 2004–C01–48. We would like also to thank the Institute of Environmental Assessment and Water Research from CSIC in Barcelona, Spain for the laboratory and analytical support. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication (http://www.arl.noaa.gov/ready/hysplit4.html).
References AragonA.,CamposA.A.,LabradaG.J.,2007.Characterizationbyelectronic microscopy of particulate matter in different places of Mexico influenced by different pollutants emissions. European Aerosol Conference, Salzburg. http://www.gaef.de/eac2007/EAC2007 abstracts/T28Abstractpdf/T13A076.pdf.Accessedon:26August2009. AragonͲPina, A., CamposͲRamos, A.A., LeyvaͲRamos, R., HernandezͲOrta, M., MirandaͲOrtiz, N., LuszczewskiͲKudra, A., 2006. Influencia de emisionesindustrialesenelpolvoatmosfericodelaciudaddeSanLuis Potosi,Mexico.RevistaInternacionalDeContaminacionAmbiental22, 5Ͳ19. CamposͲRamos, A., AragonͲPina, A., GalindoͲEstrada, I., Querol, X., Alastuey,A.,2009.CharacterizationofatmosphericaerosolsbySEMin a rural area in the western part of Mexico and its relation with differentpollutionsources.AtmosphericEnvironment43,6159Ͳ6167. Drever, J.I., 1982. Geochemistry of Natural Waters. PrenticeͲHall Inc, EnglewoodCliffs,NJ,pp.388. Dockery, D.W., Pope, A.C., 1996. Epidemiology of acute health effects: Summary of timeͲseries studied. Spengler, J.D., Wilson, R. (Editors), Particles in Our Air: Concentration and Health Effects. Harvard UniversityPress,pp.123–147 Donaldson, K., Gilmour, M.I., MacNee, W., 2000. Asthma and PM10. RespiratoryResearch1,12Ͳ15. Donaldson,K.,McNee,W.,1998.Themechanismoflunginjurycausedby PM10.IssuesinEnvironmentalScienceandTechnology10,21Ͳ32. Draxler,R.R.,Rolph,G.D.,2003.HYSPLIT(HYbridSingleͲParticleLagrangian Integrated Trajectory) Model access via NOAA ARL READY, NOAA Air Resources Laboratory, Silver Spring, MD. http://www.arl.noaa.gov/ ready/hysplit4.html. Galindo,I.,Dominguez,T.,2002.NearrealͲtimesatellitemonitoringduring the 1997Ͳ2000 activity of Volcan de Colima (Mexico) and its relationship with seismic monitoring. Journal of Volcanology and GeothermalResearch117,91Ͳ104. GalindoI.,CortesA.,DominguezT.,GavilanesJ.C.,CruzM.,1999.Notesur l'eruptionactuelleduVolcandeFuegodeColima(Mexique).Bulletinde laSocietedeVolcanologieGeneve,1Ͳ6.
Galindo, I., Elizalde, R. Solano, Cruz, M., 1998. Climatología del Volcan de Fuego de Colima (Climatology of the Fire Volcano of Colima), UniversidaddeColima,73pp. Grantz, D.A., Garner, J.H.B., Johnson, D.W., 2003. Ecological effects of particulatematter.EnvironmentInternational29,213Ͳ239. Harrison,R.M.,Pio,C.A.,1983.SizeͲdifferentiatedcompositionofinorganic atmospheric aerosols of both marine and polluted continental origin. AtmosphericEnvironment17,1733Ͳ1738. Hays,M.D.,Fine,P.M.,Geron,C.D.,Kleeman,M.J.,Gullett,B.K.,2005.Open burning of agricultural biomass: physical and chemical properties of particleͲphaseemissions.AtmosphericEnvironment39,6747Ͳ6764. Henry, R.C., Lewis, C.W., Hopke, P.K., Williamson, H.J., 1984. Review of receptor model fundamentals. Atmospheric Environment 18, 1507Ͳ 1515. Hopke,P.K.,Song,X.H.,1997.Thechemicalmassbalanceasamultivariate calibrationproblem.ChemometricsandIntelligentLaboratorySystems 37,5Ͳ14. Hopke,P.K.,2000.AGuidetoPositiveMatrixFactorization.Departmentof Chemistry,ClarksonUniversity,PotsdamNY. Hopke, P.K., Cohen, D.D., 2011. Editorial: Special Issue on Application of ReceptorModels.AtmosphericPollutionResearch2,121Ͳ121. Hueglin,C.,Gehrig,R.,Baltensperger,U.,Gysel,M.,Monn,C.,Vonmont,H., 2005.ChemicalcharacterisationofPM2.5,PM10andcoarseparticlesat urban, near city and rural sites in Switzerland. Atmospheric Environment39,637Ͳ651. INEGI, 2005. Instituto Nacional de Estadistica Geografia e Informatica (National Institute of Statistical, Geography and Computer Science), (Synthesis of Geographic information from the State of Colima, Mexico), http://www.inegi.org.mx/inegi/default.aspx., access: 20 August2009. Jorquera, H., 2009. Source apportionment of PM10 and PM2.5 at Tocopilla, Chile (22°05'S, 70°12'W). Environmental Monitoring and Assessment 153,235Ͳ251. Kelly, V.R., Lovett, G.M., Weathers, K.C., Likens, G.E., 2005. Trends in atmospheric ammonium concentrations in relation to atmospheric sulfateandlocalagriculture.EnvironmentalPollution135,363Ͳ369. Lee,S.L..,Wong,W.H.S.,Lau,Y.L.,2006.Associationbetweenairpollution and asthma admission among children in Hong Kong. Clinical and ExperimentalAllergy36,1138Ͳ1146. Miranda,J.,Zepeda,F.,Galindo,I.,2004.Thepossibleinfluenceofvolcanic emissions on atmospheric aerosols in the city of Colima, Mexico. EnvironmentalPollution127,271Ͳ279. NOMͲ035ͲECOLͲ1993 (Mexican Regulation). Methods of Measurement to Determine the Total Suspended Particle Concentration in the Air and the Procedure for the Calibration of the Measurement Equipment.http://www.semarnat.gob.mx/leyesynormas/Normas%20O ficiales%20Mexicanas%20vigentes/CNVͲ035.pdf, Accessed on: 24 August2009. Pey, J., Perez, N., Castillo, S., Viana, M., Moreno, T., Pandolfi, M., LopezͲ SebastianJ.M.,Alastuey,A.,Querol,X.,2009.Geochemistryofregional background aerosols in the Western Mediterranean. Atmospheric Research94,422–435. Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D., 2002. Lung cancer, cardiopulmonary mortality, and longͲterm exposure to fine particulate air pollution. JAMAͲJournal of theAmericanMedicalAssociation287,1132Ͳ1141. Pope, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. Journal of the Air and Waste ManagementAssociation56,709Ͳ742. Querol, X., Pey, J., Minguillon, M.C., Perez, N., Alastuey, A., Viana, M., Moreno,T.,Bernabe,R.M.,Blanco,S.,Cardenas,B.,Vega,E.,Sosa,G., Escalona,S.,Ruiz,H.,Artinano,B.,2008.PMspeciationandsourcesin Mexico during the MILAGROͲ2006 Campaign. Atmospheric Chemistry andPhysics8,111Ͳ128.
CamposͲRamosetal.–AtmosphericPollutionResearch2(2011)409Ͳ417
417
Querol,X.,Alastuey,A.,Viana,M.M.,Rodriguez,S.,Artinano,B.,Salvador, P.,doSantos,S.G.,Patier,R.F.,Ruiz,C.R.,delaRosa,J.,delaCampa, A.S., Menendez, M., Gil, J.I., 2004. Speciation and origin of PM10 and PM2.5inSpain.JournalofAerosolScience35,1151Ͳ1172.
USEPA,2006.(EnvironmentalProtectionagency).ProvisionalAssessmentof Recent Studies on Health Effects of Particulate Matter Exposure, NationalCenterforEnvironmentalAssessment,ResearchTrianglePark, NC27711.EPA/600/RͲ06/063.
Querol, X., Alastuey, A., Rodriguez, S., Plana, F., Ruiz, C.R., Cots, N., Massague,G.,Puig,O.,2001.PM 10andPM2.5sourceapportionmentin the Barcelona metropolitan area, Catalonia, Spain. Atmospheric Environment35,6407Ͳ6419.
Vega, E., Eidels, S., Ruiz, H., LopezͲVeneroni, D., Sosa, G., Gonzalez, E., Gasca,J.,Mora,V.,Reyes,E.,SanchezͲReyna,G.,Villasenor,R.,Chow, J.C., Watson, J.G., Edgerton, S.A., 2010. Particulate air pollution in MexicoCity:adetailedview.AerosolandAirQualityResearch10,193Ͳ 211.
Schauer J.J., Lough G.C., Shafer M.M., Christensen W.F., Arndt M.F., De Minter J.T., Park J.S., 2006. Characterization of metals emitted from motorvehicles.HealthEffectsInstitute133,1Ͳ76. Taran, Y., Gavilanes, J.C., Cortes, A., 2002. Chemical and isotopic composition of fumarolic gases and the SO2 flux from Volcan de Colima, Mexico, between the 1994 and 1998 eruptions. Journal of VolcanologyandGeothermalResearch117,105Ͳ119. Thurston, G.D., Spengler, J.D., 1985a. A multivariate assessment of meteorologicalinfluencesoninhalableparticlesourceimpacts.Journal ofClimateandAppliedMeteorology24,1245Ͳ1256. Thurston,G.D.,Spengler,J.D.,1985b.Aquantitativeassessmentofsource contributionstoinhalableparticulatematterpollutioninmetropolitan Boston.AtmosphericEnvironment19,9Ͳ25. Turpin, B.J., Saxena, P., Andrews, E., 2000. Measuring and simulating particulate organics in the atmosphere: problems and prospects. AtmosphericEnvironment34,2983Ͳ3013.
Vega,E.,Lowenthal,D.,Ruiz,H.,Reyes,E.,Watson,J.G.,Chow,J.C.,Viana, M., Querol, X., Alastuey, A., 2009. Fine particle receptor modeling in the atmosphere of Mexico City. Journal of the Air and Waste ManagementAssociation59,1417Ͳ1428. Viana,M.,Kuhlbusch,T.A.J.,Querol,X.,Alastuey,A.,Harrison,R.M.,Hopke, P.K., Winiwarter, W., Vallius, A., Szidat, S., Prevot, A.S.H., Hueglin, C., Bloemen, H., Wahlin, P., Vecchi, R., Miranda, A.I., KasperͲGiebl, A., Maenhaut, W., Hitzenberger, R., 2008. Source apportionment of particulatematterinEurope:areviewofmethodsandresults.Journal ofAerosolScience39,827Ͳ849. Walker, J.T., Robarge, W.P., Shendrikar, A., Kimball, H., 2006. Inorganic PM2.5ataU.S.agriculturalsite.EnvironmentalPollution139,258Ͳ271. WHO,2005.Healthaspectsofairpollutionwithparticulatematter,ozone andnitrogendioxide.WorldHealthOrganization,98pp.