Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest

19 Pages·2017·5.52 MB·English

Atmos.Chem.Phys.,17,7977–7995,2017
https://doi.org/10.5194/acp-17-7977-2017
©Author(s)2017.Thisworkisdistributedunder
theCreativeCommonsAttribution3.0License.
Contributions of mobile, stationary and biogenic sources to air
pollution in the Amazon rainforest: a numerical study with the
WRF-Chem model
SamehA.AbouRafee1,LeilaD.Martins1,AnaB.Kawashima1,DanielaS.Almeida1,MarcosV.B.Morais1,
RitaV.A.Souza2,MariaB.L.Oliveira2,RodrigoA.F.Souza2,AdanS.S.Medeiros2,VivianaUrbina3,
EdmilsonD.Freitas3,ScotT.Martin4,andJorgeA.Martins1
1FederalUniversityofTechnology–Parana,Londrina,Brazil
2AmazonasStateUniversity–Amazonas,Manaus,Brazil
3DepartmentofAtmosphericSciences,UniversityofSãoPaulo,SãoPaulo,Brazil
4HarvardUniversity,Cambridge,Massachusetts,USA
Correspondenceto:SamehA.AbouRafee([email protected])andJorgeA.Martins([email protected])
Received:31December2016–Discussionstarted:10January2017
Revised:4May2017–Accepted:31May2017–Published:30June2017
Abstract.Thispaperevaluatesthecontributionsoftheemis- 1 Introduction
sions from mobile, stationary and biogenic sources on air
pollutionintheAmazonrainforestbyusingtheWeatherRe- The impacts of anthropogenic emissions from urban areas
searchandForecastingwithChemistry(WRF-Chem)model. on the environment and human health have been covered
TheanalyzedairpollutantswereCO,NO ,SO ,O ,PM , inseveral scientificstudiesthrough differentmethodologies
x 2 3 2.5
PM andvolatileorganiccompounds(VOCs).Fivescenar- (Kampa and Castanas, 2008; Martins et al., 2010). These
10
ios were defined in order to evaluate the emissions by bio- analyses have shown that the consequences of such emis-
genic,mobileandstationarysources,aswellasafuturesce- sions go beyond their initial source and can reach even far-
nario to assess the potential air quality impact of doubled therduringlongperiods.Ingeneral,mostrecentstudiesfo-
anthropogenicemissions.Thestationarysourcesexplainthe cusmainlyontheaftermathoftheanthropogenicemissions
highestconcentrationsforallairpollutantsevaluated,except fromthemegacitiesinthedevelopingworld(e.g.,Zhuetal.,
forCO,forwhichthemobilesourcesarepredominant.The 2013).Thishashappenedbecausesuchcitieshaveincreased
anthropogenic sources considered resulted an increasing in theirenergydemand,whichhasbeenhighthroughoutrecent
the spatial peak-temporal average concentrations of pollu- decades (Lutz et al., 2001). For instance, energy consump-
tantsin3to2780timesinrelationtothosewithonlybiogenic tion in China has increased more than 300 million tons of
sources.Thefuturescenarioshowedanincreaseintherange coalequivalent(MtCE),comparedtothepreviouslyamount
of 3 to 62% in average concentrations and 45 to 109% in of 1349MtCE. Most of this energy is produced from burn-
peakconcentrationsdependingonthepollutant.Inaddition, ing fossil fuels, mainly coal (Crompton and Wu, 2005). As
the spatial distributions of the scenarios has shown that the a consequence, the atmosphere in these regions has experi-
airpollutionplumefromthecityofManausispredominantly encedagreatincreasenotonlyinparticlesbutalsoingases
transportedwestandsouthwest,anditcanreachhundredsof suchascarbonmonoxide(CO),sulfurdioxide(SO2),nitro-
kilometersinlength. gen oxides (NOx), and other secondary compounds such as
ozone(O ).Understandingthepredominantsourcesofeach
3
pollutantiskeytodesigningsuccessfulregulatorypoliciesto
improveairqualityandbringbenefitstopublichealth.
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
7978 S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution
Determining what is the real contribution of the anthro- individual contributions from the main mobile and station-
pogenic air pollutants to the environment and to human ary sources in Manaus. In addition, a companion study by
health is not a straightforward task. Its difficulty lies in Medeiros et al. (2017) focuses on how the changing energy
separately studying the emissions from natural and human matrixforpowerproductionaffectsairqualityintheManaus
sourcesandtheircomplexcombination.Althoughurbanar- region. Both studies were conducted using the Weather Re-
eascanhaveanidentityassociatedwiththeireconomicpro- searchandForecastingwithChemistry(WRF-Chem)model,
file,thetransportandinteractionsofatmosphericpollutants wherethesimulationsfordiversescenarioswereperformed
effectshampertheunderstandingoftheroleofeachemission according to the current conditions of the region. The nu-
species in the studied region. In this context, atmospheric merical experiments performed in this work addressed the
modelswithanexplicittreatmentofthephysicalandchem- followingissues:
ical processes become an indispensable tool to the studies
– participation of mobile and stationary anthropogenic
oftheurbanpollutionimpact.Severalnumericalstudiescan
sources and biogenic sources in the concentration of
be found with special focus on different aspects of the pol-
tracegasesandparticlesforthecityofManausandad-
lutionimpact,suchaspollutionepisodes(Jiangetal.,2012;
jacentareasdirectlyinfluencedbyit;
Michael et al., 2013; Kuik et al., 2015), regional and long-
distance transport (Tie et al., 2007; Guo et al., 2009; Lin et – preferential direction and the distance of the urban
al., 2010), secondary formation of gases and particles (Yer- plumeimpactfromManausintheAmazonregion;
ramileetal.,2010;Jiangetal.,2012;Loweetal.,2015),and
– impact on air quality due to the likely future increase
the effects of land use and land cover changes (Capucim et
inanthropogenicemissionsfrommobileandstationary
al.,2015;Rafeeetal.,2015).However,suchstudiesdidnot
sources.
investigatetheimpactofisolatedurbanplumes.
Thepronouncedgrowthofurbanareasinrecentdecades,
mainlyduetotheacceleratedpopulationgrowthrateandthe
2 Methodology
migratoryflowtocities,resultedinafewareasintheworld
whereanisolatedurbanareawouldinteractwithanenviron- 2.1 Areaandperiodofstudy
mentofnaturalandhomogeneouscharacteristics.Thecityof
Manausisoneoftheuniquescenariosintheworldwherean The region of the study comprises the urban area of Man-
isolated urban area is in the center of a vast tropical forest aus and its surroundings, with a total area of 230560km2
area, the Amazon rainforest (Martin et al., 2016). Thus, the (Fig. 1). Manaus is located in northern Brazil, at latitude
regionisavaluablelaboratory,forexampleforstudyingthe 3◦06(cid:48)07(cid:48)(cid:48) and longitude 60◦01(cid:48)30(cid:48)(cid:48) with an urban area of
impactofanthropogenicemissionsofairpollutantsonatmo- 377km2,presentedinthecenterofthesimulatedgrid.Cur-
sphericchemicalcomposition.Althoughthereareanumber rently,Manaushasanestimatedpopulationof2millionpeo-
of studies involving the measurements of atmospheric pol- ple, representing more than 50% of the population in the
lutantsintheAmazonregion(e.g.,Kuhnetal.,2010;Trebs state of Amazonas, with 99.49% of its population living in
etal.,2012;Baarsetal.,2012;Artaxoetal.,2013),mostof urbanareas(IBGE,2014).
thecontributionwaspublishedveryrecentlywiththeresults The simulation period encompasses the dates of 16–
of the GoAmazon2014/5 project (Martin et al., 2016, 2017; 18 March 2014, and this period is part of the rainy season
Alves et al., 2016; Pöhlker et al., 2016; de Sá et al., 2017; intheregion(Fischetal.,1998).Duetothescarceavailabil-
Kourtchevetal.,2016;Huetal.,2016;Batemanetal.,2017; ityofairqualitydata,thechoiceofthedaysforsimulations
Cecchini et al., 2016; Jardine et al., 2016). In terms of nu- was made according to that availability, which is necessary
merical studies, only a few studies focusing the role of the toevaluatetheperformanceofthemodelwiththeobserved
anthropogenicemissionsourcesinurbanareasareavailable ground-based data. Four measurement sites of meteorologi-
(Freitasetal.,2006;Andreaeetal.,2012;Becketal.,2013; calvariablesandairpollutantsconcentrationswereavailable
Belaetal.,2015).Thesestudiesdonotaddresstheparticipa- indifferentlocationsinthestudyarea,aspresentedinFig.1:
tionofeachtypeofemissionsourceonairpollutants,which T1, located within the city of Manaus, at the National In-
isimportantfortheformulationofregulatorypublicpolicies stitute for Amazonian Research (INPA); T3, located in the
forairqualitymanagement.Thistypeofevaluationcanonly north of Manacapuru, approximately 100km from Manaus,
be performed with the use of atmospheric modeling tools, at stations of the project Green Ocean Amazon (GoAma-
whichrequirethepreparationofaninventoryformobileand zon2014/5); Colégio Militar and Federal Institute of Ama-
stationarysources,sincenoofficialandpublicemissionsin- zonas (IFAM), located in the city of Manaus; and a fourth
ventoryisavailableforManaus. siteassociatedwiththeProjectREMCLAMNetworkofCli-
Given the importance of anthropogenic emissions in the mate Change Amazon from the University of the State of
Amazon region, this paper reports a numerical study evalu- Amazonas (UEA). According to the data from stations lo-
ating the impact of the urban pollution plume on pollutant cated in the city of Manaus, there was no record of rain-
concentrationsabovethepreservedforestregionconsidering fall within the given period, whereas the Manacapuru site
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S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution 7979
Figure1.Geographiclocationofthestudyarea,meteorologystations(ColégioMilitar,IFAMandT3)andairqualitystation(T1).Thewhite
contourlineshowsthedelimitationofthecityofManaus.
recorded 7.2mm rainfall. Regarding the predominant wind Table1.WRF-Chemphysicsparametrizationsusedforthisstudy.
direction, the ground-based data observed a prevalence of
winds from the north, northeast and east. Figure S1 of the Process Reference
Supplement shows a time evolution of streamlines and the
Microphysics MilbrandtandYau(2005)
potential temperature advection, where these atmospheric Surfacelayer MM5(ZhangandAnthes,1982)
conditions can be seen. In terms of wind direction clima- Soil–landparameterization NoahLSM(ChenandDudhia,2001)
tology, the prevailing northeasterly/westerly winds blow all Boundarylayer YSU(HongandDudhia,2003)
year round, 10–60◦ azimuth (November to March) and 90– Shortwaveradiation Dudhia(Dudhia,1989)
Longwaveradiation RRTM(Mlaweretal.,1997)
130◦ azimuth(MaytoSeptember)(Araújoetal.,2002).An-
otherimportantfactorwhenchoosingtheperiodofstudywas
the low incidence of biomass burning wildfires in the rainy
season.FireoutbreaksintheAmazonregionarepredominant
duringthedryseasonandcanthereforeaffecttheconcentra-
Vara-Vela et al., 2016). Furthermore, some combinations of
tionofparticulatematterandtracegases(AndreaeandMer-
physical parametrizations were tested and the combination
let, 2001; Martins and Silva Dias, 2009). Since the purpose
thatbestrepresentedthemeteorologicalparametersobserved
isonlytoanalyzetheimpactofanthropogenicemissionsat-
(temperatureandrelativehumidity)wasused.
tributedtofossilfuels,periodswiththepresenceofregional
For the biogenic emission, the module used was based
wildfireswouldnotbeideal.
on descriptions by Guenther et al. (1993, 1994), Simpson
et al. (1995) and Schoenemeyer et al. (2007). This mod-
2.2 Modeldescription uledealswithisoprene,monoterpenes,volatileorganiccom-
pound(VOC)emissionsandemissionsofnitrogenfromthe
Inthisstudy,thecoupledWRF-Chem(WeatherResearchand soil.Biogenicemissionswerecalculatedusingthecategories
Forecasting with Chemistry; Grell et al., 2005) model, ver- of land use available in the model in which emission rates
sion 3.2, was applied. The model is an online system that are estimated from the temperature and photosynthetic ac-
simultaneouslypredictsmeteorologicalandchemicalstates. tive radiation, which is the fraction of solar radiation com-
WRF/Chem or its previous versions have been applied in a prised in the range of the visible spectrum available (0.4 to
large number of studies worldwide (e.g., Grell et al., 2005; 0.7µm) for the photosynthesis process. The aerosol param-
Wang et al., 2009, 2010; Tuccella et al., 2012; Vara-Vela et eterizationusedwasbasedontheModalAerosolDynamics
al.,2015).Thephysicaloptions(Table3)usedweredefined model for Europe (MADE; Ackermann et al., 1998), devel-
accordingtotherecentoptionsinsertedinthemodel,aswell opedfromtheRegionalParticulateModel(RPM;Binkowski
as those already tested in simulations by other works (e.g., andShandar,1995),embeddedwiththerepresentationofor-
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7980 S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution
ganic aerosol from the Secondary Organic Aerosol Model 2.3 Anthropogenicemissioninventories
(SORGAM,developedbySchelletal.,2001).
For the gas-phase chemistry, the chemical mechanism 2.3.1 Vehicleemissions
used was the Regional Acid Deposition Model version 2
(RADM2; Chang et al., 1989), originally developed by Emissions of all classes of light-duty and heavy-duty vehi-
Stockwell et al. (1990). RADM2 is widely used in atmo- clesinthestudyregionweretakenintoconsiderationformo-
spheric models to predict concentrations of oxidants and bilesources.Inordertocalculatetheemissionsofairpollu-
other pollutants and contains 158 chemical reactions, of tants,informationwascollectedbasedontheestimateofthe
which21arerelatedtophotolysis. number and type of vehicles, emission factors and average
The simulation domain was configured with a 3km hor- vehicle-use intensity. The calculations included individual
izontal grid spacing, with 190 grid points in x and 136 contributionsoffivetypes/fuelcombinationsofvehicles(Ta-
grid points in y directions, centered in the city of Manaus ble2),consideringthedataestimatedfromtheBrazilianNa-
(3◦4(cid:48)12(cid:48)(cid:48)S,59◦59(cid:48)24(cid:48)(cid:48)W).Intheverticalgrid,35levelswere tionalDepartmentofTraffic(DENATRAN,2014)forthecity
defined, with the top of the model at 50hPa, corresponding of Manaus. The urban area of Manaus contains 83.22% of
to about 20km in height above the mean sea level. Analy- thevehiclesinthestateofAmazonas,correspondingtoover
sis data from the Global Model Data Assimilation System 600000 vehicles in the current fleet. Therefore, the fraction
(GDAS), with a horizontal grid spacing of 1◦ and 26 verti- of the vehicle fleet according to type and fuel consumption
cal levels were used for initial and boundary conditions of fortheentirestudygridwasconsideredbasedondatafrom
themeteorologicalvariables.Forchemicalvariables,theini- Manaus. The fractions corresponding to light-duty vehicles
tialandboundaryconditionsofsimulationsconsistofideal- usinggasohol(amixtureofgasolineandethanolrangingbe-
ized,northernhemispheric,mid-latitude,cleanenvironmen- tween 20 and 25% of anhydrous ethanol), ethanol and flex
tal conditions as described in Liu et al. (1996) and applied fuelrepresent22.81,2.49and42.29%,respectively.Heavy-
in studies such as those conducted by Grell et al. (2005), dutydiesel-poweredvehiclesrepresent8.8%,andmotorcy-
Wang et al. (2009, 2010), Tuccella et al. (2012) and Vara- clesusinggasoholrepresent23.61%.
Velaetal.(2016).Ontheotherhand,therearestudieswhere The emission factors for different vehicle types/fuel as-
thechemicalcompoundprofilesusedasinitialandboundary sumed in the calculations were based on experiments con-
conditions were based on anthropogenic emission invento- ductedinsidetheroadtraffictunnelsinthecityofSãoPaulo
ries,includingCO,NO ,SO ,speciatedVOC,blackcarbon (Martinsetal.,2006;Sánchez-Ccoylloetal.,2009;Britoet
x 2
(BC), organic carbon (OC), and particulate material (PM), al., 2013), which are the only vehicle emission factor mea-
at0.5◦×0.5◦ (e.g.,Zhangetal.,2009)orhigherresolutions surements available in Brazil. The values were adopted for
(e.g., Tie et al., 2010). Initial and boundary conditions for CO, NO , SO , and particulate matter (PM) as well as the
x 2
chemical species have also been extracted from the output distribution of VOC emission fractions related to evapora-
of global chemical transport models, such as the Model for tive,liquidandexhaustemissionsforvehiclesburninggaso-
OZoneAndRelatedchemicalTracers(MOZART;Huetal., hol,ethanolanddiesel.Thesevaluescorrespondtothosethat
2010).However,itisimportanttoevaluatetheglobalmodel havebeenrecentlyusedinthestudybyAndradeetal.(2015)
results before deciding which global model to use in order and are listed in Table 2. Based on these emission factors,
toprovidethechemicalinitialandboundaryconditions,con- diurnalprofilesusinghourlyvariationsforemissionsoftrace
sidering that there are many uncertainties among the global gasesandPMwereusedinthemodel(Martinsetal.,2006;
results. Andrade et al., 2015). The distribution of the fractionation
Different times to avoid a spin-up effect are suggested in of emission of fine particulate matter, as well as the chem-
the literature, with values of 12 (Tuccella et al., 2012; Car- ical characterization, was obtained based on several studies
valho et al., 2015), 24 (Wang et al., 2009), 36 (Hu et al., inthecityofSãoPauloforthemeasurementsoftheconcen-
2010), and 48h (Tie et al., 2010) of simulation as a model tration mass and number of particulate matter (Ynoue and
spin-up. There are also studies considering a few days as Andrade, 2004; Miranda and Andrade, 2005; Albuquerque
a model spin-up (e.g., Wang et al., 2010). In this study, etal.,2011),andareusedintheinvestigationsbyAndradeet
a 24h spin-up time was considered. For land use, spatial al.(2015)andVara-Velaetal.(2015).
datafromtheModerate-resolutionImagingSpectroradiome- Inordertocalculatethevehicle-useintensity,referencees-
ter(MODIS)2005filewereutilized,with500mgridspacing timatesfromthefirstBrazilianNationalEmissionsInventory
(Schneideretal.,2009). ofRoadMotorVehicles(MMA,2011),DENATRANandthe
BrazilianNationalAgencyofPetroleum(ANP,2014a)were
used. The intensities for light-duty vehicles, heavy-duty ve-
hiclesandmotorcyclesare21.3,128.4and70.6kmperday,
respectively.
The spatial distribution of the number of vehicles in
each grid point was based on nighttime lights from
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S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution 7981
Table2.EmissionfactorsforCO,NOx,PM,SO2,andVOCs,ingramsperkilometer,fordifferentvehicletype–fuelcombinations.
Vehicletype Fuelburned CO NOx PM SO2 VOCs VOCs VOCs
(evaporative) (liquid) (exhaust)
Light-dutyvehicles Gasohol 5.43 0.34 0.15 0.03 0.17 2.00 1.24
Ethanol 12.00 1.12 0.15 0.01 0.04 1.50 2.12
Flex 5.13 0.32 0.15 0.02 – – 0.15
Heavy-dutyvehicles Diesel 4.95 9.81 0.44 0.61 – 0 2.48
Motorcycles Gasohol 9.15 0.13 0.05 0.01 – 1.40 2.37
the Defense Meteorological Satellite Program’s Oper- erationinthestateofAmazonas.TheotherTPPslocatedin
ational Linescan System (http://ngdc.noaa.gov/eog/dmsp/ stateareverysmall.
downloadV4composites.html),consideringthesizeofurban Due to several types of technologies used in the burning
occupation. Martins et al. (2008) have described this ap- fuels of TPPs, the emission factor admitted in the calcu-
proach,whichhasperformedaluminancecalibrationaccord- lation was the intermediate values between the lower and
ing to the population density and number of vehicles, pre- upper limits adopted by the US Environmental Protection
sentingagoodcorrelationbetweentheparameters. Agency (EPA, 1998, 2010), which has a complete estima-
tion of emission factor for all air pollutants simulated. The
2.3.2 Stationarysourceemissions emission factors are described in Table 3, and for compar-
ative purposes, the values adopted in the inventory of São
The emission of thermal power plants (TPPs) of different
Paulo Environmental Protection Agency (CETESB, 2009)
types of fuel burning have been considered for the inven-
havealsobeenlisted.ThedistributionofPMhasbeendone
tory of stationary sources in the studied region and the re-
based on the fractionation designed for vehicle emissions.
finery located within the urban area of Manaus. It is note-
Inaddition,thespeciationofVOCemissionshavebeenper-
worthythatthecontributionsoftheindustrieslocatedwithin
formed for each TPP fuel type estimated by EPA (1998,
the urban area have little significance in the region, due to
2010). Regarding the average fuel consumption for each
theproductionconcentratedmainlyintransportandcommu-
type of TPP, the average value of fuel to produce 1kWh of
nicationareassuchas electronics,metalmechanicalsectors
powerhasbeenadoptedasdescribedintheannualreportof
andtheproductionofmotorcycles(MANAUS,2002).Inthis
isolated operation systems for the North region (ELETRO-
case,thecontributionsonanthropogenicemissionsoccurin-
BRAS, 2013). For fuel oil, diesel and natural gas, the val-
directly by the high consumption of electricity supplied by ues of 0.27LkWh−1, 0.29kgkWh−1 and 0.28m3kWh−1,
theTPPs.TheemissionofthepollutantspertypeofTPPin
respectively,havebeenconsidered.
eachgridpointofstudyhasbeencalculatedaccordingtothe
Some approximations that were assumed to obtain the
estimates of emission factors, fuel consumption and power
emissionfactorvaluesofTable3wereasfollows:
generation,usingEq.(1):
– Fuel with 1% sulfur content was assumed to calculate
N
E =XEF ×FC×PG , (1) theemissionfactorofSO2.
p(i,j) P (i,j,k)
k=1 – Totalorganiccompounds(TOCs)includeVOCs,semi-
volatile organic compounds, and condensable organic
whereE representstheemissionofpollutantP ateach
p(i,j)
gridpoint(i,j),ingramsperhour(gh−1);EF istheemis- compounds.
P
sion factor of pollutant P, in grams per liter (gL−1); FC is
– For NO emissions by fuel oil power plants, the mini-
thefuelconsumption,inlitersperkilowatthour(LkWh−1); x
mumvaluewasattributedtothelowestvalueavailable
andPG(i,j,k) isthepowergenerationofTPP(k)ateachgrid for vapor generation above 50th−1 and the maximum
point(i,j)minkilowatt(kW).
value was assigned to the greatest value available for
According to the Generation Database (BIG) of the Na- plantsbelow50th−1.
tional Electric Energy Agency (ANEEL, 2014), Brazil has
1890 TPPs in operation, with a total installed capacity of – FortheemissionfactorsattributedtoSO andPM,the
2
about37.8GW.BasedontheinformationcontainedinBIG, minimumandmaximumvaluesweredefinedbasedon
aspatialdistributionofTPPshasbeenperformedateachgrid thegradeoffuelburned.Inthiscase,thecombustionof
point of the study area, with 59 diesel, 6 fuel oil and 8 nat- lighter distillate oils results in significantly lower PM
ural gas power plants (Fig. 2) found in the grid, generating formation than those from the combustion of heavier
a power of 1851MW, corresponding to 96.76% of all gen- residualoils.AccordingtothevaluesproposedbyEPA,
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7982 S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution
Figure 2. Spatial distribution of diesel (yellow), fuel oil (red) and natural gas (blue) TPPs on the study grid, and the border of Manaus
(white).
burningno.4orno.5oilusuallyproduceslessSO and the approximate sums of emissions in the study grid were
2
PMthantheburningofheavierno.6oil. 0.011Tgyr−1 (versus 0.068 in this study), 0.003Tgyr−1
(versus 0.087 in this study), 0.002Tgyr−1 (versus 0.073 in
Brazil has 17 petroleum refining units with a production of
thisstudy)forCO,NO andSO ,respectively.Forexample,
about 769millionm3 of petroleum per year (ANP, 2014b), x 2
Fig. 3 shows the contribution, in percent, of NO and SO
x 2
withonlyoneunitlocatedintheNorthregion,onthebanks
emissionsfromallanthropogenicsectorsconsidered.
oftheRioNegroinManaus(locatedatlatitudeλ=3◦08(cid:48)47(cid:48)(cid:48)
and longitude ϕ=59◦57(cid:48)15(cid:48)(cid:48)) – the Issac Sabbá refinery 2.4 Numericalscenarios
(REMAN). The total emission of each pollutant of the re-
fineryisgivenbyEq.(2): The investigations were defined by five numerical experi-
ments (Table 4) to the study of the influence of mobile and
E =EP ×V, (2)
p(i,j) p stationaryemissionsintheregion,whichareasfollows:
where Ep(i,j) represents the emission of the pollutant P, in – ScenarioC0:consideringthemainsourcesintheregion
grams per hour (gh−1), EPp is the emission factor of pol- (biogenicnaturalemission,vehicle,TPPsandREMAN
lutant P in grams per liter (gL−1) and V is the volume of refinery)representingtheemissioninventoryofcurrent
refinedpetroleuminlitersperhour(Lh−1). conditionsoftheregion.
According to the National Petroleum Agency (ANP,
2014b), REMAN has as average production of approxi- – ScenarioC1:numericalexperimentwithonlybiogenic
mately1.7×106Lofpetroleumperhour.Theemissionfac- naturalemission,whichsimulatesanatmosphericcon-
tors of the pollutants have been admitted from Presidente ditionofanenvironmentwithouttheinterferenceofhu-
BernardesRefinary(RPBC),locatedinCubatão,SãoPaulo, manactivities.
Brazil.TheVOCspeciationprofilesusedhavealsobeenob-
– Scenario C2: simulation with natural and vehicular
tainedfromtheEPA(1990).
emissions, intended to evaluate how the anthropogenic
ThetotalemissionsforCO,NO ,SO PMandVOCsfor
x 2, emissionscharacterizedonsolelyvehicularsourcesaf-
thestationaryandmobilesourcesthatrepresenttheC0sce-
fecttheatmosphericchemistry.
narioareshowninTableS1intheSupplement.Theseresults
demonstrate that global inventories underestimate mobile – ScenarioC3:consideringnaturalandTPPsandrefinery
andstationarysourcesinManaus.Forinstance,intheMAC- anthropogenicemissions,aimedatassessingtheimpact
City anthropogenic emissions inventory (MACCity, 2014), ofstationarysourcesonairpollutantemissions.
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S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution 7983
Table3.EmissionfactorsofTPPsperfueltype.
Fueltype Naturalgas (g106L−1) Fueloil (gL−1) Diesel (gL−1)
Pollutant EPA CETESB EPA CETESB EPA CETESB
CO 384–1568 – 0.6 – 4.9–2.4 –
PM 121.6 48–219 0.05–1.2 0.84–1.45 0.59–1.6 0.24
NOx 512–4480 2240–8800 1.2–6.6 6.6–8 18.3–54.1 2.4
SO2 9.6 9.6 17–18.8 18.2–19.2 5.8–18.2 17.2
TOCs 176 28–92 0.03–0.3 0.03–0.13 0.04–1.6 0.03
Source:EPA,AP-42(1998,2010);CETESB(2009).
Figure3.NOx andSO2emissioncontributionfromallanthropogenicsectorsconsidered.
– ScenarioC4:simulationincludingnaturalandtwicethe 3 Results
mobile and stationary emission sources, and twice the
urban area of Manaus, which aimed at simulating an 3.1 Evaluationofthebaselinescenario
environmentpresentingagrowthoftheregionalpopula-
tionandurbanarea,aswellasincreasedanthropogenic 3.1.1 Meteorology
emissions.Theurbanexpansionhasbeenconcentrated
Figure 4 shows the comparison between the observed and
intheagriculturalandforestareas,preservingthewater
the simulated values for temperature and relative humidity
resources, as well as forest reserves and parks around
at Colégio Militar, IFAM, T1 and T3, corresponding to the
thecityofManaus.
periodsof17–18March2014.Overall,thereisagoodrepre-
The meteorological fields and air pollutants obtained from sentationofthetemporalevolutionofthetemperature(e.g.,
the simulation of scenario C0 have been compared to the r =0.91 for T1) both for the average value and the daily
ground-baseddataobserved(seeFig.1).Theanalysisofme- minimum. The T1 station has the highest peak of temper-
teorologicalvariableshasbeenconductedbasedonPielke’s ature: about 36◦C, which is the highest among the moni-
skillscore(Spielke)(Pielke,2002;HallakandPerreiraFilho, toring points compared to simulations. However, the model
2011), Pearson’s correlation coefficient (r), and mean bias has proven to present difficulties in simulating the maxi-
(MB). For the air pollutants, in addition to r and MB, two mum temperature, mainly for T1 station, which is located
statistical indexes were used, the mean normalized bias er- in the central part of the urban area of Manaus. For the
ror(MNBE)andthemeannormalizedgrosserror(MNGE). IFAMstation,whichisalsoacentralsite,diurnalpeak(e.g.,
Such parameters have often been used in several studies to 33.5◦C average observed compared to 30.3◦C simulated
assess the performance of atmospheric models (e.g., Tie et at 14:00LT) and minimum night (e.g., 24.8◦C average ob-
al., 2007; Han et al., 2009; Tuccella et al., 2012). Table 5 served compared to 26.3◦C simulated at 02:00LT) temper-
shows a summary of the statistical indexes used to evaluate atures were weakly represented. For all stations, the model
themodelperformance. presentsproblemsincapturingthepeaktemperature.Therel-
ativehumidityprofilesshowagoodlevelofagreementofthe
simulationtotheaveragevaluesofColégioMilitar(r =0.89
andRMSE=5.1)andT1(r =0.88andRMSE=4.8).How-
ever, sites IFAM and T3 have the greatest discrepancies
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7984 S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution
Table4.Summaryofsimulationscenarios.
Scenarios Emissions
C0 Biogenic,vehicularandstationary(TPPsandREMANrefinery)sources
C1 Onlybiogenicsources
C2 Biogenicandvehicular
C3 Biogenicandstationary(TPPsandREMANrefinery)sources
C4 Biogenicanddoubledmobileandstationarysources
Table5.Statisticalindexesusedtoevaluatethemodelperformance.
∗
Indexes Equation
N
P(pk−p¯)(ok−o¯)
Pearson’scorrelationcoefficient r= k=1
" #1/2" #1/2
N N
P(pk−p¯)2 P(ok−o¯)2
k=1 k=1
N
Meanbias MB= N1 P(pk −ok)=p¯−o¯
k=1
N
Rootmeansquareerror RMSE=[N1 P(pk−ok)2]1/2
k=1
Pielkeskill SPielke=(cid:12)(cid:12)(cid:12)1−σσpo(cid:12)(cid:12)(cid:12)+RMσoSE+RMSσEp−bias
Meannormalizedbiaserror MNBE= 1 PN h(pk−ok)i×100
Nk=1 ok
Meannormalizedgrosserror MNG= 1 PN h|pk−ok|i×100
Nk=1 ok
∗pkandokarethepredictedandobservedvalueattimek,respectively.
amongtheresults,withMBvaluesequalto−8.3and−10.1, estimatethevaluesobserved(MNBE=50.8),whereaswith
respectively. CO and NO the tendency is the opposite. They underesti-
x
RegardingthestatisticalindexespresentedinTable6,the mated the values in most of the simulation period (MNBE
correlation coefficient (r) has provided satisfactory results equal a −29.5 and −35.4 for NO and CO, respectively).
x
forallstations,withthelowestvaluesfortemperature(0.87) The most significant differences have been observed during
andhumidity(0.71)associatedwithT3stationandthehigh- thepeakhoursandmainlyforCO.Theweakestperformance
estvaluesof0.91and0.89forColégioMilitar,respectively. ofthemodelhappensmainlyduringthenocturnalpeak,sit-
According to Pielke’s parameter skill defined in Hallak and uatedgenerallybetween18:00andmidnight(localtime).A
Pereira Filho (2011), a good model performance occurs for possible explanation for the poor performance of the model
index values of less than 2. In this case, the performance during nocturnal peaks of CO is the fact that the fire out-
of the model is satisfactory for most of the stations, ex- breakswerenotconsidered(fourspotsoffireoccurreddur-
ceptfortemperatureatT1(S =2.9)andrelativehumid- ingtheperiodinsidethegrid,althoughalowincidenceoffire
pielke
ityatIFAM(S =3.7)andT3(S =3.9).According outbreaks was the criterion used for chosen the simulation
pielke pielke
to the mean bias, it has been observed that the simulation, period,Fig.S2),whichcouldhaveinfluencedthesimulated
in general, underestimates the majority of observed values, values. The MB indicates that the three analyzed pollutants
mainlyfortemperature(T1,MB=−1.5)andrelativehumid- present significant differences, with the value of PM MB
2.5
ity(IFAM,andT3). of1.30µgm−3comparedtoa0.66µgm−3averageobserved,
NO withvaluesof−26.2ppbcomparedtoan88.7ppbav-
x
3.1.2 Airpollutants erage observed, and CO presenting −135ppb of MB com-
pared to a 382.6ppb average observed. Similar to the Pear-
T1 is the only station that has air quality data and only for soncoefficient,themodelshowsgoodcorrelationsforPM ,
2.5
NO , CO and PM concentrations. Figure 5 and Table 7 NO andCOwith0.72,0.53and0.53,respectively.Inaddi-
x 2.5 x
showthecomparisonsbetweenthesimulation(scenarioC0) tion,inordertoassessthesensitivityofthemodeltorespond
andtheobservationofsuchpollutants.IntermsofthePM , todifferentemissionscenarios,numericalexperimentswere
2.5
ithasbeenobserved,ingeneral,thatthemodeltendstoover-
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S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution 7985
Figure4.TemporalevaluationoftemperatureandrelativehumiditysimulatedandobservedforColégioMilitar,IFAM,T1andT3.
carried out considering variations in emissions of ±15 and Thereareafewstudiesmeasuringtheimpactoftheurban
±30% in relation to scenario C0 (biogenic natural emis- plume of Manaus on the pollutant concentration downwind
sion + vehicle + thermal power plants + REMAN refin- of the city. For example, CO concentrations of 90–120ppb
ery). Temporal evolution of PM and NO concentrations wereobservedbyMartinetal.(2017)fromcrosswindtran-
10 x
isshowninFig.S3.Overall,itwasobservedthatthemodel sectsinsidetheplanetaryboundarylayer(PBL)oftheurban
is sensitive to the variations performed and for PM and plume of Manaus downwind. Considering the scenario C0,
10
NO theresponsestothevariationsarelinear.However,O representingtheemissioninventoryofcurrentconditionsfor
x 3
changes are not linear to emission variations in VOC and the region, the values found in this study stayed in the 84–
NO (Atkinson,2000;MartinsandAndrade,2008). 207ppb range, where the most variations in the concentra-
x
tionwereobservedinthelowerverticallevelsofthemodel,
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7986 S.A.AbouRafeeetal.:Contributionsofmobile,stationaryandbiogenicsourcestoairpollution
Table6.Evaluationindexesformodelperformanceofmeteorologicalvariablesduringthesimulationperiod(baselinescenarioC0).
Variable Station Meanobs Meansim σobs σsim r MB RMSE Spielke
ColégioMilitar 29.6 28.8 2.3 1.7 0.91 −0.8 1.3 1.9
◦ IFAM 28.2 28.5 2.8 1.8 0.88 0.3 1.5 1.6
Temperature( C)
T1 30.0 28.5 2.5 1.8 0.88 −1.5 1.9 2.9
T3 26.6 26.5 2.6 2.5 0.87 −0.1 1.3 1.1
ColégioMilitar 73.6 70.7 9.1 8.1 0.89 −2.9 5.1 1.7
IFAM 80.4 72.1 10.5 7.2 0.89 −8.3 9.7 3.7
Relativehumidity(%)
T1 73.9 71.8 9.3 8.2 0.88 −2.1 4.8 1.5
T3 89.8 79.7 10.6 8.7 0.71 −10.1 12.6 3.9
Table7.Evaluationindexesformodelperformanceofairpollutantsduringthesimulationperiod(baselinescenarioC0).
Variable Meanobs Meansim σobs σsim r MB MNBE(%) MNG(%)
PM2.5(ugm−3) 1.30 1.96 0.81 0.93 0.72 0.66 50.8 57.3
NOx (ppb) 88.7 62.5 53 52.7 0.53 −26.2 −29.5 47.4
CO(ppb) 382.6 247.3 296.3 93.3 0.53 −135.3 −35.4 53.1
as expected. It is not possible to say that the model is over- mal power plants, adding a total of 1.5kgNO day−1. As a
x
predicting the actual value, since it is associated with dif- result, the simulated NO and O mixing ratios changed to
x 3
ferent periods. In addition, the simulated concentrations are 30and35ppb,respectively.Thesignificanteffectsofthermal
much lower than the CO concentrations observed in previ- power plants are also evidenced in the present study by the
ous measurements for the dry season in Amazonas, under lowconcentrationofNO inthescenariosC1andC2(which
x
strongeffectsofbiomassburningemission(e.g.,Crutzenet donotincludethestationaryemissionsources),comparedto
al., 1985). O mixing ratios reported by Kuhn et al. (2010) C0(currentconditions).
3
varied from 21ppb in the adjacent background to a peak
value of 63ppb for O within the PBL at 100km distance 3.2 Scenarioemissions
3
from Manaus. In the simulated scenario C0, the peak val-
ueswereintherange26–29ppbwithinthePBLand40ppb Thediscussionsonthesimulationresultsrelatedtoscenario
above it. O mixing ratios downwind of Manaus under the emissions were analyzed in the lowest model level and di-
3
influence of anthropogenic pollution were also reported by vided in three topics – that is, the analysis of the mean and
Trebs et al. (2012) and were on average 31±14ppb, with peak behavior of pollutants, spatial analysis, and temporal
peak values of 60ppb at a distance of 19km downwind of evaluationdescribedbelow.
Manaus.Suchresultsareingoodagreementwiththeresults
3.2.1 Meanandpeakbehaviorofpollutants
in this work: 10–43ppb for the adjacent levels to 400m at
10km west of Manaus, considering the current conditions
Identifyingthedifferencesbetweenscenariosinatmosphere
givenbyscenarioC0.
models with elevated complexity is not a straightforward
In terms of previous numerical studies in the Manaus
task, due to the large number of components involved, with
urban-influenced area, Kuhn et al. (2010) applied a single-
each of them partially contributing to the evolution of the
column chemistry and meteorological model (Ganzeveld et
concentrationsinbothtimeandspace.Inthissense,theirim-
al.,2002).Twonumericalexperimentswereperformedtoas-
pactsonpeakaveragevaluesasinthespatialaverageshould
sessthewestward-movingplume,thefirstoneperformedby
be evaluated. For this purpose, the methodology for micro-
using anthropogenic emission fluxes from the EDGAR 3.2
physicalvariableproposedbyMartinsetal.(2009)hasbeen
emission database, but with the pollutant flux based on the
1◦×1◦ grid point of the location of the city of São Paulo, adapted to evaluate the air pollutants in this study. There
are two average properties. The first is the spatial average–
increasedbyafactorof7,duetotheabsenceofalocalemis-
temporalaverage(SATA),whichrepresentsameanvalueav-
sion inventory for Manaus. In this scenario, the simulated
eragebothspatiallyandovertime,andthesecondoneisthe
CO, NO and O mixing ratios at approximately 400m al-
x 3
spatialpeak–temporalaverage(SPTA),whichcorrespondsto
titudeand10kmdownwindwereapproximately140,4,and
the spatial peak average over time. The calculation of the
50ppb, respectively. The impact of stationary sources was
values was based on 87% of the study grid, removing five
evaluatedinasecondscenariobytheinclusionoffourther-
points of the grid in each extremity, which were the values
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