Science of the Total Environment 739 (2020) 139755

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Science of the Total Environment

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Estimating the air quality and health impacts of biomass burning innorthern South America using a chemical transport model

Karen Ballesteros-González a, Amy P. Sullivan b, Ricardo Morales-Betancourt a,⁎a Department of Civil and Environmental Engineering, Universidad de Los Andes, Bogotá, Colombiab Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

H I G H L I G H T S G R A P H I C A L A B S T R A C T

• WeusedWRF-Chem tomodel the trans-port of Biomass Burning plumes inNorthern South America.

• Regional aerosol background concentra-tions are dominated by biomass burningduring the dry season.

• SOA contributes a substantial fraction ofbiomass burning smoke in the region.

• Regional Ozone background levels arestrongly impacted during the Biomassburning season.

• Nearly 170 excess deaths can be associ-ated with biomass burning PM2.5 andO3 increases during February, often themost affected by fires.

⁎ Corresponding author.E-mail address: r.moralesb@uniandes.edu.co (R. Mora

https://doi.org/10.1016/j.scitotenv.2020.1397550048-9697/© 2020 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 February 2020Received in revised form 22 May 2020Accepted 25 May 2020Available online 5 June 2020

Editor: Pavlos Kassomenos

Biomass burning (BB) emissions significantly deteriorate air quality in many regions worldwide, impact humanhealth and perturbing Earth's radiation budget and climate. South America is one of largest contributors to BBemissions globally. After Amazonia, BB emissions from open and agricultural fires of Northern South America(NSA) are the most significant. Recent evidence shows a strong correlation between fire counts in NSA andBrown Carbon in some Colombian cities, suggesting a substantial seasonal contribution of regional BB sourcesto air pollution levels in the densely populated areas of NSA. In this workwe use the atmospheric regional chem-ical transport model WRF-Chem to assess the contribution of open BB events to pollutant concentration and toestimate potential health impacts associated with wildfire events in NSA. Three nested domains are used to sim-ulate atmospheric composition in the Northern part of South America and the Caribbean. Simulations includedbiogenic and anthropogenic emissions from a global emission inventory merged with local emissions for thecity of Bogotá. Twomodelling scenarioswere considered, a base casewithout BB emissions (NO_FIRE) and a sen-sitivity scenario with BB emissions. Simulations were carried out for periods of strong BB activity in NSA. In theNO_FIRE scenario, aerosol concentrations are unrealistically low.When BB emissions are is included backgroundPM2.5 concentrations increase 80%. The increment in aerosol concentrations is mainly driven by Secondary Or-ganic Aerosols. In the case of Bogotá, the most densely populated city in the domain, monthly mean increasein PM2.5 is 3.3 μgm−3 and 4.3 ppb for O3. Modeledmeteorological and air pollution fields are in better agreementwith observations when high spatial resolution (3 × 3 km) is used in the simulations. The total estimated short-term all-cause mortality associated to BB during February in the region is 171 cases, 88 PM2.5-related and 83 O3-related mortality.

© 2020 Elsevier B.V. All rights reserved.

Keywords:Biomass burning emissionsRegional atmospheric transportAir pollutionBiomass burning aerosolHealth impactsSecondary organic aerosols

les-Betancourt).

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1. Introduction

Open and agricultural fires are one of themost important contrib-utors to primary emissions to the atmosphere, with global, regional,and local impacts on air quality (e.g., Jacobson, 2014; Reddingtonet al., 2015), public health (e.g., Johnston et al., 2014; Linares et al.,2015; Yao et al., 2016), and climate (e.g., Jacobson, 2014). Biomassburning (BB) emissions are significant sources of primary organicaerosols (POA), elemental carbon (EC), carbon monoxide (CO), ni-trogen oxides (NOx) as well as volatile organic compounds (VOCs),that can react in the plume and with the surrounding atmosphereto form secondary pollutants such as tropospheric ozone (O3) andsecondary organic aerosols (SOA) (e.g., Crutzen and Andreae, 1990;Fearnside et al., 1993; Akagi et al., 2012; Bond et al., 2004; Granieret al., 2000; Majdi et al., 2019).

Open fires in the tropics are the most important contributors toglobal BB emissions (e.g., van der Werf et al., 2006, 2010). About 15%of the global BB emissions can be attributed to open fire events inSouth America (van der Werf et al., 2010). Although, most of theseevents occur on the Amazon rainforest and the Cerrado region inBrazil, a large number of wildfires occur in the grassland savannas ofNorthern South America (NSA) every year (Andreae, 2019; van derWerf et al., 2006, 2010). The most intense biomass burning activity inNSA occurs during the dry season in the region, typically from Novem-ber to April (Mendez-Espinosa et al., 2019), a markedly different annualBB activity than in Amazonia. Despite the known significance of BBemissions in Northern South America, little is known about the magni-tude and extent of its negative impacts on air quality and health. Due tothe high degree of urbanization in the region, most of the air qualitystudies carried out in South America have focused on urban air quality,often neglecting contributions from regional sources such as open BB,power generation, and long-range transport of mineral dust. However,recent works have compiled evidence suggesting a strong correlationbetween the number of fires in NSA and deteriorated ambient air qual-ity in some Colombian cities. Mendez-Espinosa et al. (2019) usedMODIS active fire data combined with HYSPLIT back-trajectories toshow that anomalously high PM and CO levels occurred when airmasses originated from the Orinoco grasslands during the times whenthe number of active fires in the region was largest. Hernandez et al.(2019) based on in-situ observations and back-trajectory analysisshowed an association between PM10 and fires in the Orinoco Basin. Re-cent observations of Brown Carbon and other BB tracers identified astrong association between those tracers and regional fire counts atdaily, weekly, and monthly time-scales Rincón-Riveros et al. (2020).These studies have shown the significant potential of BB aerosols to af-fect air quality in the densely populated cities located hundreds of kilo-meters away from the BB sources. However, no estimates of thecontribution of BB emissions to air pollution or themagnitude of poten-tial health impacts in NSA have been quantified.

Exposure to air pollution has been firmly associated to negativehealth outcomes, such as respiratory diseases, cardiovascular diseases,and decreased lung function (Kim et al., 2015). Negative health effectsand increasedmortality has been linkedwith increases in concentrationof O3 (Brook et al., 2002), CO (Bell et al., 2009), NO2 (Latza et al., 2009),and particulate matter (Brook et al., 2002; Garrett and Casimiro, 2011;Di et al., 2017). Epidemiological studies focused on exposure to BBsmoke have shown that it is harmful to human health (Reid et al.,2016). Gases and particles emitted during biomass combustion contrib-ute to the exacerbation of respiratory conditions such as asthma,chronic obstructive pulmonary disease (Johnston et al., 2014; Yaoet al., 2016), and respiratory infections (Yao et al., 2016). Other studieshave determined a contribution of BB smoke to excess number of all-cause, respiratory, and cardiovascular mortality cases (Johnston et al.,2011; Nunes et al., 2013; Linares et al., 2015). More significant effectshave been observed for fine particles (Pope-III et al., 1995; Shah et al.,2013). It is estimated that approximately 3% and 5% of deaths from

cardiopulmonary diseases and lung cancer, respectively, are attribut-able to particulate matter (WHO, 2013). As BB emissions might con-tribute significantly to aerosol concentrations, it is important toquantify the impacts of open and agricultural fires over air pollutionlevels and its associated health impacts. Therefore, in this study, weaim to use chemical transport modelling to estimate these effectsduring the peak of BB season.

Contribution from open fires to O3 and PM have been quantifiedin numerous studies using photochemical transport models(e.g., Fann et al., 2018; Rappold et al., 2017; Cuchiara et al., 2017;Baker et al., 2016). Using a modelling approach together with obser-vations allows amore complete spatial coverage of pollutant impacts(Fann et al., 2012), specially over regions where air quality monitor-ing is limited and spatially scarce. This approach provides quantita-tive estimates of BB contributions from different regions andsources (Chan, 2017) allowing to assess BB emissions impacts onair quality and public health in NSA.

We use the Weather Research and Forecasting Model coupled withChemistry (WRF-Chem) to perform regional-scale chemical transportmodelling simulations to establish the contribution from open BB tothe PM2.5 and O3 concentrations over a large domain in NSA. The nu-merical simulations are used to estimate potential health impacts asso-ciated with wildfire events. This is particularly challenging given thecomplex topography of the region and the compact and dense natureof its urban areas. The most populated urban areas in the domain areoften at heights above 2000 m in the Andes mountains, or in mountainvalleys. These cities, are not only highly populated but have extremelyhigh population density, for instance, Bogotá has a population densityof 24,000 people per km2 (e.g. Wheeler, 2015). The compact nature ofthese cities combined with the high primary local emissions associatedwith traffic and industry suggest that high resolution simulations arenecessary to capture both, the meteorological fields and the air qualitylevels in a representative way.

2. Methods and materials

The WRF-Chem version 4.1 “On-line” regional atmospheric andchemical transport model was used to simulate atmospheric composi-tion and to assess the spatial distribution of BB aerosols and gas-phasespecies. The model solves continuity equations, accounting for atmo-spheric transport, wet and dry deposition, emissions, gas-phase chemis-try, photolysis, radiation, and organic and inorganic aerosol species thatdetermine concentrations for gas-phase and aerosol species (Wanget al., 2017; Peckham et al., 2018). Detailed description of themodel dy-namical core and other specific details are reported elsewhere(e.g., Grell et al., 2005).

2.1. Model configuration

Biomass burning emissions in the region are typically located hun-dreds of kilometers away from the highly populated Andean cities(Fig. 1a–c). Therefore, a large modelling domain is necessary in orderto include all the relevant BB sources and account for regional transport.However, given the complex topography in the region (Fig. 1b) and thecompact configuration of the urban areas within the domain (Fig. 1c),high spatial resolution is required to resolve the fine-scale details asso-ciated with the granularity of population density and terrain height fea-tures. To this end, we configured WRF-Chem with three nestedsimulation domains of increasing spatial resolution (Fig. 1). Domainsare centered in Colombia (lat. 5.194°, long. −73.522°) covering thenorthern half of South America and the Caribbean. The horizontal reso-lution for the outer-most domain, D01, is 27 × 27 km, formed by 120 ×120 cells. The nested domains (D02 andD03) have horizontal resolutionof 9 × 9 km and 3 × 3 km, respectively, with 126 × 126 and 132 × 132grid cells for each domain. Domains D02 and D03 were designed tocover the most populated cities over the Andes mountains. A total of

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Fig. 1.Geographic location of the study area. a) Biomass burning emissions in the larger domain, D01, for February 2010 and February 2018. The grasslands of the Orinoco river basin andthe Amazon Rainforest are indicated. b) Topographic map and locations of nested domains D02 and D03. c) Population distribution in domains D02 and D03.

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41 sigma pressure vertical levels from surface to 50 hPa at the top of themodelwere used for all domains, and 14vertical levelswere allocated inthe first kilometer. Meteorological initial and boundary conditions forthe larger domain, D01, were taken from the National Centers for Envi-ronmental Prediction Final Operational Global Analysis (NCEP-FNL),which has a spatial resolution of 1° × 1°, and 6-hourly temporal resolu-tion (NCEP, 2000). The initial and boundary conditions were spatiallyand temporally interpolated to the modelling domains and modeltime step, respectively, using the WRF pre-processing system (WPS).The boundary and initial conditions for D02 and D03 were passeddown from the WRF-Chem simulation in the parent domain.

MODIS 20-category land use database was utilized as it accuratelyrepresents the urban area of Bogotá and the vegetation cover in subur-ban areas (Nedbor-Gross et al., 2017). The Advanced Spaceborne Ther-mal Emission and Reflection Radiometer, ASTER, topographic data(Abrams, 2000)was used in our simulations as it was shown to improvemodeledmeteorological variables due to themore accurate representa-tion of terrain height in the region of study (Nedbor-Gross et al., 2017).

The physical and chemical parameterizations used in themodel con-figuration are summarized in the Supplementary Material (Table S1).Model configuration was based on preliminary evaluations of modelperformance. Gas-phase chemistry is treated with the Model forOzone and Related Chemical Tracers chemical mechanism, MOZART(Emmons et al., 2010). MOZART includes 85 gas-phase and 12 aerosolspecies. Aerosols are described with the two-moment sectional aerosolscheme Model for Simulating Aerosol Interactions and Chemistry aero-sol scheme, MOSAIC (Zaveri et al., 2008). We used the four size-binsconfiguration of MOSAIC, which spans particle sizes from 0.039 μm to10.0 μm. This aerosol scheme was selected over a bulk scheme to betterunderstand the impact of BB over the aerosol size distribution (Fastet al., 2006; Zaveri et al., 2008; Archer-Nicholls1 et al., 2015). SOA for-mation is calculated through a simplified parameterization that usesambient ageing measurements of organic aerosols (Hodzic andJimenez, 2011). This scheme has been shown to represent SOA forma-tion well and is less computationally expensive than the VolatilityBasis Set approach (Robinson et al., 2007). We applied chemicalboundary conditions for the outer-most domain from the CommunityAtmosphereModel with Chemistry (CAM-chem) globalmodel at 6 h in-tervals. CAM-Chemhas a grid spacing of 1.9° × 2.5° and 56vertical levelsextend to 4 hPa (Lamarque et al., 2012; Emmons et al., 2010). The CAM-Chem fields were interpolated to D01 spatial resolution by the mozbctool (available at https://www2.acom.ucar.edu/wrf-chem/wrf-chem-

tools-community). Boundary and initial conditions for gas-phase andaerosol species for domains D02 and D03were obtained from the corre-sponding parent domain, and the chemical initial conditions were fromthe last time step of the previous run.

2.2. Emissions

Anthropogenic emissions from the Emissions Database for GlobalAtmospheric Research, EDGAR version 4.3.1, were mapped to the simu-lation domains (Crippa et al., 2016). EDGAR has a spatial resolution of0.1° × 0.1°. We applied diurnal variation profiles to the emissionsfrom manufacturing, transformation industry, and road transportationsectors. For Bogotá city limits, we merged emissions from a local emis-sion inventory with those of EDGAR. Aerosol emissions from commer-cial, industrial, and mobile sectors were used from the local inventory(Pachón et al., 2018). Local re-suspension from paved and unpavedroads emissions have been shown to be relevant in the city of Bogotá(Pachón et al., 2018). However, despite of its contribution, model runssuggest that road dust emissions are overestimated. Therefore, we ap-plied a reduction factor, and used 20% of the PM emitted by re-suspension from paved and unpaved roads. Biogenic emissions fromMEGAN - Model of Emissions of Gases and Aerosols from Nature(Guenther et al., 2012) were included in all simulations. MEGAN has 1km × 1 km spatial resolution and includes emissions of 150 specificcompounds (Guenther et al., 2012). Some MEGAN emitted specieshave a one-to-one correspondence with model species (e.g., isoprene,formaldehyde, ethanol,methanol, ethene, propane, propene), but largerorganic species are mapped to lumped MOZART species for alkanes, al-kenes, and aromatic compounds (Emmons et al., 2010). Fire emissionswere taken from the Fire INventory from NCAR, FINN version 1,(Wiedinmyer et al., 2011). FINN uses MODIS active fire data, landcover, and specific emissions factors to calculate spatially distributedemissions from wildfires, agricultural fires, and prescribed burns. FINNhas 15 gas-phase organic species and several primary particle speciesand are mapped to MOZART according to Wiedinmyer et al. (2011).The temporal resolution for BB emissions from FINN is daily and spatialresolution 1 km × 1 km.

2.3. Experimental design

The intensity of BB emissions in the region is typically higher duringthe November–April dry season, with maximum activity between

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February andMarch (Mendez-Espinosa et al., 2019). Therefore, to assessthe impact of regional wild-land and agricultural fires over atmosphericchemical composition in NSA two periods of high BB emissionswere se-lected for simulations. These periods are February 2010, and February2018. The first period, associated with a strong El Niño, had high BB ac-tivity over the grasslands in the Orinoco Basin. This was confirmed byabove-average fire counts reported by the Fire Information for ResourceManagement System (FIRMS) and by higher-than-seasonal PM10 con-centrations reported at local Air Quality Monitoring Network of Bogotá(Mendez-Espinosa et al., 2019). February 2018 simulation period hadhigh fire activity over the Amazon Rainforest in the borders betweenColombia, Ecuador, and Peru. Additionally, detailed PM2.5 chemical spe-ciation information is available for that period (Rincón-Riveros et al.,2020), allowing for an evaluation of themodeled aerosol chemical com-position. A base-case scenariowithout BB emissions (NO_FIRE scenario)was completed for both simulation periods. BB emissionswere includedin a sensitivity scenario (FIRE scenario). The contribution of BB emis-sions over surface PM2.5 andO3 levels is computed as the differences be-tween the FIRE and NO_FIRE simulations. Numerical experiments werecarried out from 01 February 00 h UTC to 28 February 00 h UTC. An ad-ditional sensitivity test was carried out to estimate uncertainties associ-ated to different BB emission inventories. This was done by running asecond FIRE scenario with emissions from the Global Fire Emissions Da-tabase version 4 with small fires, GFED4s (van der Werf et al., 2017).GFED4s has spatial horizontal resolution of 0.25° × 0.25° and a temporalresolution of 1 h. GFED4s, like FINN, uses burned area to estimate its fireemissions but GFED4s uses MODIS Collection 5.1 Burned Area Product(Levy et al., 2010) to estimate BB emissions (van der Werf et al., 2017).

2.4. Evaluation of model performance

Model performance was evaluated by comparing simulation resultsagainst in-situ observations in Bogotá. Benchmark criteria were appliedfor meteorological fields, PM2.5 concentration, and O3 concentration(Emery et al., 2001, 2017). Mean bias (MB), Normalised mean bias(NMB), Root mean squared error (RMSE), correlation coefficient (r),and Index of Agreement (IOA) were calculated by comparingmodel re-sults and observations over Bogotá (Supplementary Materials S9, S10).

Chemically speciated PM2.5 samples collected during the 2018 dryseason at two sampling sites located in Bogotá were used to assess themodeled aerosol composition. The sampling site locationswere selectedat suburban areas, one, at hill-top site in the Monserrate Sanctuary, andthe other at the Hacienda Noviciado (Supplementary Material S2).These sampling locations were selected based on prevalent winddirection to observe air masses arriving to the city from the East sideof the region (Mendez-Espinosa et al., 2019). Samples were collectedon quartz filters and analyzed at the Collett laboratory in ColoradoState University for Organic carbon (OC), Elemental Carbon (EC), aswell as Water Soluble Organic Carbon (WSOC), and inorganic ions. OCand ECwere determinedwith a thermal/optical transmission EC/OC an-alyzer (Sunset Labs Inc.). Aqueous extracts from the filters wereanalyzed for inorganic ions and cations, including Sodium, Ammonium,Magnesium, Calcium, Sulfate, Nitrate, and Nitrite. For these analyses aDionex DX-500 ion chromatograph with a Dionex CD-20 conductivitydetector and a Dionex IP-20 isocratic pump and self-regeneratingcation/anion SRS-ULTRA suppressor were employed. Cations wereseparated using a Dionex IonPac CS12A analytical column with aflowrate at 0.5 mL min−1 of 20-mM methanesulfonic acid eluent. Foranions, a Dionex IonPac AS14A anion-exchange column was employedand operated at a 1mLmin−1 flowrate of carbonate/bicarbonate eluent(Sullivan et al., 2008). Detailed descriptions of the sampling sitelocations and the chemical analysis performed can be found inRincón-Riveros et al. (2020) and SupplementaryMaterial (S2). To assessthe spatial distribution of modeled aerosols in our simulations, themodeled aerosol optical depth (AOD) at 550 nm was compared toMERRA-2 reanalysis (Gelaroa et al., 2017).

2.4.1. Health impactsThe sensitivity simulation results were used to determine the frac-

tion of PM2.5 and Ozone attributable to open BB emissions in the region.Based on epidemiological studies and the excess PM2.5 apportioned toBB emissions,we quantified diverse short-termhealth effects associatedwith BB. The extent of health effects were estimated using log-linearconcentration response functions (CRF). A log-linear CRF, often usedin epidemiological studies, relates the number of excess cases associatedwith a specific health endpoint, Δy, to an excess in concentration ofgiven air pollutant, Δx. The CRF in this study is Δy = y0(1 − e−βΔx),where y0 is the baseline number of cases of the health endpoint, and βis a parameter related to the relative risk. We analyzed the short-termPM2.5-related all-cause, cardiovascular, and respiratory mortality fol-lowing data reported in a recent analysis (Liu et al., 2019). We also esti-mated Chronic Obstructive Pulmonary Disease (COPD) cases due toPM2.5, and calculated the increase in emergency visits associated to re-spiratory symptoms in the most populated Colombian cities (Gonzálezet al., 2017). Baseline incidence rates for COPDmortality cases classifiedby gender and age were taken from Global Burden of Disease Study2013 (IHME, 2015). Baseline incidence for other health effects were ob-tained from the Colombian Department of Statistics annual report(DANE, 2018). Population density data was obtained from the GriddedPopulation of the World, Version 4 (GPWv4), provided by NASA'sSocio-economic Data and Applications Center. When estimates werecomputed for the specific case of Colombia, data from a 2018 censuswas used. It was assumed that BB smoke has the same toxicity asPM2.5 from other sources. Details of epidemiological studies and CRF pa-rameters used to estimate the health impacts can be found in Supple-mentary Material (S6).

3. Results and discussions

3.1. Model performance

The modeled meteorological fields for the simulations with coarserresolution, i.e., domains D01 and D02, failed to meet most performancecriteria when evaluated against observations in Bogotá. Although airtemperature, relative humidity, wind speed, and wind direction inD01 simulations captured the diurnal cycles, there are persistent biasesin thesefields, with ameanbias of−2.8 and−3.9 K for temperature, 2.0and 2.1 g kg−1 for specific humidity, 1.0 and 1.6 m s−1 for wind speed,and 52.5° and 33.5° for wind direction during February 2010. Similarmetrics were observed during February 2018 (see Supplementary Ma-terial S9). In the coarse resolution simulations, temperature is consis-tently underpredicted, causing a corresponding overprediction ofrelative humidity. For those simulations, wind speed is alsooverpredicted when compared to observations, implying a shortermodeled residence time of air masses over Bogotá, driving down themodeled PM2.5 concentrations (Fig. 2b). Similarly, simulations withthe D01 and D02 horizontal resolution capture temporal variations ofair pollutant concentrations, but PM2.5, NOx, SO2, and CO, areunderpredicted. The underprediction in NOx likely explains the night-time overprediction of O3 for both simulations periods.

However, when higher spatial resolution was used in the simula-tions (3 km × 3 km), the modeled meteorological fields improved sig-nificantly meeting performance criteria for most of the assessedvariables (see Supplementary Material S9). This resolution-driven im-provement in atmospheric fields has been observed in other studies(e.g., Crippa et al., 2017; Spiridonov et al., 2019). When horizontal reso-lution is increased, the small-scale local circulations induced by thecomplex topography are better captured (Fig. 2a). In the case of Bogotá,located in a high plateau in the Central Andes, the hills reaching up to3000 m in the Eastern margin of the plateau are completely absent inthe D01 and D02 domains, and are only captured at the 3 km horizontalresolution. Additionally, the compact urban layout of the city is bettercaptured at the 3 km × 3 km resolution. Particular improvements are

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036912151821242730333639424548515460

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PM2.5 NO_FIRE (Feb. 2010) PM2.5 FIRE (Feb. 2010) 2.5 (Feb. 2010)

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Fig. 3.Modeled ground-levelmonthly-mean PM2.5 concentration for NO_FIRE and FIRE scenarios using FINN emissions, andΔPM2.5 for both simulation periods. Panels a), b), and c) are forFebruary 2010, and panels d), e), and f) are for February 2018.

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seen in wind direction with a mean bias of ≤10.0° for both simulationperiods. There was also significant reduction in wind speed overpredic-tion, with a mean bias of 0.7 m s−1, and a decrease in air temperatureunderprediction. Even though high spatial resolution improves meteo-rological fields' representation, there are still some of the benchmarkcriteria values that the model does not meet.

Modeled ground-level PM2.5 was biased low compared to the ob-served 26.6 μg m−3 monthly mean over Bogotá for February 2018. Thebias was reduced as the horizontal resolution was increased. For theFIRE simulation the monthly mean concentrations of PM2.5 were 19 μgm−3, 20 μg m−3, and 23 μgm−3 for domains D01, D02, and D03 respec-tively (Fig. 2b). The overestimation of wind speed could explain some ofthe PM2.5 underprediction. However, this persistent underpredictioncould also indicate missing emissions in the local emission inventory.The evaluation of chemical performance of themodel for PM2.5 showedlow correlation between the model and observations. A full evaluationof model performance is shown in Supplementary Material (S10).

3.2. Biomass burning contribution

Monthly mean surface PM2.5 and O3 concentrations over NSA forboth simulation periods are shown in Figs. 3 and 4, respectively. PM2.5

concentrations in the NO_FIRE simulation show a Southeast to North-west concentration gradient. Low background aerosol concentrationsare seen over Amazonia and higher concentrations over the Andesmountains and the coastal areas. This distribution is consistent withthe locations of themost populated urban areas andwith anthropogenicemissions (Fig. 1c). The maximummonthly mean concentration in thedomain is 19.5 and 20.3 μg m−3 for PM2.5 for February 2010 andFebruary 2018, respectively. The 8-hour maximum O3 concentrationshows similar features to those of ground level PM2.5, with higher

O3 NO_FIRE (Feb. 2010) O3 FIRE (Feb. 20a). b).

3 NO_FIRE (Feb. 2018) O3 FIRE (Feb. 201d). e).

O

Fig. 4. Modeled ground-level 8 h-maximum O3 concentration for NO_FIRE and FIRE scenariosFebruary 2010, and panels d), e), and f) are for February 2018.

concentrations over the Andes for the NO_FIRE simulations (Fig. 4aand d). The largest 8-hour O3 maximum in the domain is 59.1 and57.7 ppb for February 2010 and February 2018, respectively.

When BB emissions are included, aerosol background concentra-tions in the region increase substantially (Fig. 3b and e). Domain meanPM2.5 surface concentrations over land for the NO_FIRE simulationsare 3.2 μg m−3 for both simulations periods. When BB emissions are in-cluded, the domain average PM2.5 concentration increases by 53% and109% for February 2010 and February 2018, respectively. The impactof BB emissions on 8-hour maximum O3 is an increase of 20% in theFIRE scenario for both simulation periods.

Surface PM2.5 and O3 attributable to BB emissions, i.e., ΔPM2.5 andΔO3, are computed by subtracting the FIRE and NO_FIRE simulations(Figs. 3c, f, and 4c, f). The increase in background PM2.5 from BB emis-sions is partially explained by the flux of primary particle emissions.For February 2010, FINN emissions totaled 0.03 Tg for EC and 0.18 Tgfor OC, accounting for 86% and 93% of the total OC and EC primary emis-sions in D01 respectively. During the 2018 BB season, the locations offires was different, with considerable BB emissions in the Amazonrainforest, in the South of the simulation domain (Fig. 3b). During Feb-ruary 2018 BB emissions in D01were 0.06 Tg for EC (91% of the total ECemissions) and 0.32 Tg of OC (96% of total OC emissions) according toFINN (Table 1). This is consistent with what has been observed inother studies (e.g., Aouizerats et al., 2015; Chan, 2017). In our simula-tions with FINN emissions, however, SOA was found to account formost of ΔPM2.5 (Section 3.3).

The spatial distribution ofΔPM2.5 andΔO3 is dictated by the locationof BB emissions. The emission fluxes show significant inter-annual var-iability (February 2010 and February 2018). For the 2010 period, whenwildland fires occur over the grasslands of the Orinoco Basin, BB emis-sions impact mostly the north of the domain (Figs. 3b, and 4b). During

10) O3 (Feb. 2010)

c).

[ppb] [ppb]

8) O3 (Feb. 2018)

f).

[ppb] [ppb]

047111418212528323539

46495356606367

42

047111418212528323539

46495356606367

42

Δ

Δ

-60

-25

-15

-10

-3

-1

-.5

0

.5

1

3

10

15

25

60

-60

-25

-15

-10

-3

-1

-.5

0

.5

1

3

10

15

25

60

FINN emissions

FINN emissions

1.8 ppb

2.7 ppb

using FINN emissions, and ΔO3 for both simulation periods. Panels a), b), and c) are for

Table 1Biomass burning emissions in the Northern South America simulation D01 for February2018 for FINNv1, GFED4s, and GFAS.

Species FINNv1 GFED4s GFAS

[Tg month−1] [Tg month−1] [Tg month−1]

PM2.5 0.14 0.20 0.19EC 0.06 0.76 0.01OC 0.32 0.08 0.10SO4 0.01 – –NOx 0.22 0.09 0.06SO2 0.02 0.04 0.02NH3 0.06 0.01 0.02VOCs 1.50 0.31 0.45

0.53 Tg month-1

0.0

0.2

0.4

0.7

1.3

2.4

4.5

8.3

15.5

28.8

53.7

[kg m month ]-2 -1

1E-4

FINN Aerorol Emissions

1.04 Tg month-1

0.0

0.2

0.4

0.7

1.3

2.4

4.5

8.3

15.5

28.8

53.7

[kg m month ]-2 -1

1E-4

GFED4s Aerosol Emissions

a). b).Δ ΔPM (FINN - GFED4s)2.5 (FINN - GFED4s)3O

[μg m ]-3

-50-25-15-10-3-1-.50.51310152550

-60-25-15-10-3-1-.50.51310152560

[ppb]

a). b).

c). d).

Fig. 5. a) FINN, and b) GFED4s aerosol emissions over D01 during February 2018.Differences between simulations with FINN and GFED4s fire emissions are shown forground-level c) PM2.5, and d) O3.

7K. Ballesteros-González et al. / Science of the Total Environment 739 (2020) 139755

2018, a much larger ΔPM2.5 and ΔO3 are observed over the south of thedomain (Figs. 3f, and 4f). During that BB season, the largest BB emissionfluxes occurred in the rainforests in the boundary between Ecuador,Colombia and Peru. These modelling results show that BB emissions in-creases PM2.5 over most of the domain. However, the percent contribu-tion to air pollutants concentration in urban areas is lower. This is due tothe large emission fluxes of traffic, industrial and other non-BB emis-sions in urban areas, which leads tomuch higher aerosol concentrationsover these densely populated cities. In the case of Bogotá, monthlymean ΔPM2.5 is 2.8 μg m−3 (12% increase) and 4.1 μg m−3 (17% in-crease) for February 2010 and February 2018, respectively.

Table 1 shows the large variations in estimated BB emissions overNSA across emission inventories GFED4s, FINN, and the Global Fire As-similation System (GFAS) emission inventory (Kaiser et al., 2012).Therefore, to estimate uncertainties stemming from the use of differentemission inventories, a sensitivity scenario using the GFED4s BB emis-sions was simulated for February 2018. GFED4s primary aerosol emis-sions were larger than those of FINN over NSA for the periodconsidered, but total VOC emissions in FINN are larger than in other in-ventories (Table 1). The spatial allocation of emissions between FINNand GFED4s varies significantly with emissions in the grasslands be-tween Venezuela and Colombia larger in GFED4s than in FINN (Fig. 5aand b). Both datasets show a hot-spot of emissions over the Amazonianregion in the Colombo-Ecuadorian border, with FINN assigning largeremission fluxes in that area (Fig. 5a and b). For GFED4s scenario,ΔPM2.5 = 2.7 μg m−3 and ΔO3 = 1.12 ppb, which are lower thanthose predicted with FINN emissions. However, the difference in thetotal emission fluxes and its distribution causes ground level ΔPM2.5

and ΔO3 to have different patterns over the domain. GFED4s simulationis shown to cause larger ΔPM2.5 and ΔO3 on the northeastern area inNSA, while FINN simulated impacts are larger in the western side ofthe domain, along the Andes mountains (Fig. 5c and d).

We used AOD at 550 nm from MERRA-2 reanalysis to evaluate thespatial horizontal distribution of modeled aerosol concentrations(Fig. 6). For the GFED4s simulation experiment, the magnitude of AODis underpredicted, and does not reproduces the plume over southernColombia and northern Ecuador for February 2018. The simulationwith FINN emissions has a similar AOD spatial distribution as theMERRA-2 reanalysis. These results further suggest that simulationusing FINN BB emission inventory better captures the satellite observedbackground concentrations of the NSA during dry season.

3.3. PM2.5 chemical composition

The observed PM2.5 chemical composition in Bogotá during February2018 was dominated by organic matter (OM), with 39% of PM2.5 mass(Fig. 7c). Unspeciated PM2.5 (OTH) accounts for 41%, making those themajor components. EC accounted for 5% of total PM2.5 for the observa-tion period. These results are consistent with published studies onPM10 composition in the city, which has been shown to have strongpresence of OC and EC (e.g., Ramírez et al., 2018a, 2018b). It is likely

that the actual EC fraction is higher within the urban area of Bogotá, asboth sampling sites are located outside the urban perimeter. Inorganiccompounds, including sulfate (SO4), nitrate (NO3), and ammonium(NH4) are 15% of fine aerosol mass.

PM2.5 composition in the FIRE simulation shows a higher contribu-tion from primary and secondary organic aerosols, with an overall or-ganic contribution that is closer to the observed chemical composition(Fig. 7a, b). However, even in the FIRE scenario the modeled organicaerosol fraction is still underpredicted. For the 2018 period, the excessΔPM2.5 over Bogotá is 17%, and this increase is largely dominated bySOA. The percent contribution to ΔPM2.5 is distributed as SOA 47%,POA 18%, EC 4%, and inorganic compounds 31%. The BB SOA fractionover some cities represents 29% of the total OM. Such large organic frac-tion has been reported in other studies (e.g., Paglione et al., 2019;Aouizerats et al., 2015). SOA formation is controlled by oxidation ofgas-phase VOCs followed by partitioning to the particle phase (Hodzicand Jimenez, 2011). Therefore, this large SOA contribution in the FIREsimulations is likely explained by the OH radical availability and highsolar radiation levels (Lelieveld et al., 2016; Lim et al., 2019). The in-crease in inorganic aerosols is shared between NO3 (12%), NH4 (5%),SO4 (3%), and unspeciated aerosols (11%). The increase in inorganicaerosols is driven by excess gas-phase precursors in the FIRE scenarios,mainly NH3, NO, and SO2 (Squizzato et al., 2013) as well as by primarysulfate BB emissions.

3.3.1. Estimates of health impactsCombining the simulated ΔPM2.5 field resulting from the sensitivity

analysis with log-linear CRF parameters from epidemiological studies(Liu et al., 2019; González et al., 2017), we estimated several health out-comes associated with BB emissions. Using detailed population andmortality data from the 2018 census in Colombia and CRF parametersβ from Liu et al. (2019), the total February monthly mean PM2.5-related all-cause mortality (β=0.68± 0.11%/10 μgm−3) over the coun-try attributable to BB emissions is estimated to be 52 excess cases. For

550nm

a).WRF-Chem - FINN MERRA-2WRF-Chem - GFED4s

b). c).

AOD0.00 0.09 0.18 0.26 0.35 0.44 0.53 0.61 0.70 0.80

Fig. 6. February 2018monthlymean comparison of Aerosol Optical Depth (AOD) at 550 nmbetween a)WRF-Chem simulation results with GFED4s BB emissions inventory, b)WRF-Chemsimulation with FINN BB emissions inventory, and c) MERRA-2 reanalysis products.

8 K. Ballesteros-González et al. / Science of the Total Environment 739 (2020) 139755

cardiovascular (β=0.55 ± 0.10 %/10 μgm−3) and respiratory mortality(β=0.74 ± 0.2 %/10μgm−3), the number of excess cases over a typicalFebruary is 12 and 7 cases, respectively (Fig. 8).When this calculation isrepeated over the full NSA domain, using the GWP v4 population data,the estimated excess all-cause deaths is 88 cases. Ozone related healthoutcomes are computed with the CRF parameters of Bell et al. (2005).The February monthly mean O3-related all-cause mortality (β=0.87± 0.31%/10 ppb) over the NSA domain is 83 excess deaths, and 46over Colombia alone. For cardiovascular mortality (β=1.1 ± 0.42 %/10ppb) and respiratory mortality (β=0.47 ± 1 %/10 ppb), the number ofexcess cases over a typical February is 16 and 3 cases, respectively. Toaccount for inter-annual variability in concentrations we used averageΔPM2.5 and ΔO3 from the 2010 and 2018 simulation periods.

Given the high degree of urbanization in Latin America, with 75% ofthe population living in urban areas, we estimate these endpoint mor-tality cases over the most populated cities in Colombia, for which de-tailed data was available (Table 2). Bogotá is the city with the highestpopulation density in the region and represents approximately 11% ofthe total cases due to BB related PM2.5 excess in Colombia. Medellínwith approximately 9% follows as the second most affected city. The

PM2.5 FIREPM2.5 NO_FIRE (Feb. 2018)a). b).

Fig. 7.Monthly mean PM2.5 at ground level and modeled PM2.5 chemical speciation over four cBUC, a) NO_FIRE, and b) FIRE scenarios during February 2018. c) Observed PM2.5 chemical spe

large impact overMedellín could be caused by the high ΔPM2.5 that im-pacts the city during February and the high population density. Our sim-ulation results agree with other studies that identified Bucaramanga,located closer to the sources, as potentially being affected most by BBsmoke.

There is also an expected increase in emergency hospital visits re-lated to respiratory cases in themain cities in the country. There is an es-timated 8.2% increase in emergency visits due to respiratory disease foreach 5 μgm−3 increase in PM2.5 (González et al., 2017). According to ouranalysis, the ΔPM2.5 attributable to BB implies 3700 more emergencyvisits in Bogotá, 1135 in Medellin, and around 183 in Cali, during themonth of February.

There is an expected increase in all-age mortality cases from COPDassociated with BB aerosol. PM2.5-related impacts during February ac-count for 11 (95% CI: 4, 16). February monthly mortality health out-comes over NSA season by gender and age group were also calculated(Supplementary Material S6). This monthly mean estimate shows thatmales were particularly affected by the endpoint health outcomes stud-ied with about 54% of total mortality cases, and people over 50-years ofage are affected the most with 95% of total excess deaths.

036912151821242730333639424548515460

[µg m-3]

41% 39%

5%9%3%3%

POMSOMECSO4NO3NH4OTH

(Feb. 2018)c).

ities located at high altitude Bogotá - BOG, Medellin - MED, Cali - CAL, and Bucaramanga -ciation in Bogotá (dry season 2018).

> 3.00.7 - 2.90.3 - 0.60.1 - 0.20.0

Mortality Cases

Fig. 8. Total February monthly mean PM2.5-related all-cause mortality cases estimation inColombia due to BB events over NSA.

9K. Ballesteros-González et al. / Science of the Total Environment 739 (2020) 139755

The PM2.5-related endpoint cases follow closely the population dis-tribution across NSA. The most impacted areas are the Andes region,where themost populated cities are located. Due to its high population,Bogotá is where more cases are expected to occur (Wheeler, 2015), al-though it is not located in the areas of higher surface PM2.5 increases.In Colombia the total PM2.5-relatedmonthlymean COPD endpointmor-tality cases are estimated to be 8 (95% CI: 4, 12), the number of cases inBogotá represent about 12% of total mortality cases for PM2.5-relatedendpoint.

4. Conclusions

The impact of biomass burning emissions over aerosol and gas-phase species in Northern South America was assessed by performingphoto-chemical transport model simulations during the peak of thedry season in the region. The model WRF-Chem was used to simulateregional transport model with three nested simulation domains of in-creasing spatial resolution centered in the Northern part of SouthAmerica and the Caribbean to understand the impacts of open and agri-cultural fires over air pollution levels and its associated health impacts.

Table 2Total February monthly PM2.5-related mortality cases between major cities in Colombiafor all-cause, cardiovascular, and respiratory mortality cases. The baseline incidence (perone hundred thousand people) for each outcome is 471.7 for all-cause, 130.2 for cardio-vascular, and 50.0 for respiratory mortality.

Cities Population ΔPM2.5 Total February mortality cases

All-cause Cardiovascular Respiratory

(Millions) μg·m−3 (Cases) (Cases) (Cases)

Bogotá 7.9 3.3 5.68 1.27 0.66Medellín 3.8 4.7 4.49 1.00 0.52Cali 2.3 1.2 0.88 0.20 0.10Barranquilla 1.2 2.2 0.75 0.17 0.09Bucaramanga 0.4 5.5 0.65 0.14 0.07

The increase in horizontal resolution in model grid-spacing resulted inbetter capturing topography induced small-scale local circulations,which is critical given the complex topography of the Northern Andes.The improvement in wind fields when resolution is increased resultsin smaller temperature and relative humidity biases. However, whenperformance model evaluation was applied model does not meet thecriteria values described by Emery et al. (2017) for PM2.5 and O3.Modeled PM2.5 over Bogotá for February 2018 was shown to reproduceobserved temporal variations but to underpredict PM2.5 by 13.7%, whilemodeled O3 over the same period was overpredicted by a factor of two.

A sensitivity analysis to determine the impact of BB emissionswere carried out. In this analysis, two simulation scenarios were con-sidered, a NO_FIRE scenario in which BB emissions were not includedand a FIRE scenario were BB emissions from the FINN emission in-ventory were incorporated in the simulations. The results suggestthat background aerosol loading during the dry season are domi-nated regionally by BB aerosols, and that most of the increase inaerosol concentration in the region is from Secondary Organic Aero-sol formation. Surface monthly mean PM2.5 over land in the domainincreases from 3.2 μg m−3 to 4.9 and 7.1 μg m−3 for February 2010and February 2018 respectively. This increase is much larger for theareas close to the BB sources in the Colombo-Venezuelan grasslandsand the tropical forest south of the domain. When the FINN emissioninventory was used, about 29% of the total organic aerosol mass wasBB-SOA. These results are consistent with PM2.5 chemical speciationdata collected in the city of Bogotá, during the simulation period,where organic matter was the most abundant aerosol species withabout 39% of the total PM2.5 mass. Modeled chemical speciation isbetter represented when BB emissions are included in simulations.Background O3 levels in the region were shown to be heavily im-pacted by these regional sources during the dry season with a 20% in-crease when BB emissions were included. The regional distributionof BB aerosol was assessed by comparing AOD at 550 nm withMERRA-2 reanalysis. This analysis shows there are some regionalbiases in modeled AOD, but the locations of peak AOD values seemstrongly related to BB sources.

Our simulation results suggest moderate PM2.5 increases between1.2 and 5.5 μg m−3 in the most populated urban areas during the pe-riods examined. However, there is large uncertainty in the emissions in-ventories, with significant differences in emissions of elemental carbon,organic carbon, PM2.5, and gas-phase emissions as well. A comparisonbetween the estimated emissions by different emission inventoriesshowed that there can be large variations in the total emissions andthe location of emissions as well.

The health impacts associated to BB in the region were assessed bycombining model results with population density information for theregion and applying Concentration Response Functions derived fromepidemiological studies. BB PM2.5 was associated with about 88mortal-ity cases in NSA during February due to short-term effects. Estimatedmortality were higher in densely populated areas, especially overColombia with 70% of cases in NSA. The contribution of BB aerosols toPM2.5 concentrations over Colombia accounted for 52 all-cause, 12 car-diovascular, and 7 respiratory mortality cases due to short-term expo-sure. Our sensitivity analysis suggest that BB aerosols can beresponsible for thousands of excess hospital emergency visits associatedwith respiratory diseases.

The chemical transport modelling reported in this work high-lights the importance of BB aerosol and gas-phase emissions in con-trolling background concentration during the dry season in theregion, and also shows, through numerical modelling, that theseemissions can be transported throughout the whole domain affect-ing distant cities in highly populated areas. Observational studiesof detailed aerosol chemical speciation and gas-phase compositionare necessary to further constrain chemical transport modelestimates of biomass burning smoke in the Northern SouthAmerica region.

10 K. Ballesteros-González et al. / Science of the Total Environment 739 (2020) 139755

Author contributions

Karen Ballesteros: Conceptualization,Writing - Original Draft, Inves-tigation, Visualization. Amy Sullivan: Investigation, Methodology. Ri-cardo Morales: Conceptualization, Methodology, Writing - review andediting, Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influ-ence the work reported in this paper.

Acknowledgments

This study was funded by the Colombian Administrative Depart-ment of Science, Technology and Innovation - COLCIENCIAS, undergrant contract No. FP44842-050-2017 project Number 1204-745-56533, and by the FAPA program from the Office of the vice-presidentfor Research from Universidad de los Andes. Karen Ballesteros-González acknowledge financial support from COLCIENCIAS DoctoralFellowship Convocatoria 733 - Doctorados Nacionales Departamentode Boyacá. Computational resources for this work were provided byProf. Fernando Garcia-Menendez from North Carolina State University.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2020.139755.

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