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Atmospheric Environment 43 (2009) 6388–6397

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Atmospheric Environment

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Estimating trace gas and aerosol emissions over South America: Relationshipbetween fire radiative energy released and aerosol optical depth observations

Gabriel Pereira a,*, Saulo R. Freitas b, Elisabete Caria Moraes a, Nelson Jesus Ferreira a,b,Yosio Edemir Shimabukuro a, Vadlamudi Brahmananda Rao b, Karla M. Longo c

a Remote Sensing Division (DSR), National Institute for Space Research, Av dos Astronautas, 1758 Jd. Granja, CEP: 12227-010 Sao Jose dos Campos, SP, Brazilb Center for Weather Forecasting and Climate Studies (CPTEC), National Institute for Space Research (INPE), Rodovia Presidente Dutra, Km 40, SP-RJ,CEP: 12630-000 Cachoeira Paulista, SP, Brazilc Center for Space and Atmospheric Sciences (CEA), National Institute for Space Research, Av dos Astronautas, 1758 Jd. Granja, CEP: 12227-010 Sao Jose dos Campos, SP, Brazil

a r t i c l e i n f o

Article history:Received 27 August 2008Received in revised form8 August 2009Accepted 7 September 2009

Keywords:Fire radiative powerSmoke aerosol emission coefficientAerosol optical depthBiomass burning

* Corresponding author. Tel.: þ55 12 3945 6668.E-mail address: gabriel@dsr.inpe.br (G. Pereira).

1352-2310/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.atmosenv.2009.09.013

a b s t r a c t

Contemporary human activities such as tropical deforestation, land clearing for agriculture, pest controland grassland management lead to biomass burning, which in turn leads to land-cover changes.However, biomass burning emissions are not correctly measured and the methods to assess theseemissions form a part of current research area. The traditional methods for estimating aerosols and tracegases released into the atmosphere generally use emission factors associated with fuel loading andmoisture characteristics and other parameters that are hard to estimate in near real-time applications. Inthis paper, fire radiative power (FRP) products were extracted from Moderate Resolution ImagingSpectroradiometer (MODIS) and from the Geostationary Operational Environmental Satellites (GOES) fireproducts and new South America generic biomes FRE-based smoke aerosol emission coefficients werederived and applied in 2002 South America fire season. The inventory estimated by MODIS and GOES FRPmeasurements were included in Coupled Aerosol-Tracer Transport model coupled to the Braziliandevelopments on the Regional Atmospheric Modeling System (CATT-BRAMS) and evaluated with groundtruth collected in Large Scale Biosphere–Atmosphere Smoke, Aerosols, Clouds, rainfall, and Climate(SMOCC) and Radiation, Cloud, and Climate Interactions (RaCCI). Although the linear regression showedthat GOES FRP overestimates MODIS FRP observations, the use of a common external parameter such asMODIS aerosol optical depth product could minimize the difference between sensors. The relationshipbetween the PM2.5mm (Particulate Matter with diameter less than 2.5 mm) and CO (Carbon Monoxide)model shows a good agreement with SMOCC/RaCCI data in the general pattern of temporal evolution.The results showed high correlations, with values between 0.80 and 0.95 (significant at 0.5 level bystudent t test), for the CATT-BRAMS simulations with PM2.5mm and CO.

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1. Introduction

Tropical biomass burning occurs with land-use and land-covermanagement such as tropical deforestation, agricultural landclearing, pest control and grassland management (Crutzen andAndreae, 1990; Liu et al., 2005; Werf et al., 2006). About 90% ofglobal fires are of anthropogenic origin (Andreae, 1991), emittingapproximately 4000 Tg (teragrams) of carbon into the atmosphereevery year, about 80% occurring in the tropical regions of the globe(Hao and Liu, 1994).

The biomass burning process modifies the surface albedo,surface and atmospheric net radiation budget and atmospheric

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chemistry composition. It might further affect hydrological andbiogeochemical cycles, weather and climate (Rosenfeld, 1999;Andreae et al., 2004; Moraes et al., 2004). However, biomassburning emissions are not accurately measured. One traditionalmethod to estimate the aerosols and trace gases emissions frombiomass burning (M[e]) uses the amount of biomass burnt and theemission factor per species, following the equation (Andreae andMerlet, 2001; Longo et al., 2007):

M½e� ¼ avegbvegafireEF½e�veg (1)

where a is the amount of above-ground biomass available forburning, b is the combustion factor, afire is the burning area and EF[e]

is the emission factor for a certain species (e) and for a determinedvegetation type. However, these variables are very difficult toestimate and might cause large uncertainties in the emissions

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–6397 6389

estimation, mainly when applied in a global scale (Hoelzemannet al., 2004; Ito and Penner, 2004; Ichoku and Kaufman, 2005).

With the advent of remote sensing satellite in recent years,several studies have been conducted in order to estimate the totalbiomass consumed, smoke aerosol emissions in the burnt area andtheir relationship with satellite and laboratory measurements ofFire Radiative Energy (FRE) released rate (Kaufman et al., 1998a;Wooster et al., 2003, 2005; Giglio et al., 2003; Ichoku and Kaufman,2005; Roberts et al., 2005; Jordan et al., 2008; Ichoku et al., 2008).The relationship between FRE emitted, biomass consumed andthe smoke aerosol emission coefficients based on FRE allows nearreal-time estimation of the total amount aerosols and trace gasesemitted into the atmosphere using air pollution models (Freitaset al., 2005, 2009; Wang et al., 2006; Longo et al., 2007). This alsopermits to obtain inventories to be used by global chemistrytransport models (Chatfield et al., 2002; Horowitz et al., 2003).

The main objective of this study is to develop new FRE-basedsmoke aerosol emission coefficients designed for South Americausing the WFABBA fire products (Prins et al.,1998), which is based onGOES data. Then, biomass burning emission coefficients prescribedfor this fire product will be compared with the FRE-based smokeaerosol emission coefficients for MODIS (Ichoku and Kaufman,2005). To evaluate the performance of both methodologies, theprescribed emissions are included in the regional atmospherictransport model CATT-BRAMS (Freitas et al., 2009; Longo et al.,2007) adapted for the 2002 South America dry season.

Finally, model results of CO and PM2.5mm are compared withground data collected in the Large Scale Biosphere–Atmosphere(LBA) SMOCC/RaCCI in the Amazon campaigns during the 2002 dry-to-wet transition season (Andreae et al., 2004) and the accuracy ofthe model results are determined by using linear regress model andstandard error of estimate.

2. Data and methodology

2.1. MODIS aerosol optical depth (AOD) and thermal anomalies(MOD14/MYD14) and WFABBA/GOES fire product

The MODIS sensor aboard the EOS Terra and Aqua platformsmeasures Aerosol Optical Depth (AOD), which gives the loss of solarradiation by aerosols through the processes of absorption andscattering along the vertical column of the atmosphere. The MODISLevel 2 AOD products from Terra (MOD04) and Aqua (MYDO4) havea spatial resolution of 10 km at nadir and cover a swath of 2330 km(Kaufman and Tanre, 1998). Moreover, EOS platforms perform firedetection (MOD14 and MYD14) using the brightness temperaturesassociated to 4 mm and 11 mm (mid-infrared radiation) channelsusing a contextual algorithm, retrieving fire pixel with a nominalspatial resolution of 1 � 1 km (Giglio et al., 2003). The WFABBA isa product for detection of fire and thermal anomalies based onGOES data that is available with a high frequency of observationsand with a nominal spatial resolution of 4 � 4 km at NADIR. Thealgorithm of WFABBA, as well as MODIS algorithm, uses two bands,located in 3.9 mm and 10.7 mm channels, for the detection of firepixels (Prins et al., 1998).

2.2. Fire radiative power

The FRP can be defined as the part of the chemical energyemitted as radiation in the biomass burning process. The temporalintegration of FRP gives the FRE. FRP can be estimated via anairborne infrared radiometer (Riggan et al., 2004) and also throughinfrared bands in fire data from environmental satellites such asGOES and MODIS.

The MODIS sensor estimates FRP through the method firstdeveloped by Kaufman et al. (1996, 1998a,b) and characterizes therelationship between the brightness temperature of fire andbackground pixels in the middle infrared (band center near 4 mm)and is given by:

FRPMODIS ¼ 4:310�19�

T8f 4mm � T8

b4mm

�Asampl (2)

where Tf4mm and Tb4mm are, respectively, the MODIS brightnesstemperatures of the fire pixel and the nonfire background in Kelvin(K), Asampl is the real area of the pixel (km2) and FRP is the fireRadiative power in Megawatts (MW or MJ s�1).

The WFABBA/GOES products are not included for FRP estima-tion. However, the formula (3) could be used to calculate thisphysical property.

FRP ¼ Af sT4f (3)

where Af represents the fraction of fire in the pixel (m), s is theStefan–Boltzmann constant (5.67 � 10�8 W m�2 K�4) and Tf is thefire temperature. The area and the temperature of fire are derivedby the method described by Dozier (1981).

Although Satellite FRP measurements could be an improvementto quantify biomass burning emissions, there are some errors anddetection limitations inherent to this method. MODIS and GOES-derived FRP products require complex validations that are not yetperformed. Some uncertainties are intrinsic to remote sensingderived products and factors such as sensor channel saturation byhigh fire temperatures, undetected or missed fires, cloud cover,smoldering effects of fires in FRP estimation, regional characteristicsof biomass burning and instantaneous fire size and temperature.

The FRP estimated by using WFABBA/GOES fire product areassessed via near-simultaneous FRP data derived from MODIS fireobservations. Although FRP can be estimated by several methodswhich involve a variety of errors in these processes, the techniquedescribed in Eq. (3) was chosen to extract GOES FRP due to the lack ofbackground information in WFABBA/GOES fire products. However,approximately 10% of the 2002 fires detected by WFABBA/GOESpresented an early saturation in 3.9 mm fire channel, not allowing theestimation of FRP by Eq. (3).

Instead of removing all saturated WFABBA/GOES FRP observa-tions and ignoring important episodes of biomass burning, analternative approach is adopted to assimilate smoke aerosol emis-sions FRP-based inventory to CATT-BRAMS model. Consequently, toassign an FRP value to saturated pixels, the middle infrared (MIR)radiance method proposed by Wooster et al. (2003) is applied inWFABBA data, with a constant background temperature of 300 K.The MIR radiance method for FRP estimation is based on theassumption that spectral radiance emitted in 3.9 mm waveband islinearly proportional to the FRP. MIR radiance method approach isexpressed by:

FRPMIR ¼Aga s

Z4:03

3:76

Bðl; TÞdl� Lb

Bðl; TÞ ¼ c1

l5�

exp�c2

lT

�� 1� (4)

where Ag is GOES pixel area (16 km2); a is a constant fit based inGOES MIR spectral channel (3.07; 3.06 or 3.08 � 10�9 W m�2

sr�1 mm�1 K�4, to GOES-8, GOES-9/10 and GOES-12 imager,respectively) acquired from Wooster et al. (2005); B(l,T) is Planck’sRadiation Law; c1 and c2 are constants (3.74 � 108 W m�2 and1.44 � 104 mm K, respectively); l is wavelength (mm); T is tempera-ture (K) and Lb is spectral radiance emitted by background (110 MW).

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–63976390

2.3. FRE-based smoke aerosol emission coefficientsfrom WFABBA/GOES

In this study, the FRE-based smoke aerosol emission coefficientsfor WFABBA/GOES fire data are created by using the methodproposed by Ichoku and Kaufman (2005). This method estimatesthe sum of the rate of smoke aerosol emissions (Rsa, in kg M J�1) ina given area and makes a regression with the FRE for the same area.The Rsa can be calculated by Eq. (5).

Rsa ¼

0BBBB@Pfire

i¼1Ap

�AODf

550nm�AODb550nm

baþbs

�Pfire

i¼1

� ffiffiffiffiAp

pffiffiffiffiffiffiffiffiffiffiu2þv2p

�1CCCCA (5)

where AODf550nm and AODb

550nm represent the aerosol opticaldepth at 550 nm in the smoke aerosol pixel and in the background(8 surround pixels), respectively. For the values of ba (massabsorption efficiency – 0.6 m2 g�1) and bs (mass scattering

Fig. 1. Study area divided into three large homogeneous ecosystems characteristics: (a)(in northeastern of South America); and (c) Atlantic Forest (tropical and subtropical moist

efficiency – 4.0 m2 g�1) those proposed by Ichoku and Kaufman(2005), which represent the median value for smoke aerosolemission with a time life between 5 min and 1 h, are used. In Eq. (5)Ap represents the MODIS AOD pixel area; u and v are the zonal andmeridional components of the wind at an altitude of 1.6 kmobtained from BRAMS atmospheric model integration.

Finally, for each clustered 10 km fire pixel, the FRP derived fromWFABBA/GOES (in MW or MJ s�1) and Rsa derived from MOD04/MYD04 AOD (in kg s�1), with a maximum delay of 20 min betweenthe AOD retrieval and the FRP/GOES observation, are extracted. Thedata are separated for the three South America biome regions(Fig. 1), as described in Ichoku and Kaufman (2005).

This technique allows estimation of aerosol and trace gasemissions through the equation:

M½e� ¼ Ce$FRP (6)

where M[e] represents the smoke mass flux (kg s�1), Ce (kg M J�1) isthe smoke aerosol emission coefficient and FRP is the energyreleased in the biomass burning process (MW). However, it remains

Amazon Tropical Rain Forest and Brazilian Cerrado; (b) Brazilian Caatinga biomeforest, tropical dry forest, tropical savannas, mangrove forests) and grassland biomes.

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–6397 6391

unclear if these new algorithms can be used to generate regionalemission estimates that agree with field observations.

Fig. 1 shows the study area separated into three large regions:Amazon Tropical Rain Forest and Brazilian Cerrado in (a); BrazilianCaatinga biome (in northeastern South America) in (b); and AtlanticForest (tropical and subtropical moist forest, tropical dry forest,tropical savannas, mangrove forests) and grassland biomes in (c).Amazon Tropical Rain forest is a moist broadleaf forest with sevenmillion square kilometers extension where approximately five andhalf million square kilometers are covered by rain forest (Lauranceet al., 1999). Cerrado or Brazilian Savannah is characterized byextensive savannah formations with gallery forests includingvarious types of vegetation. The savannah formation includeshumid fields, alpine pastures and dense forests in fertile soils,creating a significant difference between the amount of woody andherbaceous vegetation in the area.

Caatinga biome is characterized as a xeric shrubland and thornforest that shed their leaves seasonally. The vegetation types arerepresented by cacti, thick-stemmed plants, thorny brush, arid-adapted grasses, tropical broad-leaved trees and shrubby. AtlanticForest represents a biome that covers tropical and subtropicalmoist forest, tropical dry forest, tropical savannah and mangroveforests areas. This biome is found in Atlantic coast of Brazil andArgentina and in the continental area such as Paraguay.

2.4. Model description

The CATT, an Eulerian transport model fully coupled to BRAMS,is a numerical model that simulates biomass burning emissions,deposition and transport at grid and sub-grid scales. In this model,the trace gas and aerosol emissions, deposition and transportestimation are obtained simultaneously with the evolution ofatmospheric condition, using the dynamical and physical parame-terizations of the atmospheric model integration.

The mass continuity equation for CO and PM2.5mm in the form ofa tendency equation is expressed by Freitas et al. (2009) as:

Table 1Average emission factors (g kg�1) to CO, PM10mm and PM2.5mm assigned to FRE-basedsmoke aerosol emission coefficient’s locations.

Ecosystems PM10mm PM2.5mm CO PM2.5mm

ratioCOratio

Amazon Tropical Rain Forestand Brazilian Cerrado

12.5 9.9 117.0 0.79 9.4

Brazilian Caatinga biome(closed shrublands)

6.9 5.6 84.0 0.81 12.2

Atlantic forest andgrassland biomes

6.9 5.6 84.0 0.81 12.2

vsvt¼�

vsvt

�adv|fflfflfflfflffl{zfflfflfflfflffl}

I

þ�

vsvt

�PBL diff|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}II

þ�

vsvt

�deep conv|fflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflffl}III

þ�

vsvt

�shallow conv|fflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflffl}

IV

þ�

vsvt

�chem CO|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}V

þWPM2:5|fflfflffl{zfflfflffl}VI

þ R|{z}VII

þ Qpr|{z}VIII

; (7)

where s is the grid box mean tracer mixing ratio, term (I) representsthe 3-d transport term (advection by the mean wind), term (II) isthe sub-grid scale diffusion in the PBL, terms (III) and (IV) are thesub-grid transport by deep and shallow convection, respectively.Term (V) is applied to CO which is treated as a passive tracer witha lifetime of 30 days (Seinfeld and Pandis, 1998), term (VI) is the wetremoval applied to PM2.5, term (VII) refers to the dry depositionapplied to gases and aerosols particles and, finally, term (VIII) is thesource term that includes the plume rise mechanism associatedwith the vegetation fires.

The model simulations for the period, 15 July–15 Novemberwhich represents South America 2002 dry season are performedwith a 50 km horizontal resolution grid. The vertical resolutionvaried telescopically with higher resolution at surface (150 m) witha ratio of 1.07 up to a maximum vertical resolution of 850 m, withthe top model altitude of 20 km in 38 vertical levels. CO andPM2.5mm fields are initialized using horizontally homogeneousprofiles associated with a background situation and the model isran during a period of 15 days with sources for the spin-up. Lateralboundary condition is defined as having constant inflow and radi-ative variable outflow. CO emissions from urban-industrial-

vehicular activities and biogenic sources are not included in thisstudy, because in the areas where we focused model discussion andevaluation, biomass burning is by far the most important source.

2.5. Source emission parameterization and evaluationwith observed data

Through MODIS and GOES South America FRE-based coeffi-cients for three generic biomes, the 2002 South America dry seasonsmoke aerosol emission inventory of PM is estimated. A relation-ship between particulate matter with diameter less than 10 mm(PM10mm) and CO and PM2.5mm emission factors is adopted to esti-mate CATT-BRAMS biomass burning emissions input source. Thoseemission factors (Table 1) acquired from Wiedinmyer et al. (2006),are based on published literature such as Andreae and Merlet(2001), Hoelzemann et al. (2004), Liu (2004). Therefore, a ratiobetween emission factors is created and used to convert the smokeflux values from Ichoku and Kaufman (2005) approach in CO andPM2.5mm South America 2002 emissions inventory.

The CATT-BRAMS model simulations of CO and PM2.5mm arecompared with ground data from LBA SMOCC/RaCCI campaign,collected near 62.37oW and 10.75oS in Amazon basin. The PM2.5mm

and CO near-surface measurements were made at Ouro Preto doOeste pasture site from September to November 2002. The PM2.5mm

particle mass concentration is measured with a TEOM (TaperedElement Oscillation Mass Balance) with a 30-min temporal reso-lution from 10 September to 4 November 2002. Therefore, anintercomparison of the PM2.5mm and CO model results at 12:00 UTCwith the daily average centered at 12:00 UTC is done to evaluatethese simulations.

In SMOCC/RaCCI campaigns, the rainfall regime variability isobserved through dry-to-wet transition season. While the first fewweeks were characterized by low rainfall rates with high number ofoccurrences of vegetation fires, not only in the campaign area butall over the Amazon basin and Central Brazil, this characteristic is

clearly reflected in CO and aerosol particle surface-level measure-ments, showing values as high as 4000 ppb and 210 mg m�3,respectively, with a strong variability. This variability is influencedby diurnal evolution of boundary layer condition and indicates thatthe fires were quite close to the measurements site with fire plumesnot significantly dispersed. In October, rainfall rate begins toincrease and, consequently, the number of vegetation firesdecreased. In November the rainfall causes a remarkable reductionof fires, but a few hotspots were still observed.

Fig. 3. Linear regression with standard error bars between 2000 and 2008 South andNorth America WFABBBA/GOES and MODIS FRP.

Fig. 2. WFABBBA/GOES South and North America fire data with the total number of detected fires (dark grey), saturated fire pixels (light grey) and its percentage (black line).

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–63976392

3. Results

3.1. WFABBA/GOES FRP and FRE-based smoke aerosol emissioncoefficients accuracy assessment with MODIS FRP data

Fig. 2 shows 2000–2008 WFABBBA/GOES South and NorthAmerica fire data with the total number of detected fires (dark grey)and the saturated fire pixels and their percentage (light grey andblack line, respectively). During nine years of observation, elimi-nating the cloud contaminated pixels and the medium/low prob-ability fire pixel, GOES fire products detected approximately9.38 � 106 hotspots, in which only 6.6% or 6.16 � 105 are saturatedpixels. These values could change due to variations in biomassburning activities and seasonal weather characteristics, such asstrong dry or atypical rainy periods.

To assess GOES’s capability to retrieve regional-scale FRP, the2000–2008 South and North America data are acquired and con-verted in FRP values by Eqs. (3) and (4). Thus, for a given GOES andMOD14/MYD14 retrieved FRP 4 km2 pixel, the delay between bothfire observations is calculated and if the difference between them wasless than 15 min the data are included in regression. In cases whenthere is more than one MODIS fire observation, the value computed isthe sum of MODIS fire pixels in that simultaneous GOES area.

For GOES and MODIS 2000–2008 South and North America firedetections, approximately 28,000 fire observations satisfied theanalysis condition. Consequently, simultaneous FRP observationsare grouped in their respective months. The linear regression withstandard error bars between WFABBBA/GOES retrieved FRP (axis x)and MOD14/MYD14 FRP (axis y), through 2000–2008 and groupedin 93 months, is shown in Fig. 3.

Moreover, while MODIS currently represents the standardsensor to extract global FRP due to its channels with different gainsettings and saturation temperatures, retrieving GOES FRP are notentirely exploited. GOES and MODIS regression, shown in Fig. 3,demonstrates a correlation greater than 96% (significant at 0.5 levelby student t test) between monthly FRP measurements derivedfrom these two sensors, with a ratio of 0.34 between MODIS andGOES FRP. This difference could be explained by instrument char-acteristics such as spatial, radiometric and spectral resolutions,sensor noise and atmospheric transmittance. Also, FRP derivationmethod and assumptions in this process, could increase the sourceof error as described in Giglio and Kendall (2001), Wooster et al.(2003, 2005), Roberts et al. (2005), Roberts and Wooster (2008) andJordan et al. (2008). Some examples of error sources are the firetemperature and area estimated by Dozier (1981) method, the use

of MIR Radiance method to GOES saturated pixels and algorithmdifferences.

3.2. FRE-based smoke aerosol emission coefficients

Fig. 4 shows the smoke aerosol emission coefficients based on theWFABBA/GOES FRP for three regions of South America. These coeffi-cients are separated according to vegetation type, as described byIchoku and Kaufman (2005) and shown in Fig. 1. In the graph, x-axisrepresents the radiative energy released by fire, in MJ s�1, and y-axisshows the smoke aerosol emission rate, in kg s�1, each data pointrefers to the sum of FRP and daytime smoke aerosol emissions overthe study area and presents the standard error bars are also presented.

In the CATT-BRAMS South America 2002 biomass burningsimulations for Amazon Forest and Cerrado, the value of0.03 (kg M J�1) is assumed for the smoke aerosol emission coeffi-cient, obtained by the regression of 4561 samples of WFABBA/GOESand MOD04/MYD04 AOD observations, clustered from 112daytimes, as shown in Fig. 4a. For the Brazilian Caatinga biome, thevalue of 0.006 (kg M J�1) was adopted, corresponding to theregression of 772 samples clustered from 54 daytimes, as shown inFig. 4b. For South America below 20�S, the regression of 294samples, clustered from 17 days provided a value of 0.02 (kg M J�1).

Fig. 4. Correlation between FRE released rate (MW) and smoke aerosol emission (kg s�1) rates from WFABBA/GOES and MOD04/MYD04 AOD observations for: (a) Amazon Forestand Cerrado; (b) Brazilian Caatinga biome; and (c) Atlantic Forest and grassland biomes. Each point of cluster represents the sum of FRP and daytime smoke aerosol emissions overthe study area with the standard error bars.

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–6397 6393

Fig. 5 shows the correlation and slope distributions between thesmoke aerosol emission and FRE release rates, using the bootstraptechnique developed by Efron (1982). In this method, a populationof 1.0 � 104 reconstructs the original curve and provides theparameters to create the confidence interval for the model esti-mate. As shown in Fig. 5a–c the highest frequencies of correlationsare between 0.70–0.75, 0.90–0.95 and 0.95–0.99 (significant at 0.5level by student t test) for Amazon Forest and Cerrado, BrazilianCaatinga biome and Atlantic Forest and grassland biomes,respectively.

These values indicate a good regression agreement between thesmoke aerosol emission and FRE release rates. Furthermore, theslopes (Fig. 5d–f) are centered in 0.03, 0.006 and 0.022, indicating,as expected, that the emissions in Amazon Forest and Cerradobiomes, are greater than for Caatinga, Atlantic Forest.

The MODIS and GOES FRE-based smoke aerosol emission coef-ficients show that most of fire radiated heat is undetected byenvironmental satellites. Laboratory measurements indicate thatPM2.5mm emissions for biomass burning are about 10 g kg�1

(Yokelson et al., 2008). However, if all the FRP released rate could bedetected from space, the ratio between PM2.5mm (kg)/FRE (MJ)would vary 0.000625–0.01, indicating that most of energy is lost inconvection column (Wooster et al., 2005; Freeborn et al., 2008;Yokelson et al., 2008).

To minimize differences in MODIS and GOES FRP measure-ments, an external variable could be introduced in both satellites inorder to fit this variable into a common property introducinga correction factor. The use of MODIS AOD (MOD04/MYD04)products to generate FRE-based smoke aerosol emission coeffi-cients for MODIS and GOES data may normalize these differences.Also, by comparing the coefficients created for MODIS FRP byIchoku and Kaufman (2005) with those created for GOES thiscorrection factor is necessary.

MODIS FRE-based smoke aerosol emission coefficient found byIchoku and Kaufman (2005) for Amazon Forest and Cerrado is0.063 kg M J�1, while the value found from GOES FRE-based coef-ficient for the same region is 0.03 kg M J�1 (52% less). In addition,the coefficient value for MODIS Atlantic Forest and grasslandbiomes is 0.061 kg M J�1, three times greater than the coefficient

generated for GOES (0.02 kg M J�1). Moreover, the most significantdifference between MODIS and GOES smoke aerosol coefficients isfound in the Caatinga biome, where the value decreased approxi-mately 87%, from 0.048 to 0.006 kg M J�1. Although Fig. 3 repre-sents a comparison between the average values for MODIS andGOES South and North America FRP, the differences found in theFRE-bases smoke aerosol emission coefficients could reflect thedifferences in FRP measurements for both satellites.

3.3. CATT-BRAMS model simulation and evaluationusing South America 2002 fire emissions

Using MODIS and GOES South America 2002 fire emissions data,the total smoke aerosol emission in each fire pixel is estimated andinserted in the model. Figs. 6 and 7 show (a) total PM2.5mm and COemissions; (b) the linear regression between the modeled data andthe observed data; and (c) the time series with the near surfacePM2.5mm (mg m�3) and CO (ppb) data collected in the LBA SMOCC/RaCCI campaigns and estimated by the smoke aerosol emissioncoefficient and modeled in CATT-BRAMS.

The relationship between the PM2.5mm and CO model results at 12UTC with the ground-based measurements reveals a good agree-ment in terms of the general pattern of the temporal evolution. Asshown in the time series, the method can estimate the aerosols andgases emitted in the biomass burning process; however, when theintensity of the biomass burning is too high, the values modeled bythe FRE-based smoke aerosol emission coefficient are under-estimated, probably due to the influence of smoke in FRPmeasurements. Furthermore, this method shows typical increasedemissions in regions below 20�S, including Bolivia, Paraguay andArgentina, when compared with traditional methodologies.

The correlations and the slopes between the CO and PM2.5mm

data collected in LBA SMOCC/RaCCI campaigns and CATT-BRAMSmodel simulation are presented in Fig. 8. The histogram frequenciesfor these statistical parameters are made using the bootstraptechnique (Efron, 1982), with a population of 1.0� 104. As shown inthe graphics, the data modeled by FRE-based smoke aerosolemission coefficient indicates a good agreement with ground truthdata. The highest correlation frequencies for CO are found between

Fig. 5. Correlation and slope distributions using the bootstrap technique developed by Efron (1982) for the three WFABBA/GOES smoke aerosol emission coefficients, showing in (a), (b) and (c)the correlationand in(d), (e)and (f) theslope samples histogram distribution forAmazon Forest and Cerrado;theBrazilian Caatingabiome; and Atlantic Forest and grassland biomes, respectively.

Fig. 6. (a) Map with the total PM2.5mm released by biomass burning between 15 July and 15 November 2002; (b) linear regression between the modeled data and the observed data;and (c) Time series with near surface PM2.5mm (mg m�3) observed and estimated by the smoke aerosol emission coefficients.

Fig. 7. (a) Map with the total of CO released by biomass burning between 15 July and 15 November 2002; (b) linear regression between the modeled data and the observed data;and (c) Time series with near surface CO (ppb) observed and estimated by the smoke aerosol emission coefficients.

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–6397 6395

0.80 and 0.90 (significant at 0.5 level by student t test), as show in(a). Furthermore, for the simulations with PM2.5mm the highestfrequency values are found between 0.85 and 0.90 (significant at0.5 level by student t test), as observed in (b).

Fig. 8c and d illustrate the slope frequency distribution for theCO and PM2.5mm simulations. The highest slope frequencies for thesimulation of emissions are between 0.80–0.90 and 1.20–1.40 forCO and PM2.5mm, respectively. Moreover, these values indicate that

Fig. 8. Bootstrap technique for CO and PM2.5mm showing the relationship between LBA SMcorrelation for CO and PM2.5mm, respectively, are presented. In (c) and (d) the frequency dis

the method for estimating the PM2.5mm based on smoke aerosolemission coefficients through the FRP satellite measurementsoverestimated this aerosol by 20%. Also, the CO emission simulationis made by applying a PM2.5mm based emission factor for eachvegetation type (Andreae and Merlet, 2001; Longo et al., 2007). Inthis indirect method, the CO values are underestimated by 10%.However, these overestimates and underestimates are comparablewith the uncertainties in traditional emissions methods.

OCC/RaCCI ground data and the CCATT-BRAMS model simulation. In (a) and (b) thetributions of slope are shown.

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–63976396

Furthermore, this method based on FRP satellite measurementsshows great promise for accuracy improvement applying Yokelsonet al. (2008) CO/PM ratio.

Also, the assumptions in FRP retrievals and FRE-based smokeaerosol emission coefficients might introduce several source oferrors such as simultaneous observations of emitted smoke and fire,MODIS AOD accuracy and its conversion in aerosol mass density,smoke plume height assumption, cloud cover, heterogeneity of areasselected to generate smoke aerosol emission coefficients and FRPalgorithms differences (Ichoku and Kaufman, 2005; Giglio andKendall, 2001; Wooster et al., 2005; Jordan et al., 2008).

4. Conclusions

In this study, 2000–2008 South and North America GOES andMODIS fire products are noted to show a remarkable relationshipwith monthly FRP measurements. Although GOES FRP over-estimate MODIS FRP by three times, the new GOES FRE-basedsmoke aerosol emission coefficients could minimize FRP sensormeasurement differences by introducing an external variable asa correction factor for satellite-derived emission estimation. TheMODIS and GOES FRE-based smoke aerosol emission coefficients,derived from the relationship between the smoke aerosol emissionrate and the FRE released rate, shows a good correlation with theground data from the LBA SMOCC/RaCCI campaigns with a PM2.5mm

and CO overestimating and underestimating about 20% and 10%respectively, but still comparable with the uncertainties in tradi-tional emissions methods.

The present technique is a substantial improvement in trace gasand aerosol emissions estimation, mainly for real-time applicationssince above-ground biomass, burned area, moisture conditions,combustion factor and burn efficiency, necessary for the traditionalmethods, are impossible to obtain in near real-time applications.

However, the FRE-based smoke aerosol emission coefficientsshow promise for accuracy development and new coefficients forsmall areas could be derived for South America, minimizing,consequently, the variability in regional coefficients which coverdistinct biomes.

Acknowledgments

This study was supported by CAPES (Brazilian Coordination forthe Improvement of Higher Education Personnel) and resulted inthe first author master’s degree proficiency. Also, the authorswould like to thank C. Ichoku for many useful discussions in the PMreleases integration; to J. Hoffman, C. C. Schmidt and E. Prins forfrequent support in GOES/FRP measurements explanations.

References

Andreae, M.O., 1991. Biomass burning: its history, use, and distribution and itsimpact on environmental quality and global climate. In: Levine, J.S. (Ed.), GlobalBiomass Burning. The MIT Press, Cambridge, MA, pp. 3–21.

Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomassburning. Global Biogeochemical Cycles 15, 955–966. 2000GB001382.

Andreae, M.O., Rosenfeld, D., Artaxo, P., Costa, A., Frank, G., Longo, K.M., SilvaDias, M.A.F., 2004. Smoking rain clouds over the Amazon. Science 303,1342–1345.

Chatfield, R.B., Guo, Z., Sachse, G.W., Blake, D.R., Blake, N.J., 2002. The subtropicalglobal plume in the Pacific Exploratory Mission-Tropics A (PEM-Tropics A),PEM-Tropics B, and the Global Atmospheric Sampling Program (GASP): howtropical emissions affect the remote Pacific. Journal of Geophysical Research 107(D16), 4278. doi:10.1029/2001JD000497.

Crutzen, P.J., Andreae, M.O., 1990. Biomass burning in the tropics: impact onatmospheric chemical and biochemical cycles. Science 250, 1669–1678.

Dozier, J., 1981. A method for satellite identification of surface temperature fields ofsubpixel resolution. Remote Sensing of Environment 11, 221–229.

Efron, B., 1982. The Jackknife, the Bootstrap, and Other Resampling Plans. Society ofIndustrial and Applied Mathematics CBMS-NSF Monographs. 38.

Freeborn, P.H., Wooster, M.J., Hao, W.M., Ryan, C.A., Nordgren, B.L., Baker, S.P.,Ichoku, C., 2008. Relationships between energy release, fuel mass loss, and tracegas and aerosol emissions during laboratory biomass fires. Journal ofGeophysical Research 113 (D1), D01102. doi:10.1029/2007JD008489.

Freitas, S.R., Longo, K., Silva Dias, M., Silva Dias, P., Chatfield, R., Prins, E., Artaxo, P.,Grell, G., Recuero, F., 2005. Monitoring the transport of biomass burningemissions in South America. Environmental Fluid Mechanics 5, 135–167.doi:10.1007/s10652-005-0243-7.

Freitas, S.R., Longo, K., Dias, M., Chatfield, R., Dias, P., Artaxo, P., Andreae, M.,Grell, G., Rodrigues, L., Fazenda, A., Panetta, J., 2009. The coupled aerosol andtracer transport model to the Brazilian developments on the regional atmo-spheric modeling system (CATT-BRAMS). Part 1: model description and eval-uation. Atmospheric Chemistry and Physics 9, 2843–2861.

Giglio, L., Kendall, J.D., 2001. Application of the Dozier retrieval to wildfirecharacterisation: a sensitivity analysis. Remote Sensing of Environment 77,34–49.

Giglio, L., Descloitres, J., Justice, C.O., Kaufman, Y., 2003. An enhanced contextual firedetection algorithm for MODIS. Remote Sensing of Environment 87, 273–282.

Hao, W.M., Liu, M.H., 1994. Spatial and temporal distribution of tropical biomassburning. Global Biogeochemical Cycles 8 (4), 495–503.

Hoelzemann, J.J., Schultz, M.G., Brasseur, G.P., Granier, C., Simon, M., 2004. Globalwildland fire emission model (GWEM): evaluating the use of global area burntsatellite data. Journal of Geophysical Research 109, D14S04.

Horowitz, L., Walters, S., Mauzerall, D., Emmons, L., Rasch, P., Granier, C., Tie, X.,Lamarque, J.F., Schultz, M., Brasseur, G., 2003. A global simulation of troposphericozone and related tracers: description and evaluation of MOZART, version 2.Journal of Geophysical Research 108 (D24), 4784. doi:10.1029/2002JD002853.

Ichoku, C., Kaufman, Y.J., 2005. A method to derive smoke emission rates fromMODIS fire radiative energy measurements. IEEE Transactions on Geoscienceand Remote Sensing 43 (11), 2636–2649.

Ichoku, C., Giglio, L., Wooster, M.J., Remer, L.A., 2008. Global characterization ofbiomass-burning patterns using satellite measurements of fire radiative energy.Remote Sensing of Environment 112, 2950–2962.

Ito, A., Penner, J.E., 2004. Global estimates of biomass burning emissions based onsatellite imagery for the year 2000. Journal of Geophysical Research 109,D14S05. doi:10.1029/2003JD004423.

Jordan, N.S., Ichoku, C., Hoff, R.M., 2008. Estimating smoke emissions over the USSouthern Great Plains using MODIS fire radiative power and aerosol observa-tions. Atmospheric Environment 42, 2007–2022.

Kaufman, Y.J., Remer, L.A., Ward, D.E., Kleidman, R., Flynn, L., Shelton, G.,Ottmar, R.D., Li, R.-R., Fraser, R.S., McDougal, D., 1996. Relationship betweenremotely sensed fire intensity and rate of emission of smoke: SCAR-C experi-ment. In: Levin, J. (Ed.), Global Biomass Burning. The MIT Press, pp. 685–696.

Kaufman, Y.J., Tanre, D., 1998. Algorithm for Remote Sensing of TroposphericAerosols from MODIS. MODIS Algorithm Theoretical Basis Document. ProductID: MOD04, Revised 26 October, 1998. Available at: <http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf>.

Kaufman, Y.J., Justice, C.O., Flynn, L., Kendall, J.D., Prins, E.M., Giglio, L., Ward, D.E.,Menzel, W.P., Setzer, A.W., 1998a. Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research 103, 32215–32238.

Kaufman, Y.J., Kleidman, R.G., King, M.D., 1998b. SCAR-B fires in the tropics: prop-erties and remote sensing from EOS-MODIS. Journal of Geophysical Research103, 31955–31968.

Laurance, W.F., Fearnside, P.M., Laurance, S.G., Delamonica, P., Lovejoy, T.E., Rankin-de Merona, J.M., Chambers, J.Q., Gascon, C., 1999. Relationship between soilsand Amazon forest biomass: a landscape-scale study. Forest Ecology andManagement 118, 127–138. doi:10.1016/S0378-1127(98)00494-0.

Liu, Y., 2004. Variability of wildland fire emissions across the contiguous UnitedStates. Atmospheric Environment 38, 3489–3499.

Liu, J., Drummond, J.R., Li, Q., Gille, J.C., Ziskin, D.C., 2005. Satellite mapping of COemission from forest fires in Northwest America using MOPITT measurements.Remote Sensing of Environment 95, 502–516.

Longo, K., Freitas, S.R., Setzer, A., Prins, E., Artaxo, P., Andreae, M., 2007. The CoupledAerosol and Tracer Transport model to the Brazilian developments on theRegional Atmospheric Modeling system (CATT-BRAMS). Part 2: model sensi-tivity to the biomass burning inventories. Atmospheric Chemistry and PhysicsDiscussion 7, 8571–8595.

Moraes, E.C., Franchito, S.H., Brahmananda Rao, V., 2004. Effects of biomass burningin Amazonia on climate: a numerical experiment with a statistical-dynamicalmodel. Journal of Geophysical Research 109, 1–12.

Prins, E.M., Felts, J.M., Menzel, W.P., Ward, D.E., 1998. An overview of GOES-8diurnal fire and smoke results for SCAR-B and 1995 fire season in SouthAmerica. Journal of Geophysical Research 103, 31821–31835.

Riggan, P., Tissell, R., Lockwood, R., Brass, J., Pereira, J., Miranda, H., Campos, T.,Higgins, R., 2004. Remote measurement of energy and carbon flux from wild-fires in Brazil. Ecological Application 14, 855–872.

Roberts, G., Wooster, M.J., 2008. Fire detection and fire characterization over Africausing Meteosat SEVIRI. IEEE Transactions on Geoscience and Remote Sensing46, 1200–1218. doi:10.1109/TGRS.2008.915751.

Roberts, G., Wooster, M.J., Perry, G.L.W., Drake, N., Rebelo, L.-M., Dipotso, F., 2005.Retrieval of biomass combustion rates and totals from fire radiative powerobservations: part 2 – application to southern Africa using geostationary SEVIRIimagery. Journal of Geophysical Research 110, D21111.

Rosenfeld, D., 1999. TRMM observed first direct evidence of smoke from forest firesinhibiting rainfall. Geophysical Research Letters 26, 3105–3108.

G. Pereira et al. / Atmospheric Environment 43 (2009) 6388–6397 6397

Seinfeld, J., Pandis, S., 1998. Atmospheric Chemistry and Physics. John Wiley & SonsInc., New York.

Wang, J., Christopher, S.A., Nair, U.S., Reid, J.S., Prins, E.M., Szykman, J., Hand, J.L., 2006.Mesoscale modeling of Central American smoke transport to the United States: 1.‘‘Top-down’’ assessment of emission strength and diurnal variation impacts.Journal of Geophysical Research 111, D05S17. doi:10.1029/2005JD006416.

Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Kasibhatla, P.S., Arellano Jr., A.F.,2006. Interannual variability in global biomass burning emissions from 1997 to2004. Atmospheric Chemistry and Physics 6, 3423–3441.

Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X., O’Neill, S.,Wynne, K.K., 2006. Estimating emissions from fires in North America for airquality modeling. Atmospheric Environment 40, 3419–3432.

Wooster, M.J., Zhukov, B., Oertel, D., 2003. Fire radiative energy for quantitativestudy of biomass burning: derivation from the BIRD experimental satellite andcomparison to MODIS fire products. Remote Sensing of Environment 86,83–107.

Wooster, M.J., Roberts, G., Perry, G.L.W., Kaufman, Y.J., 2005. Retrieval ofbiomass combustion rates and totals from fire radiative power observa-tions: FRP derivation and calibration relationships between biomassconsumption and fire radiative energy release. Journal of GeophysicalResearch 110, D24311.

Yokelson, R.J., Christian, T.J., Karl, T.G., Guenther, A., 2008. The tropical forest andfire emissions experiment: laboratory fire measurements and synthesis ofcampaign data. Atmospheric Chemistry and Physics 8, 3509–3527.