Assessment of the impact of biomass burning on air quality in the Colombian River Basin

Andrea Juliana Hernández Villamizar

Universidad Nacional de School of Engineering, Chemical and Environmental Engineering Department Bogotá, D.C., Colombia 2019

Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Andrea Juliana Hernández Villamizar

Dissertation submitted as partial requirement to obtain the degree of: Doctor of Engineering – Chemical Engineering

Advisor: Rodrigo Jiménez Pizarro, Ph.D.

Research area: Environmental Processes Research group: Air Quality Research Group

Universidad Nacional de Colombia School of Engineering, Chemical and Environmental Engineering Department Bogotá, D.C., Colombia 2019

Acknowledgements

I want to express my gratitude to everyone who supported me throughout the processes of this research. My sincere thanks to: • Professor Rodrigo Jiménez for his guidance, patience, motivation and support throughout the course of my PhD studies. • Colciencias for funding my Ph.D. and for funding this research project through grant number 1101-569-35161 for the project “Atmospheric emissions and impact of land use change and intensive agriculture on air quality in the Colombian ORIB”. • Professor John Lin for giving me the opportunity to do my research stay at his research group (Land Atmosphere Interactions Research group). • To all the members of LAIR group who were of great support while learning the STILT model, specially Derek Mallia and Dien Wu, who were always there to help solve my questions. • To Fulbright Colombia for giving me the opportunity of doing my research stay at the University of Utah. • To the Fulbright community of Utah, who were of great support during my research stay. • To Universidad Nacional de Colombia – Sede Manizales, especially to professor Beatriz Aristizabal for the opportunity of participating in different courses such as the WRF training and Aerosol measurements, both of which provided basis for conducting my research and contribute to my formation as air quality researcher. • To IGAC for the opportunity of participating in courses and attending conferences including the Aerosol Measurement schools in Bolivia and the Short Early Career Course held at Breckenridge Colorado. Both courses were very useful in my formation process. • Alvaro Tatis, for his help conducting measurements at Arauca. • Members of Air Quality research group. VI Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

• Mi familia, en especial a mis padres quienes han sido siempre mi motivación y ejemplo para superar todas las dificultades del camino. Este logro es de ustedes. • Mi esposo. Sin ti esto no habría sido posible, gracias por estar ahí y darme las fuerzas para continuar cuando todo parecía imposible.

Resumen and Abstract VII

Resumen

Los llanos son un ecosistema de sabana de cerca de medio millón de kilómetros cuadrados que ocupa gran parte de la cuenca del río Orinoco, en el norte de Suramérica, y se extiende desde el piedemonte de los Andes colombianos hasta el delta del Orinoco en el Océano Atlántico en Venezuela. Este ecosistema binacional experimenta quema de biomasa extensiva y periódica. Durante la temporada seca (periodo de quemas de biomasa) los vientos alisios del noreste se intensifican sobre la región de los Llanos, lo cual implica que las emisiones generadas por quema de biomasa pueden ser transportadas a las ciudades de la Orinoquia localizadas al sureste de los Llanos, cerca al piedemonte de los Andes.

La quema de biomasa es una fuente significativa de aerosoles y gases traza que afecta la salud humana, la química atmosférica y el clima. Los efectos en la salud son principalmente causados por el material particulado y el ozono (producido mediante reacciones fotoquímicas). La mayoría de las partículas emitidas por la quema de biomasa se encuentran en el rango de acumulación (diámetro de partícula < 1 µm), lo cual implica bajas velocidades de sedimentación, largos tiempos de residencia en la atmósfera y potencial para ser transportadas a grandes distancias. Las partículas orgánicas emitidas por las quemas contienen componentes mutagénicos y carcinogénicos que generan efectos genotóxicos en células alveolares humanas, aun cuando las concentraciones de PM10 en aire ambiente son inferiores a los estándares de la Organización Mundial de la Salud.

Investigaciones previas realizadas en Venezuela en los 1980-1990’s mostraron impactos significativos de las quemas de biomasa en los Llanos venezolanos. Hasta hace muy poco, no existían estudios sobre la influencia de la quema de biomasa en los Llanos colombianos. Adicionalmente, al inicio de esta investigación, ninguna ciudad de la Orinoquia contaba con sistemas de monitoreo de calidad del aire, entonces la información sobre niveles de material particulado en la región es muy escasa. Solo recientemente, una VIII Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin de las principales ciudades de los Llanos colombianos estableció una red de monitoreo de calidad del aire.

El problema de atribución de fuentes, particularmente el de establecer la contribución de las emisiones generadas por quema de biomasa a la degradación de la calidad del aire, es usualmente abordado empleando simulación y/o herramientas analíticas (p.e. composición química para modelos de receptores), cada una con sus propios desafíos. Uno de los problemas fundamentales cuando se simulan los impactos de la quema de biomasa, es construir inventarios de emisión, ya que la quema de biomasa es un problema complejo, no estacionario y de alta variabilidad espacial y temporal. La estimación de emisiones es aún más difícil en las sabanas de los Llanos, donde la información disponible muestra la predominancia de fuegos pequeños, con más del 75% de los fuegos menores a 115 ha, lo cual es menor al tamaño mínimo detectable de productos de área quemada globales. El otro aspecto por resolver es determinar si las emisiones y condiciones meteorológicas resultan en transporte regional o sinóptico de las emisiones de quema de biomasa. Adicionalmente, los dos enfoques (simulación -para propósitos de validación- y herramientas analíticas) requieren medición y muestreo de material particulado.

Esta tesis investigó el impacto de las emisiones generadas por quema de biomasa en la calidad del aire de la Orinoquia colombiana. Este estudio incluyó los siguientes pasos: (1) el desarrollo de un inventario de emisiones usando una metodología para integrar diferentes productos satelitales para detectar los fuegos pequeños que predominan en la región; (2) monitoreo de aerosoles en la columna atmosférica usando fotometría solar (Sitio exploratorio de AERONET); (3) campañas enfocadas en la medición y muestreo de material particulado durante periodos de alta y baja actividad de quema de biomasa; (4) simulaciones meteorológicas usando el modelo WRF, y (5) simulaciones atmosféricas Lagrangianas para entender el transporte de las emisiones por quema de biomasa y su contribución a los niveles de material particulado medido en sitios relevantes.

Las mediciones mostraron que la calidad del aire en los Llanos colombianos se deteriora significativamente durante los periodos de quema de biomasa. El contenido de aerosoles finos en la columna atmosférica incrementó el espesor óptico de aerosoles (AOD) de ~0.15 durante periodos con baja actividad del fuego, hasta ~1 durante periodos de quema de Resumen and Abstract IX biomasa. La quema de biomasa afectó de forma similar a un sitio rural y un sitio suburbano cerca del piedemonte de los Andes, causando incrementos de PM10 de 21 µg/m³ y 26 µg/m³ en Taluma (rural) y Libertad (suburbano), respectivamente, durante un periodo de alta actividad de quema en los Llanos de Colombia y Venezuela. Los sitios más cercanos a la frontera con Venezuela experimentaron mayores impactos. Las concentraciones en Yopal y Arauca se incrementaron cerca de 70 µg/m³ durante un periodo de alta actividad de fuego en Venezuela.

Los niveles de PM10 y carbono negro (BC) alcanzados durante la temporada de quemas en los Llanos son similares, e incluso mayores, a los observados en algunas estaciones del área urbana de Bogotá, donde existen múltiples fuentes móviles e industriales. Los aumentos de PM10 estuvieron relacionados con aumentos en los trazadores de quema de biomasa (BC y potasio soluble, K+) y trazadores de polvo. Técnicas exploratorias de atribución de fuentes demostraron que cerca del 80% de los aumentos observados de PM10 son causados por emisiones de quema de biomasa y 20% son debidos a emisiones de polvo.

Una de las principales contribuciones de esta tesis fue el desarrollo de un indicador de la contribución de las partículas emitidas por fuegos a las concentraciones en el receptor (indicador CFER). Esta herramienta emplea la potencia radiativa del fuego (FRP) de las detecciones de fuegos activos de MODIS, como un proxy de las emisiones de quemas, y la huella de influencia generada a partir de simulaciones Lagrangianas estocásticas, para estimar la influencia de las emisiones por quemas en las concentraciones promedio diarias de aerosoles en los receptores. Este indicador se desempeñó razonablemente bien para las observaciones de PM10 en Yopal, Arauca y Taluma, y tiene el potencial para convertirse en una herramienta operacional combinando detecciones de fuego en tiempo casi real y simulaciones Lagrangianas.

Los aumentos estimados de PM10 calculados con STILT subestiman significativamente los aumentos observados. El análisis reveló que la subestimación es insensible a los campos meteorológicos empleados (GDAS y WRF), la desagregación de emisiones y emisiones de otras fuentes. Los resultados sugieren que la subestimación está probablemente relacionada con la formación de aerosoles secundarios no incluidos dentro del enfoque de modelación empleado. X Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Esta tesis contribuye al entendimiento del impacto de la quema de biomasa en la calidad del aire de la Orinoquia colombiana, abordando un problema de contaminación regional que no había sido previamente identificado en Colombia. Los resultados proveen una evidencia científica sólida del impacto de las quemas en los Llanos en la calidad del aire de las ciudades de la Orinoquia colombiana, provee por primera vez fuerte evidencia del transporte transfronterizo de la contaminación por quema de biomasa desde Venezuela, y también desarrolló un indicador que puede ser usado por las autoridades ambientales para predecir episodios de contaminación y tomar acciones para reducir la exposición de la población.

Palabras clave: Quema de biomasa, simulaciones Lagrangianas, material particulado, Orinoquia, Contaminación transfronteriza, aerosoles

Resumen and Abstract XI

Abstract

The “Llanos” is a half a million square kilometers savanna ecosystem that occupies most of the Orinoco River Basin (ORIB) in Northern South America, from the Colombian Andes foothills almost to the Orinoco River delta at the Atlantic Ocean in Venezuela. This binational savanna ecosystem undergoes periodic and extensive biomass burning (BB). During the dry season (BB period) northeast trade winds are intensified over the Llanos region, which implies that BB emissions may be transported to Colombian Orinoco river basin cities located southwest of the Llanos near the Andes foothills. Colombian Llanos have a population of 1.7 million inhabitants, 73% of them in the urban areas, who can potentially be exposed to BB pollutants.

Biomass burning (BB) is a significant source of trace gases and aerosols that affects human health, atmospheric chemistry and climate. Health effects are mainly caused by particulate matter (PM) and photochemically-produced ozone. Most of BB emitted particles are within the accumulation mode (particle diameter < 1μm), which implies low deposition velocities, long residence times in the atmosphere and potential for long-range transport. BB-derived organic particles contain mutagenic and carcinogenic components that lead to genotoxic effects on human alveolar cells, even when ambient air PM10 concentrations are below the World Health Organization (WHO) standard.

Previous research conducted in Venezuela in the 1980-1990’s showed significant air quality impacts of BB in the Venezuelan Llanos. Until recently, there were no studies on the influence of biomass burning in the Colombian Llanos. Furthermore, at the beginning of this research no Colombian ORIB city had air quality monitoring systems, so there was scarce information on particulate matter levels in the region. Only recently, one of the main Colombian Llanos cities has established an air quality network. XII Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

The source attribution problem, specifically the one of disentangling the BB emissions contribution to air quality degradation is usually tackled using simulation and/or analytic tools (e.g. chemical composition for receptor modeling), both of which have its own challenges. One of the fundamental problems when simulating BB impacts is building emission inventories, because BB is a complex, non-steady state problem, of high spatiotemporal variability. Emission estimation is even more difficult in the Llanos savannas where available information shows the predominance of small fires with more than 75% of patches smaller than 115 ha, which is smaller than the minimum detectable burned size of available global burned area products. The other aspect is to determine if emissions and meteorological conditions result in regional or even synoptic scale transport of BB emissions. Also, measurement and sampling of PM is required for both approaches (simulation and analytical tools).

This thesis investigated the impact of biomass burning emissions on air quality of Colombian Orinoco river basin. The study included the following steps: (1) the development of a BB emission inventory using a methodology for integrating different satellite products to detect small fires that are predominant in the region; (2) monitoring of aerosol column using sun photometry (exploratory AERONET site); (3) measurement campaigns focused on particulate matter measurements and sampling during periods of high and low biomass burning activity; (4) meteorological simulations with a state of science model (WRF), and (5) Lagrangian atmospheric simulations to understand the transport of biomass burning emissions and their contribution to the particulate matter levels at relevant locations.

The measurements showed that air quality in the Colombian Llanos significantly deteriorates during biomass burning periods. Fine mode aerosol content in the total atmospheric column increased the Aerosol Optical Depth (AOD) from ~0.15 during periods with low fire activity up to ~1 during biomass burning periods. BB affected in a similar way rural and suburban locations close to the Andes foothills, causing PM10 to increase by 21 µg/m³ and 26 µg/m³ at Taluma (rural) and Libertad (suburban) sites, respectively, during a period of high fire activity in Colombia and Venezuelan Llanos. Urban locations closer to the Venezuelan border were more severely impacted. Concentrations at Yopal and Arauca increased by ~ 70 µg/m³ during a period with high fire activity in the Venezuelan Llanos.

Resumen and Abstract XIII

The levels of PM10 and BC reached during the biomass burning period in the Llanos region are similar, or even higher than the levels observed in some urban monitoring stations of Bogota, where dense industrial and mobile sources exist. PM10 enhancements were closely related enhancements of biomass burning (BC, K+) and dust tracers. Exploratory source apportionment techniques demonstrated that around 80% of the observed PM10 enhancements are due to biomass burning emissions, and 20% are caused by soil dust.

One of the main contributions of this thesis was the development of an indicator of the contribution at the receptor of particles from fire emissions (CFER). This tool uses Fire Radiative Power (FRP) from MODIS active fire detection as a proxy of fire emissions and the footprint generated from backward stochastic Lagrangian modeling to estimate the influence of fire emissions on the daily average aerosol concentration at receptors. This indicator performed reasonably well for PM10 observations at Yopal, Arauca and Taluma and has the potential to become an operational tool combining near real-time MODIS fire detections and Lagrangian simulations.

Estimated PM10 enhancements calculated with STILT significantly underestimated observed enhancements. The analysis revealed that the underestimation is rather insensitive to: (1) driving meteorology (GDAS or WRF wind fields); (2) time allocation of emissions, and (3) emissions from other sources. Results suggest that the underestimation is likely related to secondary aerosol formation, not included within the modeling framework.

This thesis contributes to the understanding of the impact of biomass burning on air quality of Colombian Orinoco River basin, addressing a regional pollution problem not previously identified in Colombia. Results provide solid scientific evidence of the impact of Llanos biomass burning on the air quality of Colombian Llanos cities, provides for the first time hard evidence of transboundary transport of BB pollution from Venezuela, and also provides an indicator that could be used by the environmental authorities to forecast pollution episodes and take actions to reduce population exposure.

Keywords: Biomass burning, Lagrangian simulations, Particulate matter, Orinoco River Basin, Transboundary pollution, Aerosols

Content XV

Content

Pág.

Resumen ...... VII

List of figures ...... XVII

List of tables ...... XXIII

List of symbols and abbreviations ...... XXV

1. The savanna ecosystem and the Orinoco river basin savannas...... 9 1.1 The savanna ecosystem and the role of fire...... 9 1.2 Anthropogenic fire use in savannas ...... 13 1.3 The northern South America Llanos region ...... 16 1.3.1 Llanos landscapes ...... 18 1.3.2 Climate and atmospheric circulation ...... 21 1.3.3 Land cover, land use and population ...... 24 1.4 References ...... 27

2. Fire emissions in the Llanos region ...... 33 2.1 Burned area estimation ...... 34 2.2 Global biomass burning emission inventories ...... 39 2.2.1 GFED ...... 39 2.2.2 FINN ...... 41 2.3 Burned area estimation for savanna ecosystems ...... 41 2.4 Methodology ...... 43 1.1.1 Landsat burned area identification ...... 46 1.1.2 MODIS burned area identification ...... 47 1.1.3 Landsat and MODIS integration ...... 51 1.1.4 Burned area from active fire data ...... 53 1.1.5 Emission calculation ...... 54 1.1.6 Date attribution ...... 57 2.5 Results ...... 58 2.6 Conclusions ...... 65 2.7 References ...... 66

3. Evidences of the impact of fire emissions on air quality levels ...... 72 3.1 Fire activity and meteorological conditions during measurement campaigns .... 73 3.2 AERONET measurements ...... 76 3.2.1 AERONET data description ...... 76 3.2.2 UNC-Gaitan site...... 81 3.2.3 Results ...... 83 XVI Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

3.3 Measurement campaigns ECALB and ECALB2 ...... 88 3.3.1 Methods ...... 88 3.3.2 Results ...... 94 3.4 Conclusions ...... 108 3.5 References ...... 109

4. Transboundary pollution ...... 113 4.1 Introduction ...... 114 4.2 Materials and Methods ...... 117 4.2.1 Study area and emissions estimates ...... 117 4.2.2 PM10 and O3 measurements...... 118 4.2.3 Atmospheric transport and fire activity...... 119 4.3 Results ...... 121 4.4 Conclusions ...... 130 4.5 References ...... 131

5. Understanding atmospheric transport of biomass burning emissions ...... 137 5.1 Atmospheric dispersion modeling ...... 137 5.1.1 Lagrangian particle dispersion models (LDPM) ...... 141 5.2 Manuscript: Understanding the impact of biomass burning on air quality pollution in Northern South America savannas through Lagrangian simulations and a tailor-made emission inventory...... 143 5.2.1 Abstract ...... 144 5.2.2 Introduction ...... 144 5.2.3 Methodology ...... 146 5.2.4 Results ...... 155 5.2.5 Discussion ...... 162 5.2.6 Conclusions ...... 167 5.3 References ...... 168

6. Conclusions and perspectives ...... 173 6.1 Conclusions ...... 173 6.2 Perspectives ...... 175

Content XVII

List of figures

Pág.

Figure 1-1: Biomass burning in Colombian Llanos (a) savanna ecosystem in Colombian high plains (b) agricultural plot in Colombian high plains. (Source: Alvaro Tatis) ...... 15 Figure 1-3: Llanos region definition. (a) Llanos definition by Trujillo et al., (2010) (b) Llanos definition by Olson et al., (2001) (c) Llanos definition by G. Sarmiento, (1983) (d) Land cover over Colombian Llanos from (Instituto de Hidrología y Estudios Ambientales, 2012)...... 17 Figure 1-4: Llanos region, main Llanos cities and native American Communities in Colombian Llanos. Source: Base map from ESRI, Llanos region Trujillo et al., (2010) and native American Communities from Agencia Nacional de Tierras (https://www.datos.gov.co/Agricultura-y-Desarrollo-Rural/Resguardos-Ind-genas/2wvk- ve5b) ...... 18 Figure 1-5: Llanos landscapes. Source: Adapted from (Hétier & Falcón, 2003; Hoyos, 1994) ...... 20 Figure 1-6: Colombian high plains savannas. (Source: this thesis.) ...... 20 Figure 1-7: Climatology average of surface precipitation rate in mm/day (a) and surface air temperature in °C (b) for the period 1981-2010 from NCEP/NCAR Reanalysis (https://www.esrl.noaa.gov/psd/data/composites/day) ...... 22 Figure 1-8: Backward trajectories for 2015 simulated by HYSPLIT using GDAS1 meteorological data. (a) Daily trajectories grouped in clusters and (b) Percentage of trajectories in each cluster. (Source: this thesis) ...... 23 Figure 1-9: Land cover map for Colombian Llanos for the period 2005-2009 according to Colombia Land Cover Map (Instituto de Hidrología y Estudios Ambientales, 2012) ...... 25 Figure 1-10 Land cover from MODIS Land Cover Product MCD12 ...... 26 Figure 2-1: Diurnal cycle from GOES for Colombian and Venezuelan Llanos. (Source: this thesis from GOES data) ...... 42 Figure 2-2: Flowchart of the biomass burning emission estimation. (i) Landsat burned area identification using BAMS tool (ii) Burned area identification based on the application of spectral indexes (iii) Landsat and MODIS burned area integration (iv) Active fires-based burned area (v) emission estimation and (vi) date attribution. (Source: this thesis.) ...... 44 Figure 2-3: Example of burned area identification over Landsat scenes using BAMS. Red polygons are burned areas that occurred between February 23 and February 7. (Source: this thesis) ...... 47 Figure 2-4: Selection of reference burned polygons for threshold definition. L0 represents Landsat pre-fire image of day 38 (February 7, 2015) and L1 represents Landsat post-fire XVIII Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin image of day 54 (February 23, 2015). That Landsat interval corresponded to two MOD13Q1 intervals: MO-M1, February 2-February 18, 2015 and M1-M2, February 18- March 06, 2015. Difference of NDVI was calculated between MO-M1 (dNDVI-4933) and between M1-M2 (dNDVI-6549). Polygons having less dNDVI in M0-M1 interval than in M1-M2 interval were burned between February 7 and February 18 and were used as the reference burned polygons. (Source: this thesis)...... 48 Figure 2-5: Threshold definition for GEMI index. a) Cumulative histogram of minimum index value within the reference burned polygons used to define the seed pixel threshold. A pixel is considered as burned if its index is less than 0.41, that corresponds to the 80% of cumulative frequency. b) Cumulative histogram of average index value within the reference burned polygons used to define the second stage threshold. A pixel is considered as burned if index is less than 0.46, that corresponds to the 80% of cumulative frequency. (Source: this thesis) ...... 49 Figure 2-6: (a) Example of seed and second stage pixels using the GEMI, NDVI and BAI index and (b) resulting burned area identified using spectral indexes over MOD13Q1. (Source: this thesis)...... 50 Figure 2-7: Methodology for assigning MODIS intervals to Landsat polygons. L1 refers to Landsat pre-fire image. L2 refers to Landsat post-fire image. M1-M2 and M2-M3 refers to the two intervals partially included within the Landsat interval. (Source: this thesis) ...... 52 Figure 2-8: Integration of Landsat and MODIS polygons. (Source: this thesis) ...... 53 Figure 2-9: Burned area in the Llanos region for the period January 17 – February 18, 2015. (a) Burned area aggregated in cells of 0.25°. (b) Burned area polygons and Llanos Landscapes (c) Detailed Llanos landscape classification (Hétier & Falcón, (2003) within Venezuelan Alluvial Plains with low burned area (d) MODIS Land cover. (Source: this thesis) ...... 59 Figure 2-10: Number of fires, burned area and TPM emissions for each Modis land cover category. Colors in stack bars represent the more detailed land cover category from Corine land cover for Colombia. Venezuela only had Modis category and is shown at the bottom of each bar. (Source: This thesis)...... 60 Figure 2-11: Number of active fires per country (a) Colombia, b) Venezuela and percentage of area represented by each fire size interval at (c) Colombia and (d) Venezuela. (Source: This thesis)...... 61 Figure 2-12: Daily TPM emissions per country. True date corresponds to burned areas that were overlapped by active fires and assigned dates correspond to burned areas with no active fire detection that were attributed a date based on a daily fraction of active fires calculated using 3-day centered mean in 0.5° cells. (Source: this thesis)...... 62 Figure 2-13: Differences in (a) burned area and (b) emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED). (Source: this thesis)...... 64 Figure 2-14: GFED carbon emissions in the Llanos region for the period 2007-2016 (a) monthly emission by category (b) Boxplot of monthly emissions for the period 2007-2015. (Source: this thesis using data from GFED)...... 65 Resumen and Abstract XIX

Figure 3-1: Active fires (3-day centered mean of MCD14ML fire detections; fires in Venezuelan Llanos are shown in gray and fires in Colombian Llanos are shown in orange), precipitation and wind speed during measurement campaigns are shown in middle and bottom panel. (Source: this thesis) ...... 74 Figure 3-2: AERONET UNC-Gaitan site. (Source: this thesis) ...... 81 Figure 3-3: Sun photometer installed at UNC-Gaitan. (Source: this thesis) ...... 82 Figure 3-4: Number of fires in the Llanos Region, AOD (500 nm), Angstrom Exponent and fine mode fraction. Active fires are presented as MODIS MCD14 fire counts (in bars) and as the 3-day moving average of fire detections (lines) in black for Venezuelan llanos and in orange for Colombian Llanos. (Source: this thesis using data from AERONET, UNC-Gaitan site) ...... 83 Figure 3-5: Biomass burning emissions transport from Brazil to Colombia on September 16, 2015. (a) Carbon monoxide from Aqua/AIRS (b) Aerosol Optical Depth. (Source: NASA Worldview https://worldview.earthdata.nasa.gov/) ...... 85 Figure 3-6: (a) Clusters of Aerosol size distributions, dV(r)/dln(r). (b) Coincident Aerosol Optical Depth for the aerosol size clusters (c) Dates of the aerosol size clusters. (Source: this thesis using data from AERONET – UNC-Gaitan site) ...... 86 Figure 3-7 Single scattering albedo spectra. (Source: this thesis using data from AERONET – UNC-Gaitan site) ...... 87 Figure 3-8: Aerosol classification for UNC-Gaitan based on (a) clusters based on SSA and EAE values as proposed by Giles et al., (2012) and (b) clusters based on AAE and EAE values as proposed by Russell et al., (2010) ...... 87 Figure 3-9: Measurement sites campaigns ECALB1 and ECALB2. (Source: this thesis) 88 Figure 3-10: Measurement sites (A) Taluma and (B) Libertad. (Source: this thesis) ...... 90 Figure 3-11: PM10 concentration during beginning of the dry season (ECALB1a and ECALB2a). (Source: this thesis)...... 95 Figure 3-12: PM10 concentrations at Taluma, relative humidity, precipitation and 3-day centered mean of active fires during ECALB1 (fires in Venezuelan Llanos are shown in gray and fires in Colombian Llanos are shown in orange) (Source: this thesis; active fires from the MCD14 product) ...... 96 Figure 3-13: 3-Hourly concentrations at Taluma. (Source: this thesis)...... 97 Figure 3-14 PM10 concentrations during ECALB1b. (Source: this thesis)...... 98 Figure 3-15 Active fires, precipitation, relative humidity and PM10 concentrations during ECALB2b ...... 99 Figure 3-16: Correlation matrix for (a) Taluma ECALB2 and (b) Libertad ECALB1 (Source: this thesis) ...... 100 Figure 3-17: PM10 and concentrations of soluble potassium (a) and silicon (b). (Source: this thesis) ...... 101 Figure 3-18: Scatter plots and linear regression of (a) soluble potassium vs. PM10 and (b) silicon vs. PM10. (Source: this thesis) ...... 102 Figure 3-19: Location of Bogota sites used for comparison with Taluma and Libertad measurements (Source: this thesis, coordinates of Bogota sites from (Ramírez et al., 2018) y (Vargas et al., 2012) ...... 103 XX Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-20 Average concentrations of PM10 and its main components and comparison with ambient air values at Bogota...... 105 Figure 3-21 (a) Mass reconstruction (b) Mass reconstruction percentage ...... 106 Figure 3-22: Contribution of each factor to the simulated contribution using APCS. The “zero pollution day” was assumed as the baseline concentration which corresponds to the average of the days of the lowest concentrations measured at the beginning of the dry season at Taluma and Libertad. (Source: this thesis)...... 108 Figure 4-1: Study area, including the Llanos region, the 2 cities were measurements were conducted, and the temporary AERONET UNC-Gaitan site (https://aeronet.gsfc.nasa.gov/)...... 118 Figure 4-2: Monthly precipitation and fire activity statistics for the Venezuelan and Colombian Llanos for the period 2001-2014. The precipitation data were obtained from the Climatic Research Unit (CRU, Harris et al., 2014). B) Daily TPM emissions as estimated by GFED during the measurement campaign and the dry season of December 2014 – May 2015...... 123

Figure 4-3: A) PM10 concentrations and B) GFED TPM emissions during the measurement period. Blacks lines in panel A) denote the average concentration (solid line) and standard deviation (dotted line) measured in site Y3 during a period not affected by biomass burning (September – December 2017)...... 124 Figure 4-4: Ozone concentrations in Arauca (left side) and Yopal (right side) during the measurement period. Black lines denote the average concentration (solid line) and standard deviation (dotted line) of ozone concentrations measured in Arauca (site A4) and Yopal (Y3) during a period not affected by biomass burning (September – December 2017)...... 125 Figure 4-5: Correlation of PM10 at Arauca and Yopal with the indicator of the contribution at the receptor of particles from fire emissions (CFER). The data shown corresponds to the daily average at each city during the coupled period of time (April 1 – May 7, 2015). The color and fill of the points denotes the city (Arauca in gray/empty, Yopal in orange/full). Point size denotes accumulated rain along the 72-h backward trajectory. . 127 Figure 4-6: A) MODIS-Terra Aerosol Optical Depth (AOD), B) MODIS-Terra Angstrom exponent, and C) active fires and footprints (for Yopal) for 3rd May 2015. Footprints were calculated from 72-h backward trajectories. Active fires included in C) are fire detections from May 1-3. Fires in (a) are the ones detected by Terra in the morning overpass time (10:30 LT) when AOD was measured. Terra morning does not capture the hours of high fire activity (15:00-21:00 LT), but its AOD provides an indicator of the advection of emissions from Venezuela to Colombia...... 128 Figure 4-7: A) Time series of active fires in Colombia and Venezuela as observed by MODIS and AERONET measurements at the UNC-Gaitan site. Top panel shows the MODIS 3-day mean of active fires in the Colombian (orange) and Venezuelan (black) Llanos. Panels below show AOD, AE and fine mode fraction measured at UNC-Gaitan. B) Monthly MODIS AOD for the region shown in Figure S6. Boxes represent percentile 25, 50 and 75 of daily AOD for each month during the period 2010-2016. The thick red line corresponds to the monthly average of daily AOD for 2015...... 129 Resumen and Abstract XXI

Figure 5-1: Study area and measurement sites. Source (this thesis) ...... 147 Figure 5-2: Flowchart of biomass burning emission estimation. (i) Landsat burned area identification using BAMS tool (ii) Burned area identification based on the application of spectral indexes (iii) Landsat and MODIS burned area integration (iv) Active fires-based burned area (v) emission estimation and (vi) date attribution. (Source: this thesis)...... 149 Figure 5-3: Simulated vs. Observed temperature, wind speed and wind directions. (Source: this thesis) ...... 153 Figure 5-4: Number of fires by size interval in Colombia (A) and Venezuela (C) and accumulated fraction of burned area and number of fires by size interval in Colombia (B) and Venezuela (D). (Source: this thesis)...... 156 Figure 5-5: Daily TPM emissions per country. True date corresponds to burned areas that were overlapped by active fires and assigned dates correspond to burned areas with no active fire detection that were attributed a date based on a daily fraction of active fires calculated using 3-day centered mean in 0.5° cells. (Source: this thesis)...... 157 Figure 5-6: Differences in (a) burned area and (b) emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED). (Source: this thesis)...... 158 Figure 5-7: Observed PM10 enhancement and fire emissions contribution to PM10 (a) using daily and 3-hourly HDEZ emissions and (b) using emission aggregated at cells of 0.25° X 0.25° and 0.05° X 0.05°. Dotted line and gray rectangle represent the average concentration measured on February 2 a day with little fire activity in Colombia and which concentrations were similar to concentrations measured at the beginning of the dry season when there was little fire activity. (Source: this thesis) ...... 160 Figure 5-8: Comparison of contributions using WRF and GDAS. (Source: this thesis) . 161 Figure 5-9: Observed PM10 enhancement and fire emissions contribution to PM10 using (a) GFED vs HDEZ inventory and (b) HDEZ and GFED hybrid inventory (HDEZ emissions for Colombian Llanos and GFED multiplied by 2 outside Colombian Llanos) ...... 161 Figure 5-10: Average contribution to the observed enhancements against distance. Gray dotted line corresponds to the distance from Taluma to Venezuela boundary. (Source: this thesis)...... 162 Figure 5-11: Difference between observed PM10 enhancement and simulated contribution divided by the simulated contribution versus d50 (distance to which 50% of the contribution is produced). (Source: this thesis)...... 165 Figure 5-12: Correlation of PM10 with the indicator of the contribution at the receptor of particles from fire emissions. (Source: this thesis) ...... 166

XXII Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Content XXIII

List of tables

Pág.

Table 1-1: Savanna definitions ...... 10 Table 1-2: Land cover in Colombian Llanos for the period 2005-2009 according to Colombia Land Cover Map (Instituto de Hidrología y Estudios Ambientales, 2012) ...... 25 Table 1-3: Population in the municipalities. Source: DANE...... 27 Table 2-1: Global burned area products ...... 37 Table 2-2: Burned area identification spectral indexes calculated over MOD13Q1 product ...... 47 Table 2-3: Threshold for burned area identification using spectral indexes. (Source: This thesis) ...... 51 Table 2-4: Corine land cover categories and their associated biomass loading, combustion efficiency and PM10 emission factors ...... 55 Table 2-5: Modis land cover categories and their associated biomass loading, combustion efficiency and emission factors ...... 56 Table 2-6: Burned area and emissions from the emission inventory developed in this work (Hernandez; HDEZ), the Global Fire Emission Database (GFED) and FINN inventory for the period January 17 – February 18, 2015 ...... 63 Table 3-1: Measurement campaigns ...... 75 Table 3-2 Main AERONET products ...... 77 Table 3-3: Aerosol classification approaches using AERONET products ...... 80 Table 3-4: Measurement periods ...... 82 Table 3-5: Chemical analysis performed on the samples ...... 90 Table 3-6 Description of sites, period and potential emission sources of each group presented in Figure 3-20 ...... 104 Table 3-7: Factors identified by the APCS analysis ...... 107 Table 5-1: Advantages and disadvantages of Eulerian and Lagrangian approaches (Based on (John C. Lin, 2012; Whelpdale, 1991) ...... 138 Table 5-2: WRF configurations tested and model evaluation for the period February 3 - 9, 2015...... 152 Table 5-3: Burned area and emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED) emission inventories for the period January 17 – February 18, 2015 ...... 158

XXIV Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

List of symbols and abbreviations

Latin symbols

Symbol Term SI units Definition 2 퐵퐴푠푓 Burned area from small fires m Equation 2.1 Particulate matter with an aerodynamic µg/m3 PM10 diameter smaller than 10 µm Particulate matter with an aerodynamic µg/m3 PM2.5 diameter smaller than 2.5 µm standardized value of the element i for Equation 3.2 푍 푖,푘 the sample k ∗ [푃]푝 × 푚 Rotated principal component scores Equation 3.3 ∗ [퐵]푝 × 푛 Rotated PC scoring matrix [ 푍]푛 ×푚 Matrix of normalized concentration Concentration of element i for the sample µg/m3 퐶 푖,푘 k Standard deviation for element i over all µg/m3 𝜎 푖 samples arithmetic mean concentration of element µg/m3 퐶̅ 푖 i over all samples (푍0)푖 Z-score for the artificial sample Enhancement of PM10 respect to the µg/m3 Equation 3.5 average of the days of the lowest ∆푃푀10 푘 concentrations measured at the beginning of the dry season Footprint. upstream influence of an m2.s/mol Equation 4.1 upwind source located at (xi,yi), prior time 푓(푥⃗⃗⃗⃗푟 , 푡푟|푥푖, 푦푗, 푡푚) tm, on concentrations at the receptor site, located at the 3-dimensional position 푥⃗⃗⃗⃗푟 , at time tr

Greek symbols

Symbol Term SI units Definition Ratio of MCD64A1 burned area to active ∝ 푟,푠,푣,푦 fires within MCD64A1 burned areas Correction factor that modifies he Equation 2.2 훾 푟,푠,푣,푦 burned area associated with FCout

𝜌푁퐼푅 Reflectance in the NIR (MODIS band 2) 𝜌푅퐸퐷 Reflectance in the red (MODIS band 1) XXVI Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Symbol Term SI units Definition 푉(휆) Voltage measured at wavelength 휆 Equation 3.1 훽0 Constant term of multiple regression Coefficient of multiple regression of the 훽 푝 source p

Abbreviations

Abbreviation Meaning NDVI Normalized Difference Vegetation Index GEMI Global Environment Monitoring Index BAI Burned Area Index MODIS Moderate Resolution Imaging Spectroradiometer MERIS Medium Resolution Spectroradiometer GFED Global Fire Emission Database FINN Fire Inventory from NCAR FRP Fire Radiative Power GFAS Global Fire Assimilation System BA Burned area FCout Number of active fires outside of MCD64A1 burned areas CASA Carnegie–Ames–Stanford approach biogeochemical model EF Emission factor dNBR difference normalized burned area BAMS Burned Area Mapping Software MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global product MODIS/Terra and Aqua Burned Area Monthly L3 Global 500 m SIN MCD64A1 Grid V006 MODIS/Aqua+Terra Thermal Anomalies/Fire locations 1km FIRMS MCD14ML V005 NRT (Vector data) GOES Geostationary Operational Environmental Satellite Landsat 8 OLI- Landsat Operational Land Imager (OLI) and the Thermal Infrared TIRS Sensor (TIRS). NIR Near Infrared SWIR Shortwave infrared TPM Total particulate matter BB Biomass burning ITCZ Intertropical Convergence Zone AERONET Aerosol Robotic Network Measurement campaign 1: Estudio de calidad del aire en la Orinoquia - ECALB1 línea base parte 1 Measurement campaign 2: Estudio de calidad del aire en la Orinoquia - ECALB1 línea base parte 2 AOD Aerosol Optical Depth AE Angstrom Exponent SSA Single Scattering Albedo AAOD Absorption Aerosol Optical Depth AAE Absorption Angstrom Exponent Resumen and Abstract XXVII

Abbreviation Meaning EAE Extinction Angstrom Exponent FMF Fine Mode Fraction PMF Positive Matrix Factorization APCS Absolute Principal Component Scores CMB Chemical mass balance PCA Principal component analysis ORIB Orinoco River Basin Indicator of the contribution at the receptor of particles from fire CFER emissions EDGAR Emission Database for Global Atmospheric Research CRU Climatic Research Unit

Introduction

Biomass burning is a significant source of trace gases and aerosols (Crutzen & Andreae, 1990). During 2017, fires emitted 38 Tg of total particulate matter (TPM) globally (according to data from the Global Fire Emission Database; Randerson et al., 2012), 59% of which were produced by savanna, grassland, and shrubland fires. Biomass burning emissions affect atmospheric chemistry, climate and human health (Crutzen & Andreae, 1990; Langmann, Duncan, Textor, Trentmann, & van der Werf, 2009; G. R. van der Werf et al., 2010). Health effects are mainly caused by particulate matter (PM), primary (directly emitted) and secondary (formed from primary emissions), and ozone. Most of biomass burning emitted particles are within the accumulation mode (particle diameter < 1μm) (Reid & Koppmann, 2005). Hence, these particles have low deposition velocity, long residence time in the atmosphere and have the potential for long-range transport.

Particulate matter emissions from biomass burning are of especial relevance from the public health perspective. They represent an acute exposure (exposure to a high level of pollutants during a short period of time) that lasts around three months per year (Ignotti et al., 2010), different from the chronic exposure typically found in urban areas. Short-term acute exposure to PM subtly increases the rate of cardiovascular events after pollution episodes (Anderson, Thundiyil, & Stolbach, 2012). Effects of acute exposure to biomass burning emitted particles have also been reported, showing increases in hospitalization for respiratory diseases in children and elderly (Ignotti et al., 2010; A. M. C. Da Silva, Mattos, Ignotti, & Hacon, 2013). Moreover, the aerosol health effects are significantly related to the particle chemical composition. Biomass burning emitted organic particles contain mutagenic and carcinogenic components that cause genotoxic effects on human alveolar cells, even when ambient air PM10 concentrations are below the World Health Organization (WHO) standard (50 µg/m3, 24-hour average). (De Oliveira Alves et al., 2014).

The Llanos is a half a million km2 savanna ecosystem (Guillermo Sarmiento, 1984), shared by Colombia and Venezuela. It is located within the Orinoco River Basin and extends from 2 Introduction the Colombian Andes foothills to the Orinoco Delta in Venezuela. It constitutes the largest savanna ecosystem in Northern South America and the second largest savanna in South America, after the Brazilian Cerrado (G. Sarmiento, 1983). The Llanos undergoes biomass burning during the dry season, mainly generated by anthropogenic causes that include land clearing and its use a management tool in extensive cattle grazing (as a way of obtaining fresh pasture) (Armenteras, Romero, & Galindo, 2005). To a lesser extent, fire also occurs for natural reasons, since it is part of the dynamics of savanna ecosystems (Medina & Silva, 1990; G Sarmiento & Monasterio, 1975).

Fire season is determined by the dry season length, which has differences through the region. There is a precipitation gradient with higher precipitation and shorter dry season in the southwest Colombian Llanos and lower precipitation and longer dry season in Venezuelan Llanos (G. Sarmiento, 1983). Precipitation regime is driven by the meridional movement of the Inter-tropical converge zone, ITCZ (Pacheco & León-Aristizábal, 2001; Pulwarty, Barry, Hurst, Sellinger, & Mogollon, 1998). During the dry season (austral summer) the ITCZ migrates southward toward the Amazon Basin, southern Colombia and Ecuador (Poveda, Waylen, & Pulwarty, 2006), intensifying the northeast trade winds over the Llanos region. This implies that Llanos emissions may be transported to Colombian Orinoco cities located southwest of the llanos in the Andes foothills.

High uncertainty exists in burned area estimations for the region. Some of them are from global burned area products (Anaya-Acevedo & Chuvieco-Salinero, 2010), which are not expected to be accurate at local scales since they use conservative thresholds to avoid commission errors and are design to describe global and regional fire activity (Araújo & Ferreira, 2015). Besides, the spatial resolution of available global burned area products makes small fires (predominant in savanna ecosystems) difficult to detect (Laris, 2005). On the other hand, regional estimations based on spectral indexes for burned area identification also lead to different estimations since depend on the detection algorithm and threshold definitions.

Armenteras-Pascual et al., [2011] reported that the average burned area for the period 2000-2009 in Colombia was 395,644 ha (according to the global product MCD45). This average burned area highly differs from the estimates of burned area for Colombian Llanos reported by Romero-Ruiz et al., [2012] that showed that on average 2.75 million hectares Introduction 3 were burned each year during the dry season for the period 2000-2008 and from González- Alonso et al., [2013] estimations that reported that about 1 million hectares were burned during February 2007. Despite the differences between estimations, they all show that vast areas of the Llanos are burned each year, which creates a concern about the atmospheric fate of the emitted particles.

Studies in Venezuela from the 1980-1990’s reported increases of total suspended particulate matter, trace gases and photochemical ozone during the fire season (Sanhueza, Crutzen, & Fernández, 1999; Sanhueza, Rondon, & Romero, 1987). However, in Colombian Orinoco cities there had been no study of the air quality impact of savanna fire emissions and most of these cities do not even have an air quality network. Quantifying the emissions and characterizing the dispersion processes of biomass burning emitted particles is needed to establish control strategies to protect population health in this area of the country that represents a quarter of Colombian territory and whose population may significantly grow due to the policies for the Orinoco region development.

Considering that there is a knowledge gap on the potential air quality impacts of the emissions caused by the large areas that are burned each year in the Llanos region, the research problem addressed by this thesis seeks to answer the following questions:

How much is the contribution of biomass burning to the particulate matter levels in Colombian Orinoco? How is the best way of representing spatially and temporally the biomass burning emissions for quantifying its impact on air quality? How representative or the typical biomass burning condition is the study period?

The hypothesis of this research is that a substantial amount of biomass burning emissions will have a limited injection height, hence, that emission fraction will have a high probability of impacting the air quality of Orinoco cities.

The general objective of this research was to estimate the impacts of biomass burning emissions to the air quality of Colombian Orinoco region. To accomplish that, the following specific objectives were met:

4 Introduction

Specific objective 1. To evaluate the correlation between composition and concentration of particulate matter measured under low and high influence of biomass burning.

Two measurement campaigns were conducted in the Llanos regions, in sites with no urban sources influence. PM10 levels and its chemical composition were measured during periods of high and low biomass burning activity. This project also included the installation and operation of a sun photometer from the Aerosol Robotic Network, AERONET of NASA and its data were used to analyze the influence of biomass burning on air quality. Additionally, PM data from a measurement campaign done in Yopal and Arauca by the environmental authority (Corporinoquia) were also analyzed.

Specific objective 2. To quantify and spatio-temporally disaggregate particulate matter and soot emissions produced by biomass burning in Orinoco’s savannas.

I developed a spatio-temporal disaggregated emission inventory for one-month period that covered one of the air quality measurement campaigns conducted during this project. This inventory required developing a methodology for integrating different satellite products in order to be able to detect small fires that are predominant in savanna ecosystems, and to temporally disaggregate the emissions, as required for its implementation in atmospheric modelling.

Specific objective 3. To estimate the contribution to biomass burning emissions to levels of particulate matter and black carbon measured in a site located in Colombian Orinoco appropriated for validating the simulation model.

A Lagrangian atmospheric model was used to analyze the transport of biomass burning emissions and to estimate the contribution of biomass burning emissions to the observed PM10 levels.

Specific objective 4. To estimate the contribution of biomass burning emissions to levels of particulate matter in the main cities of Colombian Orinoco.

Introduction 5

The estimation of the contribution of biomass burning emissions to levels of particulate matter was conducted by both exploratory source apportionment techniques and trough the analysis of the enhancements observed in Yopal and Arauca caused by biomass burning in Venezuela.

This thesis is organized in five chapters. Chapter 1 (The savanna ecosystem and the Orinoco River Basin savannas) describes the Orinoco region and a review of fire occurrence in this ecosystem. This includes study area definition, landscape characteristics, land use, climatic conditions, burning causes and burned area estimations.

Chapter 2 (Fire emission in the Llanos area) addresses specific objective 2. This chapter presents the emission inventory methodology that integrates Landsat burned area identification, MODIS burned area identification using spectral indexes and active fire detections to produce daily emissions and its application for the period January 17 - February 18, 2015.

Chapter 3 (Evidences of the impact of fire emissions on air quality levels) addresses specific objective number 1. This chapter shows the results of the two measurement campaigns done in Puerto Gaitan, Arauca and Villavicencio, their relation with meteorological conditions and fire activity. It also presents the results of chemical composition of some of the PM samples collected during the measurement campaigns and its relation with biomass burning activity.

Chapter 4 (Transboundary pollution) presents the analysis of measurements done by Corporinoquia in Arauca y Yopal during a period of high biomass burning in Venezuela and no fire activity in Colombia that evidenced transboundary pollution.

Chapter 5 (Contributions of fire emission to particulate matter levels in Orinoco cities) presents the impacts of biomass burning emissions to air quality as estimated from Lagrangian modeling. It includes the meteorological simulations required as input for Lagrangian modeling, its validation and the enhancements of particulate matter levels caused by biomass burning emissions as estimated from the developed emission inventory and global emission inventories.

6 Introduction

Finally, conclusions and perspectives are presented in Chapter 6. This thesis is part of the Colciencias-funded project: “Atmospheric emissions and air quality and public health impacts associated to land use change and intensive agriculture in the Colombian Orinoco region” that addresses various aspects of atmospheric emissions in the Orinoco River Basin, including particulate matter emissions from biomass burning, agriculture, from oil production, greenhouse gas fluxes from intensive agriculture and from native savannas.

Most of the results were published in peer-review journals and presented in science conferences.

Hernandez, A.J, Morales, L.A., Wu, D., Mallia, D.V., Lin, J.C., Jimenez R. Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Base. Atmospheric Environment. 2019.

Hernandez A.J., Morales L.A., Lin, J.C., Jimenez R. “Understanding the impact of biomass burning on air quality pollution in Northern South America savannas through Lagrangian simulations and a tailor-made emission inventory”. To be submitted to: Journal of Geophysical Research: Atmospheres.

Conferences

Hernandez, A.J., Morales L.A, Jimenez R. (2017). Desarrollo de una nueva metodología para la estimación de inventarios de emisión por quema de biomasa apropiados para simulación Lagrangiana y su aplicación a la Orinoquia colombiana (Poster). VI Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública. Cali, Colombia.

Hernandez, A.J., Morales, L.A., Jimenez R. (2017). Contaminación transfronteriza por quema de sabana en la Orinoquia Colombiana. (Oral presentation). VI Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública. 2017. Cali, Colombia.

Introduction 7

Hernandez, A.J., Morales, L.A., Jimenez R. (2016). Air quality degradation in Northern South America due to biomass burning transboundary pollution (Poster) 2016 IGAC Science conference. Breckenridge, CO, USA.

Hernandez, A.J., Morales, L.A., Tatis Bautista, A.D., Cañas Soler, F., Jimenez, R. (2015). Entendiendo la calidad del aire en la Orinoquia colombiana durante la temporada de quema de biomasa (Oral presentation). V Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública. Bucaramanga, Colombia.

Hernandez, A.J., Morales, L.A., Tatis-Bautista A.D., Rodríguez, N., Cañas, F., Ramos- Cantillo, J.A., Jimenez, R. (2015). Understanding air quality in the Colombian Orinoco region during the biomass burning season. (Poster) Latin American Measurements School: From measurement technologies to applications. La Paz, Bolivia.

Research stay

I got a fellowship of the Fulbright Foreign Student program that allowed me to do a 6-month research stay at the Department of Atmospheric Sciences of the University of Utah. I worked under the supervision of Dr. John Lin, director of the group Land Atmosphere Interactions research group. During my research stay I was trained in the use of the STILT Lagrangian Model and made most of the simulation work of this thesis.

1. The savanna ecosystem and the Orinoco river basin savannas

1.1 The role of fire in the savanna ecosystem

Savannas are the second largest plant formation in tropical America (Huber, 1987; G Sarmiento & Monasterio, 1975). They extend over an area of about 269 million hectares (Rippstein et al., 2001) and include the Cerrados (Brazil) in Brazil, the Llanos (Colombia and Venezuela), the Llanos de Moxos or Moxos plains (Bolivia), the Gran Sabana (Venezuela), the Rupuni savannas (Guyana), the savannas of the Rio Branco-Rupununi (Brazil and Guyana), the coastal savannas of the Guianas (Guyana, Surinam and French Guiana and northern Brazil), the Amazonian campos (Brazil), the llanos of Magdalena (Colombia) and the savannas of Miskito (Nicaragua and Honduras) (G. Sarmiento, 1983).

Savanna definition have been an object of controversy in part due to the heterogeneity of this ecosystem (Mistry, 2014; Solbrig et al., 1992) and also because natural savannas have been confused with savanna-like areas resulting from anthropogenic forest degradation (Ratnam et al., 2011; Veldman et al., 2015).

According to Beard, (1953) savanna is an Amerindian word first used in Cuba and Haiti and it was first published by Oviedo (1535) who defined it as: “land which is without tress but with much grass either tall or short”. Many savanna definitions have been proposed since then, some of them are presented in Table 1-1.

Some definitions are mainly based on the original savanna word definition, as the one presented by Archer et al., (2001) and Scholes & Archer, (1997). Others restrict the occurrence of savanna to climatic components (Lüttge & Lüttge, 2008) or the existence of plant adaptation mechanisms against water loss (Beard, 1953). Others incorporate the seasonality, the tropical nature of the ecosystem (not referring just the geographical 10 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin meaning but the processes and conditions operating at the ecosystem level that are restricted to the tropical zone) and the role of fire (Guillermo Sarmiento, 1984)

Table 1-1: Savanna definitions

(Beard, 1953) “Virtually continuous, ecologically dominant stratum of more or less xeromorphic herbs, of which grasses and sedges are the principal components, with scattered shrubs, trees or palms sometimes present” (Guillermo “The type of ecosystem of the warm (lowland) tropics dominated Sarmiento, 1984) by a herbaceous cover consisting mostly of bunch grasses and sedges that are more than 30 cm in height at the time of maximum activity. The herbaceous cover shows a clear seasonality in its development, with a period of low activity related to water stress. Fire in these ecosystems is a recurring natural factor, and fires may also be started by people once a year. The savanna may include woody species (shrubs, trees, palm trees), but never form a continuous cover that parallels the grassy one.” (Walker, 1987) Tropical (and near-tropical) ecosystems characterized by strong seasonality (usually wet summer/dry winter) related to water stress, in which the vegetation consists of a continuous herbaceous cover of mostly C4 grasses and sedges, an in most of which there is a significant but discontinuous cover of shrubs and trees. (Archer et al., 2001; “Communities or landscapes with a continuous grass layer and Scholes & Archer, scattered trees” 1997) (Lüttge & Lüttge, “Open habitats typically dominated by grasses and often 2008) strongly affected by seasonal changes of rainfall” (Ratnam et al., “Predominantly mixed tree-C4 grass systems defined by fire 2011) tolerance and shade intolerance of their species”

Walker, 1987 incorporates the predominance of C4 grasses (grasses with a C4 photosynthetic pathway). This was also highlighted by Ratnam et al., (2011) who considers the presence of C4 grass systems as a trait of natural savanna ecosystems that differentiate them from degraded forest, that look like savannas but differ from natural ones in their functional ecology and how they respond to perturbations. C4 grasses are more efficient which results in higher productivity and less water loss under water stress (Mistry, 2014), have low decomposition rates, dry out rapidly in the dry season and thus are inevitable flammable and promote fire where they product sufficient biomass, are shade intolerant and indirectly depend on fires to maintain their preferred light levels (Ratnam et al., 2011). Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 11

The role of fire in savannas has also been controversial. There had been basically three hypotheses: (1) fire originated savanna, (2) fire occurs in savanna, plants are adapted to fire, but it is not essential to maintain savannas and (3) fire is needed to maintain savanna ecosystems. The first hypothesis proposed that savannas were derived from forest as a result of constant fires (Budowski, 1956). Present knowledge rejects this hypothesis of solely anthropogenic origin of savanna. Palynologic records of the Cerrado flora from 32000 BP, as well as charcoal from Late Quaternary, proved that savanna and its natural fires existed long before the arrival of humans (Pinheiro & Monteiro, 2010).

The first hypothesis was also criticized by Beard, (1953), even before palynological evidenced were found. He argued that there were edaphic conditions typical of savanna, distinct from those of forest, which must have existed far back into history. In the same monography, he proposes the following second hypothesis: “Large stretches of grass, once developed, would become liable to catch fire occasionally from natural causes and burning would become more frequent after man’s arrival. Thus, vegetation would become secondarily adapted to fire, but would not be a necessity to is maintenance, this depending upon drainage conditions”. Although he recognized that savannas are a fire-adapted ecosystem, he failed to recognize the role of fires in maintaining different aspects of savanna ecosystems.

The third hypothesis considers fire one of the interacting factors that affects the savanna dynamics (Frost & Robertson, 1987; Walker, 1987) and classifies natural savannas as fire- dependent ecosystems (Giselda Durigan & Ratter, 2016; Pivello, 2011). Fires maintain the compositional mix of trees and shrubs (Ratnam et al., 2011), promote herbaceous productivity, consume dead material, return nutrients to the soil, stimulate plant reproduction (by removing plant cover, reducing competition and stimulating germination in some species) and maintain plant diversity (Frost & Robertson, 1987; Veldman et al., 2015).

Fire suppression have shown to increase the woody plant density in a process known as bush or brush encroachment (Scholes & Archer, 1997). This process has been observed in different savannas that have been protect from fire. For example, Venezuelan savannas had increases in its arboreal community after 16 years of fire exclusion (San Jose & Farinas, 1983) and Cerrado open form savannas that were replaced by the forest form of Cerrado after 38 years of fire protection (G. Durigan & Ratter, 2006). 12 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

However, this is not always the case. J. F. Silva et al., (2001), tested the hypothesis that exclusion from fire and cattle is responsible for the increase in tree cover in open savanna vegetation by comparing four plots under different regimes (with and without fire exclusion). They found increases in the woody component of all plots and conclude that is very difficult to disentangle the effects of fires from the other factors that influence the tree/grass ratio in savannas. They also highlight that studies commonly used as evidence of the effect of fire on savanna structure (showing increase in tree cover after fire exclusion) lack of a proper experimental control to distinguished between the results of exclusion and other intervening factors.

These different results and processes observed when fire is suppressed can be better understood using the conceptual models and terminology presented by Veldman, (2016). He recommends the use of three different terms to refer to “savannas”: old-growth savannas, when referring to ancient grassy biomes; derived savannas when referring to deforestation; and secondary savannas when referring to the grass vegetation formed after land abandonment of agricultural areas (including crops, tree plantations or planted pastures) or forest-encroached savannas that were originally old-growth savannas. Old- growth savannas, derived savannas and secondary savannas are indistinguishable from each other based on tree and herbaceous cover; detailed floristic composition must be established to differentiate the old-growth savannas.

Fire has different effects depending on the type of savanna. On one hand, old-growth savannas require fires to maintain its biodiversity. When fire regimes are altered, or under factors like overgrazing or presence of invasive species, the savanna is converted to degraded savanna. In case of fire exclusion, the latter can be converted into a forest- encroached savanna (Veldman, 2016). Once the savanna reaches this state, in case of abandonment it becomes a secondary savanna and cannot spontaneously recover its old- growth savanna state (Cava et al., 2017).

On the other hand, derived savanna come from old-growth forest that were degraded, and by action of fire became a savanna-like structure. However, its plant community composition is substantially different and fire, although “maintains” its structure, is a Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 13 destructive disturbance that prevents the recovery of previously established forest vegetation (Veldman, 2016).

Compiling previously stated definitions and concepts, old-growth tropical savannas can be defined as: Ancient ecosystems with a continuous herbaceous-stratum of C4-grass plants, including treeless tropical grasslands as well as grassy ecosystems with fire-tolerant trees that occur across vast regions of the tropics, which structure is determined by interactions among climate, vegetation, fire, herbivores and edaphic factors (Veldman, 2016; Walker, 1987).

1.2 Anthropogenic fire use in savannas

Although fire occurs naturally, most of the fires are anthropogenic (Bilbao et al., 2010). Indigenous communities have been using fire since thousands of years ago (Mistry et al., 2005; United Nations University, 2015) and, specifically for neotropics, paleoecological evidence demonstrated a huge increase in fire activity synchronous with human arrival (12500 years BP) (Montoya & Rull, 2011).

Indigenous communities have used fire in a careful and accurate way (Pivello, 2011), developing the knowledge of how to manage fire in a way that ensures that the ecosystem do not lose its ecological properties (Giselda Durigan & Ratter, 2016). They use fire for several reasons that include cultivation, hunting, harvesting natural resources, clean and maintain the savanna, protection, communication, livestock grazing and pest elimination (Mistry et al., 2005; Rodríguez, 2007). These uses are briefly described below.

Fire is used for clearing land and fertilized the soil with ashes in a practice called shifting cultivation. In this practice trees are cut and after few months, when felled trees are drier, they are burned. The ashes fertilize the soil and maintain a high productivity for 2 to 3 years. Then, the field is abandoned for few decades to recover (fallow period). Natural vegetation around the plot is left intact to facilitate forest regrowth after land abandonment (Pivello, 2011).

14 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Fire is used to drive game in hunting. In this practice either multiple points are ignited simultaneously to leave one escape route where hunters wait and kill the animal, or encircle the pray so the fire kills it (Mistry et al., 2005).

The use of fires for harvesting natural resources includes its use for honey extraction (Pivello, 2011) and for stimulating fruiting. In some fruit tree species, fires in the early dry season promote flowering and the subsequent fruit production (Mistry et al., 2005).

Indigenous people use fire to replace old grasses and in that way keep the savanna green, clean and beautiful (Rodríguez, 2007; Sletto & Rodriguez, 2013). Kraho community members (Brazil) think that savanna needs to be burned every 2-3 years to keep it clean (Mistry et al., 2005). Furthermore, communication routes are regularly burned because tall grasses impedes the speed of walking and are perceived as dangerous because of the presence of animals such as snakes and scorpions (Mistry et al., 2005).

Savanna protection is one of the main purpose of fire across indigenous communities living in savannas, and the one that fire management policies should follow, because it is essential to maintain savanna structure, functioning and biodiversity (Giselda Durigan & Ratter, 2016). Uncontrolled and catastrophic fires are prevented by burning small patches of savanna. This results in a mosaic of small portions of savanna in different states of re- growth that act as a fire-break (Bilbao et al., 2010) and creates microhabitats that support different species and hence, preserve biodiversity (Mistry et al., 2005). Within Pemón community (Venezuela), this process is achieved following a cooperative burning strategy in which each person starts the fire where the previous fire died out (Bilbao et al., 2010; Rodríguez, 2007).

Finally, fire is also used to stimulate the new grass growth that is palatable for cattle (United Nations University, 2015) and to eliminate pests, such as insects (Mistry et al., 2005).

Nowadays, besides indigenous communities, people use fire to remove vegetation to install crops or pastures, to perform shifting cultivation, to burn crop residues or stimulate pasture regrowth. Shifting cultivation is not done as carefully as the original practice. Private agricultural lands are relatively small, so large burns are made with higher frequency. This Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 15 is also happening in the existing indigenous communities, since most of them now live in relatively small reserves and became semi-sedentary (Pivello, 2011).

Figure 1-1: Biomass burning in Colombian Llanos (a) savanna ecosystem in Colombian high plains (b) agricultural plot in Colombian high plains. (Source: Alvaro Tatis)

(a)

(b)

Traditional indigenous knowledge of fire management is at risk. Younger members have been influenced by outsiders and have adopted the perspective of fire as a destructive tool (Mistry et al., 2005; Sletto & Rodriguez, 2013). They think that fire is bad for the environment, lack of knowledge of traditional fire control methods and experiment a deep sense of cultural embarrassment (Rodríguez, 2007). 16 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

The misconceptions about fire perceived only as a destructive tool, popularized based on its effects on fire-sensitive ecosystems (e.g. forest), ignore its benefits on fire-dependent ecosystems and have resulted in the adoption of fire restricting policies in savannas (Mistry et al., 2005). Examples of these policies include the EDELCA fire control program in the Canaima National Park (Gran Sabana, Venezuela) (Rodríguez, 2007) and the prohibition of fires in the Brazilian Cerrado that only permit fires on protected areas (Giselda Durigan & Ratter, 2016).

Fortunately, some countries have implemented fire management strategies that protect savanna ecosystems and reduce emissions. Namibia and Australia have policies for emissions abatement based on avoiding late dry season wildfire problems using controlled burns. These burns are typically made in the early dry season and create a landscape of burnt and unburnt patches which resembles the cooperative burning of indigenous communities and complement them with contemporary tools such as satellite information and geographic information systems (Russell-Smith et al., 2017). This policy is an example of how to incorporate the traditional indigenous knowledge on fire management into the land uses and emission mitigation needs of the modern society while ensuring the conservation of savanna ecosystem, its biodiversity and ecosystem services.

1.3 The northern South America Llanos region

The Colombian – Venezuelan Llanos constitute the major savanna region of northern South America (G. Sarmiento, 1983). Llanos cover an elongated area limited by the Andes foothills in the west, Venezuelan coastal range in the north, Guiana shield and Branco- Negro forest in the south. In Colombia this region is within the departments of Arauca, Meta, Casanare and Vichada. In Venezuela the Llanos belong to the states of Apure, Barina, Cojedes, Portuguesa, Guarico, Anzoategui and Monagas.

Different Llanos area delimitations have been proposed. (Beard, 1953; Rippstein et al., 2001; G. Sarmiento, 1983) consider the Colombian Llanos region to extend to the . However, the area between Vichada River and Guaviare River corresponds to the Branco-Negro forest (Olson et al., 2001). (G. Sarmiento, 1983) also considers areas at the foothills of Venezuelan Andes within the Llanos definition, but according to the ecoregions Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 17 defined by (Olson et al., 2001), these areas corresponds to Apure-Villavicencio dry forests. The other discrepancy between Llanos definitions is whether the Llanos in Venezuela extend over the right bank of the Orinoco River Basin. For the purposes of this thesis, I will adopt the Llanos delimitation proposed by (Trujillo et al., 2010) which divides de Orinoco river basin according to the type of landscape that forms different types of vegetation cover. Figure 1-2 shows llanos delimitation proposed by Trujillo et al., (2010), Olson et al., (2001), Sarmiento, (1983) and land cover over Colombian llanos, which shows that Llanos delimitationn proposed by Sarmiento, (1983) includes forests between Vichada and Vichada river.

Figure 1-2: Llanos region definition. (a) Llanos definition by Trujillo et al., (2010) (b) Llanos definition by Olson et al., (2001) (c) Llanos definition by G. Sarmiento, (1983) (d) Land cover over Colombian Llanos from (Instituto de Hidrología y Estudios Ambientales, 2012). (a) (b)

(c) (d)

18 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 1-3: Llanos region, main Llanos cities and native American Communities in Colombian Llanos. Source: Base map from ESRI, Llanos region Trujillo et al., (2010) and native American Communities from Agencia Nacional de Tierras (https://www.datos.gov.co/Agricultura-y-Desarrollo-Rural/Resguardos-Ind-genas/2wvk- ve5b)

1.3.1 Llanos landscapes

There is a high heterogeneity in the Llanos savannas landscapes and types of vegetation (San José et al., 1998). Main landscapes include alluvial plains, aeolian plains, high plains (both, flat and dissected), rolling hills, fluvial terraces and forest plains. These landscapes are closely related to the origin and mode of deposition of the Llanos surface sediments (Goosen, 1971).

The llanos region was gradually filled with sediments derived from Eastern Colombian Andes, Guyana Plateau and Caribbean Coastal Range through the process of alluvial sedimentation, which occurs by action of rivers where young mountains border low-lying plains (San José, 1998 y Goosen). Most of the sediments date from Late Pleistocene to early Holocene. Near foothills, the coarsest particles are deposited in the form of alluvial fans and then they gradually become a vast alluvial overflow plain. In the eastern part of Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 19 the Llanos, the stronger winds and longest dry season resulted in aeolian processes, in which fine material from freshly deposited alluvia sediments were removed and redeposited in areas south-west of origin, forming the aeolian plains (Goosen, 1971).

Faults along the Andes caused tectonic subsidence between the Andes and and as a result the right-hand of part of Meta River is more elevated than the aeolian plains and much better drained (FAO, 1965; Goosen, 1971). This elevated area constitutes what is called the Colombian high plains (Right-hand bank of Meta river ending at the Cinaruco River). Venezuela also have high plains in is eastern region, in which tectonic deformation, regressive erosion and other processes modified its original sedimentary process topography (Hétier & Falcón, 2003). In a small part of Venezuelan high plains, erosion has shaped plains creating the rolling hills landscape. (Goosen 1971, y Hetier y López, 2003).

These llanos landscapes encompass almost any physiognomic type of savanna. (G. Sarmiento, 1983) presents a detailed description of savanna vegetation of each llanos landscape. Alluvial fans are characterized by the presence of deciduous tropical forest, but savannas constitute a major portion of the landscape (G. Sarmiento, 1983). The alluvial overflow plains are flat areas (gradient less than 1%) characterized by the presence of natural leeves or banks, splay areas and slackwater areas or basins (Goosen, 1971). Natural leeves border the streams and remain unflooded during the rainy season. Its soil formed in sandy alluvium and its vegetation is either gallery forest or seasonal savannas. The latter may be tree savannas on higher banks or treeless grasslands on the lower ones.

The leeves gently change into splay areas, where silty alluvium predominates. These silty areas are mostly treeless savannas. In the slackwater areas, clay soils are predominant. These areas are waterlogged during part of the rainy season due to the accumulation of rain water because of the limited soil drainage of the soils. Its vegetation is either esteros or permanent swamps (G. Sarmiento, 1983).

Aeolian plains have extensive dune fields superimposed on large areas of loess-like (fine- textured) material. Except for the sand dunes, is a flat area and its drainage is poor (FAO, 1965). Dunes have a dry seasonal savanna, with a few trees or entirely treeless. Lowlands have grass and sedge esteros, with moriche palm as its only tree. (G. Sarmiento, 1983)

20 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 1-4: Llanos landscapes. Source: Adapted from (Hétier & Falcón, 2003; Hoyos, 1994)

Colombian high plains vegetation is mainly a seasonal tree savanna. The eastern Venezuelan high plains are also a seasonal tree savanna although a wooded savanna occurs on the steep slopes of the incised valleys (G. Sarmiento, 1983).

Figure 1-5: Colombian high plains savannas. (Source: this thesis.)

Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 21

There is also gallery forest throughout the whole region, bordering almost every water stream. They account for around 10% of total Llanos area in Colombia and 8% in Venezuela (Rondón et al., 2006).

There is high diversity in soil properties among and within landscapes. However, in general, the Llanos soils are acidic, have very low levels of both, organic matter and available nutrients (Rippstein et al., 2001; Rondón et al., 2006). Their nutrient deficiency is in part caused because of the nature of the Llanos parent rocks (FAO, 1965).

1.3.2 Climate and atmospheric circulation

Llanos climate is highly influenced by the Intertropical Covergence Zone (ITCZ), a region close to the equator where the northerly and southerly trade winds converge characterized by ascending air, low atmospheric pressure, deep convective clouds and heavy precipitation (Pacheco & León-Aristizábal, 2001; Poveda et al., 2006; Pulwarty et al., 1998). During the austral summer the ITCZ migrates southward towards the Amazon basin, southern Colombia and Ecuador (Poveda et al., 2006) and thus northeast trade winds (blowing from north-east to south-west) predominate over the Llanos. These winds are strongest between December and April, which is also the period of minimum rainfall. When the ITCZ shifts to the north, the winds abate and the rainy season starts (Goosen, 1971).

Precipitation regime is primarily unimodal in most of the region, except for the Andes foothills where there is a bimodal regime caused by Andean regimes influence (Pacheco & León-Aristizábal, 2001; Pulwarty et al., 1998). There is a precipitation gradient across the Llanos that goes from the Andes to the east. Precipitation decreases from around 3000 mm/year in the southwest Andes foothills (Rippstein et al., 2001) to 900-1300 mm/year in the Orinoco Delta (San José et al., 1998). Dry season length also varies from 1 or 2 months in the southwest Llanos to 5 to 6 months in the east. (Rondón et al., 2006). Figure 1-6a shows the average precipitation rate for the period 1891-2010 using data from NCEP/NCAR Reanalysis, in which the precipitation gradient is observed.

There is a meteorological feature present in the Llanos during the dry season called the Orinoco low-level jet, which is a single stream tube centered around 67.65°W and 7.5°N with a zonal and meridional extension of 1500 km and 500km, respectively. It flows between the Cordillera de la Costa and Guiana highlands and extends parallel to the Andes cordillera 22 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

(Jimenez-Sanchez & Markowski, 2017), transporting air masses between the Orinoco Delta and the Colombian Llanos. This stream shows mean wind speeds of about 11 m/s during the austral summer (November-March) near 925 hPa (~750 MASL), with maximum intensities from January to March (wind speeds of about 14m/s). The maximum wind speeds are reached at 07:00 LST and the minimum at 16:00 LST (Torrealba et al., 2010).

According to data from NCAR/NCEP Reanalysis, climatological mean temperature (1981- 2010) is around 25°C (Figure 1-6b) with the highest temperatures in Venezuelan Llanos during the months March to May. As with the precipitation there is temperature gradient with the highest temperatures in Venezuelan Llanos. Appendix A shows the climatological means of temperature, precipitation rate and wind speed in three-month intervals. Mean relative humidity in Venezuela is about 65% (Hétier & Falcón, 2003) and in Colombian Llanos ranges from 65-90%, being higher in the southwest Llanos (Pacheco & León- Aristizábal, 2001). In the dry season relative humidity falls to values of 50-60% (Goosen, 1971).

Figure 1-6: Climatology average of surface precipitation rate in mm/day (a) and surface air temperature in °C (b) for the period 1981-2010 from NCEP/NCAR Reanalysis (https://www.esrl.noaa.gov/psd/data/composites/day) (a) (b)

Figure 1-7 shows 72-h backward trajectories for the period January-June, 2015. Trajectories were run from middle Colombian Llanos (5 N, 71 W) using the HYSPLIT model (Stein et al., 2015) driven by GDAS meteorological fields. Trajectories were grouped into clusters for each month. It is important to point out that as clustering process was made Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 23 independently for each month, there is no relation between the cluster number and air masses origin among the panels. During January, February and March, most of the trajectories came from northeast. During April and May, around 67% of the trajectories came from northeast and 33% came from the south and were much shorter than trajectories from NE.

Figure 1-7: Backward trajectories for 2015 simulated by HYSPLIT using GDAS1 meteorological data. (a) Daily trajectories grouped in clusters and (b) Percentage of trajectories in each cluster. (Source: this thesis)

(a)

(b)

24 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

1.3.3 Land cover, land use and population

Llanos have historically been used mainly for extensive cattle ranching and subsistence agriculture (Goosen, 1971). In the case of Colombian Llanos, after 1980s, when oil fields exploitation led to the infrastructure development, more intensive agricultural uses started (Etter et al., 2010). By 2007, 23% of the Colombian Llanos underwent land cover change (M. H. Romero-Ruiz et al., 2012). Nowadays, land uses include intensive agriculture, cattle ranching, oil palm and forestry plantations. There has been an intensification of livestock systems, with nearly 2.5 million of introduced pastures. Extensive cattle ranching is still the dominant land use, in which system fire is used as a way of obtaining fresh pasture regrowth (M. Romero-Ruiz et al., 2010). The most recent agricultural and livestock census reports a cattle herd of 4.7 million heads (22% of Colombian bovine heads) for the 4 Departments that constitute Colombian Llanos (Departamento Administrativo Nacional de Estadística, 2014).

In Venezuela, cattle ranching is also the main land use (Rondón et al., 2006), and although in the last decades there is no information available about areas devoted to specific crops or about land management, it is estimated that 26% of savannas are under agricultural use (López-Hernández et al., 2005).

According to the data from land cover map of Colombia (IDEAM, 2013), that uses satellite images of the period 2005-2009, the main land cover in Colombian Llanos corresponds to savannas with 10.5 Mha, represented within the category “3.2 Areas with herbaceous and/or arbustive vegetation”. Converted areas correspond mainly to agricultural areas with 0.86 Mha and pastures, with 1.2 Mha (pastures refer to introduced pastures). Forest comprises about 20% of the Llanos area. The Land cover map is shown in Figure 1-8 and Table 1-2 shows the areas for each land cover category (level 2 of Colombian Corine Land Cover legend). Western Llanos is where most of agricultural and pasture areas occur. Also, there are significant agricultural areas south of Meta River in Puerto Gaitan and Puerto Lopez municipalities in Meta Department.

Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 25

Table 1-2: Land cover in Colombian Llanos for the period 2005-2009 according to Colombia Land Cover Map (Instituto de Hidrología y Estudios Ambientales, 2012) Category code Description Area (ha) 1.1/1.2/1.3 Urban, industrial, commercial areas and mining areas 10,400 2.1 Transitory crops 68,247 2.2 Permanent crops 113,571 2.3 Pastures 1,248,421 2.4 Heterogeneous agricultural areas 686,409 3.1 Forests 3,372,295 3.2 Areas with herbaceous and/or arbustive vegetation 10,514,505 3.3 Open areas with little or no vegetation 490,671 4.1 Wet continental areas 92,126 5.1 Continental water 216,279 99 Clouds 2,544

Figure 1-8: Land cover map for Colombian Llanos for the period 2005-2009 according to Colombia Land Cover Map (Instituto de Hidrología y Estudios Ambientales, 2012)

26 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Since there is no recent land cover information available for Venezuela, the Modis Land Cover information from the MCD12 product was used (Friedl & Sulla-Menashe, 2015). Land cover for the whole Llanos from that MODIS product is presented in Figure 1-9. Great differences are observed for Colombian Llanos when comparing land cover from Colombian land cover map with the MCD12 product. These differences are caused by both, the coarser resolution of MODIS product and the differences in land cover categories. For example, most of the Colombian Llanos that are natural savannas (herbaceous and/or arbustive vegetation under Corine land cover legend) appear as grasslands in MODIS product and only a small area of savanna appears. Also, most of the agricultural areas clearly visible in Colombian Llanos using Colombian land cover map are not identified by the MODIS product. Despite these differences, MODIS product clearly shows that agricultural area in Venezuela is much larger than in Colombian Llanos.

Figure 1-9 Land cover from MODIS Land Cover Product MCD12

Chapter 1. The savanna ecosystem and the Orinoco river basin savannas 27

The population of the 4 Colombian Departments that are included in the Llanos is 1.739.934 people. The main cities are Villavicencio, Yopal and Arauca, the capitals of Meta, Casanare and Arauca Departments. Population of the capitals are presented in Table 1-3.

Table 1-3: Population in the municipalities. Source: DANE.

Municipality Area (km2) Population (2018) Arauca 5,841 92,107 Puerto Carreño 12,409 16,504 Villavicencio 1,328 516,831 Yopal 2,771 149,426 Total 774,868

1.4 References

Archer, S., Boutton, T. W., & Hibbard, K. A. (2001). Trees in grasslands: biogeochemical consequences of woody plant expansion. In Global biogeochemical cycles in the climate system (pp. 115–138). Sand Diego, California: Academic Press.

Beard, J. S. (1953). The Savanna Vegetation of Northern Tropical America. Ecological Monographs, 23(2), 149–215. Retrieved from http://www.jstor.org/stable/1948518

Bilbao, B. a., Leal, A. V., & Méndez, C. L. (2010). Indigenous Use of Fire and Forest Loss in Canaima National Park, Venezuela. Assessment of and Tools for Alternative Strategies of Fire Management in Pemón Indigenous Lands. Human Ecology, 38(5), 663–673. https://doi.org/10.1007/s10745-010-9344-0

Budowski, G. (1956). Tropical savannas: a sequence of forest felling and repeated burnings. Turrialba, 6, 23–33.

Cava, M. G. B., Pilon, N. a. L., Ribeiro, M. C., & Durigan, G. (2017). Abandoned pastures cannot spontaneously recover the attributes of old-growth savannas. Journal of Applied Ecology, (November), 1–9. https://doi.org/10.1111/1365-2664.13046

Departamento Administrativo Nacional de Estadística. (2014). Censo Nacional Agropecuario.

Durigan, G., & Ratter, J. a. (2006). Successional changes in cerrado and cerrado/forest ecotonal vegetation in western S??o Paulo State, Brazil, 1962-2000. Edinburgh Journal of Botany, 63(1), 119–130. https://doi.org/10.1017/S0960428606000357

Durigan, G., & Ratter, J. a. (2016). The need for a consistent fire policy for Cerrado conservation. Journal of Applied Ecology, 53(1), 11–15. https://doi.org/10.1111/1365-2664.12559

Etter, A., Sarmiento, A., & Romero, M. H. (2010). Land Use Changes (1970-2020) and the Carbon Emissions in the Colombian Llanos. In Ecosystem Function in Savvanas. Measurement and modeling at landscape to global scales (pp. 383–402). CRC Press.

FAO. (1965). Soil survey of the Llanos orientales. New York. Rome. 28 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Friedl, M., & Sulla-Menashe, D. (2015). MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. https://doi.org/10.5067/MODIS/MCD12Q1.006

Frost, P. G. H., & Robertson, F. (1987). The ecological effects of fire in savannas. In B. H. Walker (Ed.), Determinants of tropical savannas (pp. 93–140). Oxford: ICSU Press.

Goosen, D. (1971). Physiography and soils of the Llanos Orientales, Colombia. PhD-Thesis University Amsterdam, 199. https://doi.org/10.1016/0016-7061(73)90053-0

Hétier, J. M., & Falcón, R. L. (2003). Tierras llaneras de venezuela, 333–402.

Hoyos, P. (1994). Agricultura en las sabanas de suramerica tropical. Cali, Colombia. Retrieved from http://bibliotecadigital.agronet.gov.co/bitstream/11348/6667/1/66791.pdf

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2. Fire emissions in the Llanos region

Previous studies in the Llanos region have shown that large areas are burned each year during the dry season, mostly through small fires (Armenteras et al., 2005; Romero-Ruiz, 2011). Those fires release many pollutants that may impact regional air quality. Understanding this regional pollution problem requires accurate spatio-temporal disaggregated emission inventories.

Biomass burning emissions are usually estimated following a bottom-up approach that consists of multiplying the emission factor of the pollutant of interest by the amount of biomass burned, which is calculated by multiplying the burned area, the biomass density and combustion efficiency (Seiler & Crutzen, 1980). Among these factors, burned area dominates the uncertainty in emission estimation at scales relevant to regional air quality modeling (Urbanski et al., 2011). Thus, improving the accuracy of emission estimates should focus on reducing the uncertainty on burned area identification.

The objective addressed in this chapter is to quantify and spatio-temporally disaggregate particulate matter and soot emissions produced by biomass burning in Orinoco’s savannas. To achieve that, a methodology for integrating different satellite products was developed in order to be able to detect small fires, and to temporally disaggregate the emissions, as required for its implementation in atmospheric modelling. The inventory was developed for a 1 month-period (January 17 - February 18 2015) that covered one of the air quality measurement campaigns conducted in this project, with the purpose of incorporating the emissions in an atmospheric model and reproduce the observed PM10 enhancements.

This chapter is divided in 6 sections. First section presents approaches for burned area estimation and global burned area products. Section 2, describes the available global emission inventories. Section 3, discusses the challenges associated with emission estimation for savanna ecosystems. Section 4, presents the methodology that was 34 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin developed and followed to build the emission inventory for Llanos savannas. Section 5, presents the results of the inventory, compares them with global inventories and discusses how representative is compared to the typical fire activity of the Llanos region. Section 6 presents the conclusions of this chapter.

2.1 Burned area estimation

Satellite remote sensing has been widely used for monitoring fire activity and estimate burned areas. Satellites follow either polar (e.g. Aqua, Terra, Landsat, Suomi-NPP) or geostationary orbits (e.g. GOES, Meteosat), each having its own limitations and advantages. Geostationary satellites are located at a much higher altitude (35000 km from Earth surface) than polar orbit satellites (~700 km from Earth surface), which limits its spatial resolution. However, they are continuously observing the same point providing high temporal resolution (5 – 30 minutes) (Zhang et al., 2012). Polar orbit satellites have better spatial resolution than geostationary orbit satellites. Overall, the higher the spatial resolution, the less area is covered by a single image, and the larger the swath the higher temporal resolution. For example, Landsat has a spatial resolution of 30 meters and a swath of 185 km, which results in a revisit time (time between 2 successive views of the same area) of 16 days, while Terra has a spatial resolution of 250 m -1 km, a swath of 2330 km, and can provide two views of the same area per day (equator crossing time: 10:30 and 22:30 LT).

These differences in spatial and temporal resolution imply advantages and disadvantages in the context of fire detection. Polar orbit satellites better detect small fires, however, their low temporal frequency results in many fires not being detected when fire activity starts right after the overpass time. On the other hand, geostationary satellites provide the diurnal fire dynamics, but have problems with small fire identification. It has been shown that when comparing simultaneous observations of both type of satellites, geostationary satellites tend to underestimate fire activity (Roberts & Wooster, 2014; Xu et al., 2010) Because of these advantages and limitations some studies use both satellite data in a complementary way (Longo et al., 2010; Pereira et al., 2011; Roberts et al., 2011; Shimabukuro et al., 2014)

Chapter 2. Fire emissions in the Llanos region 35

Satellite observations provide information on fires through the identification of both, active fires at the time of satellite overpass and changes in the surface caused by fire (burned area).

Active fire detection is based on the energy released by fires at middle infrared wavelengths that can be up to 10.000x more intense than non-burning surrounding pixels (Xu et al., 2010). Although these large differences in radiance make possible to detect fires much smaller than the spatial resolution of the remotely sensed data, the spatial extent of fires cannot be reliably detected from active fires. Active fire products identify the presence of fire within the pixel but do not provide burned area. Moreover, fire detections may underestimate fire activity because the satellite may not overpass at the time of the day when the fire occurs, the presence of clouds and smoke may preclude active fire detection and fast moving fire fronts are not captured by the active fire detection (Boschetti et al., 2015). Due to these disadvantages, the majority of burned area algorithms use fire-induced spectral changes to map the spatial extent of fires.

After a fire occurs there is a reduction in the reflectance at visible (0.5 – 0.7µm) to near infrared (NIR) (0.8-1.2 µm) wavelengths associated with the deposition of charcoal (Smith et al., 2007) and a raise in the short-wave infrared reflectance (SWIR) (2.0-2.5 um) associated with the combined effects of the increased soil exposure, increased radiation absorption by charred vegetation and decreased evapotranspiration relative to healthy vegetation (Lentile et al., 2006). Current methods for burned area identification use these spectral changes either by the application of thresholds to spectral indexes over pre-fire and post-fire images, or by implementing algorithms of change detection applied over multi- temporal satellite data (Roy et al., 2013). Burned area mapping based on these fire-induced changes is complex and often highly site and time specific because observed changes depend on the pre-fire vegetation structure, underlying soil reflectance, combustion completeness, vegetation regrowth after the fire and interference with shadows, flooding, agricultural harvesting or ploughing (Boschetti et al., 2015). Because of that site- dependency, spectral indexes methods required the definition of local thresholds for regional applications (L. a. Hardtke et al., 2015; Loboda et al., 2007). Spectral indexes include the Normalized Difference Vegetation Index (NDVI), Global Environment Monitoring Index (GEMI) (Pinty and Verstraete, 1992), Burned Area Index (BAI) (Martin & Chuvieco, 36 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

1998), among others, and can be applied to different products as long as they have the required spectral resolution.

Coarse spatial resolution sensors have been used to generate global burned area products including: Vegetation (VGT) on board the SPOT satellites (recently replaced by PROBA-V sensor on board the PROBA satellite); Moderate Resolution Imaging Spectroradiometer (MODIS), on board the TERRA and AQUA satellites; and Medium Resolution Spectroradiometer (MERIS) on board the ENVISAT satellite. A summary of the burned area products produced from those sensors, including their spatial resolution and a description of their burned area detection algorithms is shown Table 2-1.

MODIS sensor has been used for developing three burned area products: MCD64 (Giglio et al., 2009, 2018) , MCD45 (Roy et al., 2005), both produced by NASA Modis Land Science Team and more recently, the MODIS Fire CCI product (Chuvieco et al., 2018), produced by European Space Agency. MCD64, is considered the most accurate among the operational products, although significant underestimation of burned area has been demonstrated (Padilla et al., 2015).

A spatial and temporal intercomparison of Fire CCI, Copernicus Burnt Area, MCD45 and MCD64 demonstrated that these products do not truly agree on total burned area, burn location and timing (Humber et al., 2018). According to that comparison, MCD64 detects the largest burned area. Fire CCI and MCD45 detect similar amount of burned area, while Copernicus product showed significant discrepancies from the other products.

On a global and annual basis, Copernicus product detected the least amount of burned area, detected less burned area in successive years (as opposed of other products that did not demonstrate a trend over the study period), and had temporal patterns that did not show agreement with the other products. Some of the discrepancies of that product are attributed to the use of a running vegetation index-based time series, which requires the algorithm to exceed a greater threshold as time series expands. Specifically for Northern South America, Copernicus, Fire CCI and MCD45 detected less than 25% of the total burned area reported by MCD64 for the period 2005-2011 (Humber et al., 2018).

Chapter 2. Fire emissions in the Llanos region 37

Table 2-1: Global burned area products Product- Satellite Grid Time Algorithm Producer - size series Reference MCD64 – Aqua and 500 m 2001- Hybrid approach that combines MODIS Terra present active fires and 500 m daily surface NASA Modis Sensor: reflectance input data. It uses a Land Science MODIS vegetation index calculated using short- Team wave infrared channels and applies dynamic thresholds to detect persistent (Giglio et al., spectral changes. Active fires are used to 2009, 2018) guide the statistical characterization of burn-related and non-burn related change. MCD45 Aqua and 500 m 2001- Burned areas are identified by changes in NASA MODIS Terra present the observed reflectance of MODIS band Land Science Sensor: 2 and band 5 compared to a predicted Team MODIS reflectance value based on a Bi- (Roy et al., directional reflectance distribution 2005) function (BRDF) model. Copernicus Proba 300 m 2014- Temporal index made of daily surface burned area Sensor: present reflectance and of averaged surface products PROBA-V reflectance calculated from historical time Vegetation series of the pixel in the near infrared European instrument waveband. A pixel is identified as burned Comission’s if its value lower than the mean value Copernicus minus two standard deviations of the Global Land Temporal Index computed over a 0.5 Service degree window (Tansey et al., 2008) Fire CCI ESA 250 m 2005- Combines temporal changes in near- European Space Envisat 2011 infrared MERIS reflectance with MODIS Agency Sensor: active fire detections in a two-phase (Alonso-Canas MERIS - algorithm. First, active fires are used to & Chuvieco, identify seed pixels and then uses a 2015) region growing algorithm to delimitate burned patches.

MODIS Fire CCI Terra 250 m 2001- Two-phase algorithm based on temporal European Space Sensor: 2016 composites of red and NIR bands of daily Agency MODIS MOD09 reflectance product and MODIS (Chuvieco et al., active fire detections. As in the MERIS 2018) Fire CCI product, active fires are used to identify seed pixels and a second phase uses contextual region growing algorithm to add new burned pixels to each path created in the first phase.

The MODIS Fire CCI product offers greater sensitivity to small fires due the higher spatial resolution compared to other global products. However, it has higher commission and 38 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin omission errors than the latest MCD64-collection 6 (Giglio et al., 2018), which detects around 30% more burned area than its previous collection 5.1.(Chuvieco et al., 2018).

The underestimation of global products is caused by the coarse spatial resolution of the products and the large amount of area burned in small fires (Nogueira et al., 2017; Randerson et al., 2012). Fires smaller than 4-10 product pixel are unlikely to be detected by global burned area products (Padilla et al., 2015). Additionally, global products are not expected to be 100% accurate at local scales since they are designed to provide consistent spatial-temporal mapping at regional scales, their algorithms use conservative thresholds to avoid commission errors (Araújo & Ferreira, 2015) and their prediction capacity is affected by specific land cover types, phenology states or landscape characteristics (Padilla et al., 2015).

Burned area identification based on finer spatial resolution satellites like Landsat (30 meters) permit to map small burned areas and has been used as reference information for the validation of global burned area products (Araújo & Ferreira, 2015; Padilla et al., 2014), regional burned area algorithms (Leonardo a. Hardtke et al., 2015) and for studying fire dynamics (Pereira Júnior et al., 2014; Tarimo et al., 2015).

Using Landsat for burned area mapping has its own challenges since most of the algorithms require human intervention or parameter tuning (Bastarrika et al., 2014; Boschetti et al., 2015) and its temporal resolution is 16 days making it difficult to find a series of cloud-free images to accurately identify the burned area (Laris, 2005). The new tool for supervised burned area mapping using Landsat images (BAMS tool; Bastarrika et al., 2014) uses several spectral indexes and thresholds that are established based on visual delimitation of training polygons defined by the user, allowing the production of accurate burned area maps at local scale.

There are recent efforts for generating high resolution burned area datasets. Landsat images and the computing capacity of Google Engine were recently combined to produce the annual burned area of 30 meter resolution for the year 2015 (Long et al., 2018).

Chapter 2. Fire emissions in the Llanos region 39

2.2 Global biomass burning emission inventories

The most common emission inventories from biomass burning are based on either global burned area products and/or active fire products. They include the Global Fire Emission Database, GFED (G. R. van der Werf et al., 2010; Guido R. Van Der Werf et al., 2017) and the Fire Inventory from NCAR, FINN (Wiedinmyer et al., 2011), both of which are briefly described in the following sections.

There are also emission inventories that are based on an alternative approach of estimating fuel consumption based on fire radiative energy (Integration of satellite-based calculated Fire Radiative Power-FRP). Although first version of that approach reported that the ratio between biomass consumption and energy released was constant and independent of vegetation type (Wooster et al., 2005), GFAS (Global Fire Assimilation System; Kaiser et al., (2012)) inventory developers found that the conversion factor depends on land cover type. This emission inventory assumes that GFED describes the real fire activity and use it to determine the conversion factor between FRP of GFAS and the real dry matter combustion rate (Kaiser et al., 2012). Guido R. Van Der Werf et al., (2017) highlights that there is still substantial uncertainty in this approach that uses a scaling factor to match GFED estimates.

2.2.1 GFED

GFED provides daily and 3-hourly estimations of fire emissions at a 0.25° resolution and is primarily intended for use within large-scale atmospheric models and for interpreting regional and continental-scale controls on fire activity (Giglio et al., 2013). It combines burned area estimations with a biogeochemical model framework that provides estimates of biomass in different carbon pools (e.g. grasses, stems, litter) (Guido R. Van Der Werf et al., 2017). The fourth version of GFED derives burned area from MODIS MCD64A1 c.5.1 product and on its version GFED4s incorporates an algorithm that uses MODIS active fire detections to estimate areas of small fires (Randerson et al., 2012).

A detailed description of the estimation of burned area from small fires is presented in Randerson et al., (2012). Further modifications implemented in the current GFED4s version are described by Van Der Werf et al., (2017). Briefly, burned area from small fires (BAsf) is 40 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin estimated in each 0.25° grid cell (i), month (t), seasonal interval (s) and vegetation type (v) as:

퐵퐴푠푓(𝑖, 푡, 푣) = 퐹퐶표푢푡(𝑖, 푡, 푣) × ∝푟,푠,푣,푦 × 훾푟,푠,푣,푦 (2-1)

Where FCout is the number of active fires outside of MCD64A1 burned areas in each grid cell, α is a ratio of MCD64A1 burned area to active fires within MCD64A1 burned areas, and γ is a correction factor that modifies the burned area associated with FCout as these fires are often associated with smaller burn scars compared to active fires within MCD64A1 burned areas (Randerson et al., 2012). α and γ are estimated each year (y) for each region, seasonal interval and aggregated vegetation type. The γ correction factor is calculated by comparing the difference normalized burned area (dNBR) of active fires observed outside and inside of MCD64A1 burned areas with unburned control areas following:

푑푁퐵푅표푢푡(푟, 푠, 푣, 푦) − 푑푁퐵푅푐표푛푡푟표푙(푟, 푠, 푣, 푦) 훾푟,푠,푣,푦 = (2-2) 푑푁퐵푅푖푛(푟, 푠, 푣, 푦) − 푑푁퐵푅푐표푛푡푟표푙(푟, 푠, 푣, 푦)

Burned area is converted to carbon emissions using the Carnegie–Ames–Stanford approach (CASA) biogeochemical model. Fuel consumption depends on the amount of biomass and combustion completeness that are calculated within the model as a function of satellite-derived plant productivity, fractional tree cover and meteorological datasets including precipitation, temperature and solar radiation. A dry matter carbon content of ap- proximately 48% is used to convert model estimates of fire carbon emissions to dry matter emissions (prior to the application of EFs). Emissions of trace gases and aerosol emissions are then calculating using emission factors mainly based on Andreae & Merlet, (2001) (G. R. van der Werf et al., 2010; Guido R. Van Der Werf et al., 2017)

Distributions of monthly emissions to daily and 3-hourly timescale is achieved by combining active fires from MCD14ML with the day of burning reported by MCD64A1. In tropical regions a 3-day centered mean smoothing filter is applied to adjust for gaps in MODIS coverage. Further disaggregation into 3-hourly timescale is achieved by creating a climatological cycle of burning in each region and for different vegetation types using Chapter 2. Fire emissions in the Llanos region 41 information from GOES-11 (west) and GOES-12 (east) for the period 2007-2009 (Guido R. Van Der Werf et al., 2017).

2.2.2 FINN

FINN (Wiedinmyer et al., 2011) provides daily, 1 km resolution, estimates of fire emissions. This product was designed to provide input needed for modeling atmospheric chemistry and air quality at scales from local to global. It uses MODIS active fire product and assumes burned area at the pixel size (1 km2) except for savanna in which they assume a burned area of 0.75 km2 per pixel.

Type of vegetation is determined by using the MODIS Land Cover, and then, the 16 land cover classes from MODIS are grouped into more generic classes to better match information on fuel loading and emission factors resulting in six categories: (1) savannas and grasslands, (2) woody savannas and shrublands, (3) tropical forests, (4) temperate forest, (5) boreal forest and (6) cropland. Emission factors were taken from available datasets (Akagi et al., 2011; Andreae & Merlet, 2001) and biomass loading were taken mainly from Hoelzemann et al., (2004).

The fraction of biomass assumed to burn is assigned as a function of tree cover, which is obtained from the MODIS Vegetation Continuous Fields product. For areas with less than 40% tree cover, 98% of herbaceous layer is considered to be burned and no woody fuel is assumed to burn.

2.3 Burned area estimation for savanna ecosystems

Burned area estimation in savanna environments must consider that fires in this ecosystem are small and patchy, because they are used for land clearing and pasture management which limits their size to individual plots.

In the case of Colombian savannas previous studies have shown than more than 75% of patches are smaller than 115 ha (Armenteras et al., 2005), a fire size difficult to detect using global burned area products. The underrepresentation of fire activity by active fires products 42 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin is especially true for the case of Northern South American savannas. The overpass time of MODIS Aqua and Terra (10:30 and 22:30 local time for TERRA platform and 13:30 and 01:30 for AQUA platform) misses the fires occurring in late afternoon, when most fires occurred in the Llanos region according to active fire detections from GOES geostationary satellite (Figure 2-1). Also, over the Equator there are data gaps between adjacent MODIS swaths.

Figure 2-1: Diurnal cycle from GOES for Colombian and Venezuelan Llanos. (Source: this thesis from GOES data)

This underrepresentation of fires in the MODIS active fire products makes the use of FINN emission inventory and even the hybrid approaches like the one used by GFED4s (in which burned area is complemented with small fire detection based on active fires) unsuitable for the regional inventories in Northern South America savannas.

Chapter 2. Fire emissions in the Llanos region 43

When emissions are required to be implemented in atmospheric models besides accurate estimation of burned area, the temporal disaggregation is required. Realistic comparisons with surface observations require the emission to be incorporated at daily or even diurnal timescales to reproduce interaction of emissions with atmospheric transport (Mu et al., 2011).

Landsat burned area estimation could overcome the small fire detection challenge (Liu et al., 2018), although the cloud cover may prevent the detection on some areas or periods. Nonetheless, the temporal resolution of Landsat makes this burned area data difficult to use for atmospheric modeling, since it is not possible to identify the day in which emissions occurred. However, if active fire detections are used to attribute dates to Landsat-based burned polygons, it is possible to build an emission inventory that detects small fires with the temporal resolution required for atmospheric modelling.

The main goal of this work is to improve the accuracy of local emission inventories to be used in atmospheric modelling. The approach is based on the combination of Landsat images and MODIS products. This hybrid method takes advantage of the Landsat capabilities to detect small fires, MODIS reflectance to detect burned areas beneath the cloud cover in Landsat images, and MODIS active fire product to temporally disaggregate the emissions.

2.4 Methodology

Emissions were calculated following the bottom-up approach of multiplying the emission factor for particulate matter by biomass loading, combustion efficiency and burned area, BA. Among these factors, burned area dominates the uncertainty in emission estimation at scales relevant to regional air quality modeling (Urbanski et al., 2011). Thus, emission factors, biomass loading and combustion efficiency were taken from available information in the literature and the work focused on developing a hybrid approach to estimate BA and its temporal disaggregation. Methodological steps of the biomass burning emission inventory are summarized in Figure 2-2.

The burned area identification on Landsat images was done using the Burned Area Mapping Software - BAMS (Bastarrika et al., 2014)Figure 2-2, step i). This tool uses several 44 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin spectral indexes and thresholds that are established based on the visual delimitation of training polygons defined by the user, allowing the production of accurate burned area maps at the local scale.

Figure 2-2: Flowchart of the biomass burning emission estimation. (i) Landsat burned area identification using BAMS tool (ii) Burned area identification based on the application of spectral indexes (iii) Landsat and MODIS burned area integration (iv) Active fires-based burned area (v) emission estimation and (vi) date attribution. (Source: this thesis.)

One of the problems when using Landsat for BA identification is that some areas are not observed due to the cloud cover. In these areas, BA was estimated through the application Chapter 2. Fire emissions in the Llanos region 45 of spectral indexes over MOD13Q1 product (Figure 2-2, step ii). Thresholds were defined based on calculated spectral indexes over burned polygons previously identified as burned in Landsat scenes.

MOD13Q1 product (MODIS/Terra Vegetation Indices 16-Day L3 Global) was chosen over other available MODIS reflectance products because as a composite of 16 days minimizes the effects of cloud cover. Moreover, the product has a resolution of 250 m, which permits the detection of smaller fires compared to the 500m products. However, this implies that only bands 1 (Red) and 2 (NIR) are available, which limits the number of spectral indexes for burned area detection that can be applied.

The NDVI is an index itself and comes at a resolution of 250 m. NDVI and two more burned area identification indexes that can be calculated using the red and NIR bands were used: The Global Environment Monitoring Index (GEMI) (Pinty & Verstraete, 1992) and the Burned Area Index (Martin & Chuvieco, 1998).

Landsat and MODIS burned polygons required two steps for their integration (Figure 2-2, step iii). First, Landsat polygons were taken to MODIS time intervals. Then, polygons were selected based on Landsat image quality. The methodology applied kept only Landsat polygons were Landsat images were of good quality and kept MODIS polygons where no Landsat identification was possible due to the quality of the image.

MODIS active fire detections from the MCD14ML product (Figure 2-2, step iv) were used as a mean of incorporating fires that could have been omitted by the BA algorithm applied over MOD13Q1 in areas with low Landsat quality.

Then, to assign a date to the burned polygons, active fire detections were used through a two-step process (Figure 2-2, step vi). First, overlapping active fire detection and burned areas were identified. Then, for the remaining areas, where no active fires were detected, emissions were distributed based on the daily fraction of emissions as calculated from MCD14ML active fire detections (Giglio et al., 2006) during the emission inventory period following the approach used by Mu et al., (2011) for disaggregating monthly GFED3 emissions into daily time step.

46 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

2.4.1 Landsat burned area identification

The burned area identification over Landsat 8 OLI-TIRS scenes was done by using BAMS (Bastarrika et al., 2014). BAMS calculates several spectral indexes for burned area identification in a two-phase supervised methodology. The first phase uses strict criteria to identify the clearly burned pixels (seed pixels), while the second phase analyze the vicinity of the seed pixels using more flexible criteria to identify neighboring pixels as burned.

Criteria are established by using training polygons visually identified by the user. A pair of scenes (pre-fire, post-fire) are processed (the tool calculates the spectral indexes) and then the user draws training polygons in areas that were burned after the pre-fire scene. Due to the strong reflectance of burned areas in the shortwave infrared (SWIR band), they are easily identified as red areas in a false color image by using band 7-SWIR as red, band 5- NIR (Near Infrared) as green and band 3-red as blue (Figure 2-3). Seed polygons should be located in small and quite homogeneous areas, with strong burned signal. Second- phase polygons are bigger than seed polygons and should be located in areas that show low severity. The tool uses the criteria established by the training polygons to generate burned polygons over the entire scene. Then, the user inspects the resulting polygons and adds new polygons or modifies the existing ones if necessary. As a result, a threshold file is created that can be used for identifying burned areas over other scenes.

The process of establishing the thresholds was done for each of the scenes covering the study area for the period January – February 2015. Then, the resulting threshold file was used for burned area identification in other time periods of the same scene (at least three scenes were used for covering the emission inventory period). Areas covered by clouds are not considered in the BA identification done by BAMS. The tool masks out cloud areas using the Quality Assessment band of Landsat 8. For those regions, we implemented a methodology similar to the one used by BAMS (Bastarrika et al., 2014) but using the MOD13Q1 vegetation indices product instead of Landsat images.

Chapter 2. Fire emissions in the Llanos region 47

Figure 2-3: Example of burned area identification over Landsat scenes using BAMS. Red polygons are burned areas that occurred between February 23 and February 7. (Source: this thesis) February 7, 2015 February 23, 2015

2.4.2 MODIS burned area identification

MODIS burned area identification was done through the use of spectral indexes applied over the MOD13Q1 product that included NDVI, and two more burned area identification indexes that can be calculated using the red and NIR bands (Table 2-2).

Table 2-2: Burned area identification spectral indexes calculated over MOD13Q1 product Global 𝜌 − 0.125 𝜌 is the 퐺퐸푀퐼 = 휂 ∗ (1 − 0.25휂) − 푅퐸퐷 푁퐼푅 Environment 1 − 𝜌푅퐸퐷 reflectance in 2 2 Monitoring Index 2(𝜌푁퐼푅 − 𝜌푅퐸퐷 ) + 1.5𝜌푁퐼푅 + 0.5𝜌푅퐸퐷 the NIR (MODIS (GEMI) (Pinty & 푤ℎ푒푟푒, 휂 = band 2) and 𝜌푁퐼푅 + 𝜌푅퐸퐷 + 0.5 Verstraete, 1992) 𝜌푅퐸퐷 is the Burned Area Index 1 reflectance in 퐵퐴퐼 = 2 2 (Martin & (0.1 − 𝜌푅퐸퐷) + (0.06 − 𝜌푁퐼푅) the red (MODIS Chuvieco, 1998) band 1).

Temporal differences of those three indexes (dNDVI, dGEMI and dBAI) were also calculated.

48 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

We followed the same two-stage approach of BAMS tool (seed pixels and second stage pixels). Thresholds were established based on the value of the indexes in burned polygons previously identified as burned over Landsat images. We selected the thresholds using the Landsat scene path 6 row 57 for the period February 7 – February 23, 2015. MODIS intervals covering that Landsat period were February 2 – February 18 – March 6, 2015. Since Landsat and MODIS images had different time intervals it was necessary to identify which Landsat polygons were burned in the MODIS interval 1 (February 2-18) and interval 2 (February 18 – March 6). This was done by calculating the average of dNDVI within each Landsat polygon. Polygons where the dNDVI for interval 1 (dNDVI February 18 – NDVI February 2) was less than dNDVI for interval 2 (February 18 – March 6) were considered burned in the interval 1 (Figure 2-4). Polygons greater than 4 MODIS pixels burned in the interval 1 were chosen for the threshold estimation.}

Figure 2-4: Selection of reference burned polygons for threshold definition. L0 represents Landsat pre-fire image of day 38 (February 7, 2015) and L1 represents Landsat post-fire image of day 54 (February 23, 2015). That Landsat interval corresponded to two MOD13Q1 intervals: MO-M1, February 2-February 18, 2015 and M1-M2, February 18-March 06, 2015. Difference of NDVI was calculated between MO-M1 (dNDVI-4933) and between M1-M2 (dNDVI-6549). Polygons having less dNDVI in M0-M1 interval than in M1-M2 interval were burned between February 7 and February 18 and were used as the reference burned polygons. (Source: this thesis).

Chapter 2. Fire emissions in the Llanos region 49

For NDVI and GEMI, fire decreases the pre-fire value, so a maximum value must be set, below which the pixel is considered burned. BAI, on the other hand, increases with fire, so a minimum value must be set, above which the pixel is considered burned. For NDVI and GEMI indexes, seed pixel criteria were defined by constructing a cumulative histogram of the minimum index value within the reference polygons and chose as the threshold the value corresponding to the 80% of cumulative frequency (Figure 2-5, a). For second stage, the same procedure was followed, but using the cumulative histogram of the average index value instead of the minimum value within the polygons (Figure 2-5, b).

Figure 2-5: Threshold definition for GEMI index. a) Cumulative histogram of minimum index value within the reference burned polygons used to define the seed pixel threshold. A pixel is considered as burned if its index is less than 0.41, that corresponds to the 80% of cumulative frequency. b) Cumulative histogram of average index value within the reference burned polygons used to define the second stage threshold. A pixel is considered as burned if index is less than 0.46, that corresponds to the 80% of cumulative frequency. (Source: this thesis)

b)

frequency

frequency

Cumulative relative relative Cumulative Cumulative relative

Minimum index within burned polygon Average index within burned polygon

For BAI, since it increases with fire, the seed pixel criteria were defined by constructing a cumulative histogram of the maximum index value within the reference polygons, and the threshold was established as the value corresponding to the 20% of the cumulative frequency. As for NDVI and GEMI, this ensures that at least 80% of the reference polygons will have a seed pixel. The second phase criteria for BAI, was established using the value 50 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin of the 20% of cumulative frequency using a cumulative histogram of the average value of the index within the polygons. Resulting thresholds are shown in Table 2-3.

A pixel was identified as seed pixel or a second-phase pixel if it met the threshold established for all the six indexes. Two polygon layers were created from the seed pixels and second-phase pixels. Then, only the second-stage pixels that have a seed pixel were retained as the final burned polygons.

Figure 2-6: (a) Example of seed and second stage pixels using the GEMI, NDVI and BAI index and (b) resulting burned area identified using spectral indexes over MOD13Q1. (Source: this thesis).

(a)

(b)

Chapter 2. Fire emissions in the Llanos region 51

Table 2-3: Threshold for burned area identification using spectral indexes. (Source: This thesis)

Index Seed pixel Second stage GEMI < 0,41 < 0,46 dGEMI < -0,12 < -0,05 BAI > 120 > 70 dBAI > 70 > 20 NDVI < 3.100 < 4.000 dNDVI <-2.400 < -1.600

2.4.3 Landsat and MODIS integration

Landsat and MODIS burned polygons required two steps for their integration. First, Landsat polygons were adjusted to MODIS time intervals. Then, polygons were selected based on Landsat image quality. The methodology applied kept only Landsat polygons were Landsat images were of good quality and kept MODIS polygons where no Landsat identification was possible due to the quality of the image.

The integration processing was done to each Landsat scene covering the study area. Figure 2-7 shows the methodology for assigning MODIS intervals to Landsat polygons. Each Landsat (L1-L2) interval corresponds to two MODIS intervals (M1-M2 and M2-M3). The average dNDVI was calculated within the Landsat polygon for the two corresponding MODIS intervals. Then, the dNDVI was compared; the smallest value indicated that the polygon was burned in that interval. The new burning interval was defined by L1-M2 if burned in the first MODIS interval or M2-L2 if burned in the second MODIS interval.

The goal was to keep MODIS polygons only in the areas where Landsat quality was not good and keep Landsat polygons in areas with good Landsat quality. In areas where only one image had bad quality, Landsat and MODIS polygons were compared and Landsat polygon was taken if MODIS polygon area was smaller than 1.4 of the Landsat polygon. This was done because it was observed that sometimes clouds could have caused Landsat polygon to be partially captured. If these were the case and the fire was bigger than the polygon identified by Landsat, MODIS polygon would be much bigger and was assumed to be the true area. 52 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 2-7: Methodology for assigning MODIS intervals to Landsat polygons. L1 refers to Landsat pre-fire image. L2 refers to Landsat post-fire image. M1-M2 and M2-M3 refers to the two intervals partially included within the Landsat interval. (Source: this thesis)

Figure 2-7 shows the methodology for integrating Landsat and Modis burned polygons. First, Landsat quality band was integrated over the three images corresponding to a MODIS interval (integrated Landsat quality layer). Pixels with good quality in all the three images were given a value of 1. Pixels with bad quality in any of the three images were given a value of 0.

To implement those selection criteria, the majority value (1 or 0) was calculated within each MODIS polygon. The MODIS polygons in which the majority was 1 (good quality in the three Landsat images) were discarded. Then, spatial selection of intersecting LANDSAT and MODIS polygons was made and those polygons were processed separately from the not intersecting polygons.

From the no intersecting polygons, two layers were obtained. The first contained MODIS polygons in the areas with low Landsat quality (MODIS without LANDSAT layer) and the second contained Landsat polygons (Landsat without MODIS layer), that corresponded to Chapter 2. Fire emissions in the Llanos region 53 either good Landsat quality (MODIS polygons discarded) or burned areas not identified by the algorithm used over MODIS images.

The area of intersecting polygons was compared and when MODIS polygon area was greater than 1.4 MODIS polygon was kept (MODIS with LANDSAT layer) and Landsat polygon was discarded. When it was less than 1.4, Landsat polygon was kept (LANDSAT with MODIS layer). Then, the layers of MODIS without Landsat, Landsat without MODIS, MODIS with LANDSAT and LANDSAT with MODIS were integrated.

Figure 2-8: Integration of Landsat and MODIS polygons. (Source: this thesis)

2.4.4 Burned area from active fire data

MODIS active fire detections from the MCD14ML product were also used as a mean of incorporating fires that could have been omitted by the BA algorithm applied over MOD13Q1 in areas with low Landsat quality. To avoid false detections, only active fires 54 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin with confidence level higher than 70 were considered. For each fire detection, a buffer of 500 m (pixel size) was assumed to represent the BA. Detections in multiple days over the same area were discarded, since they may have corresponded to gas flares rather than biomass burning. Gas flare pixels identified by the NOAA global gas flaring database ( (https://ngdc.noaa.gov/eog/viirs/download_global_flare.html; Elvidge et al., 2016) were also discarded.

A spatial selection of polygons that did not overlap the burned area identified by MODIS or LANDSAT polygons was made and the resulting polygons were merged with the MODIS and LANDSAT polygons in a unified burned area layer.

2.4.5 Emission calculation

Emissions were calculated following the bottom-up approach of multiplying the emission factor of particulate matter by the amount of biomass burned. The latter was calculated by multiplying burned area, biomass density and combustion efficiency (Seiler & Crutzen, 1980). Emission factors, biomass density and combustion efficiency depend on the type of vegetation that was burned.

Due to the data availability of information, different information sources for Colombian and Venezuelan Llanos were used. For Colombia, a more detailed approach was implemented using the most recent available land cover information Corine Land Cover 2005-2009 (Instituto de Hidrología y Estudios Ambientales, 2012). Land cover categories were aggregated into more general classes that better match the available information on biomass density, combustion efficiency and emission factors (Akagi et al., 2011; Etter et al., 2010). For Venezuela, MOD12 land cover product was used. Biomass density and combustion efficiency for the different savanna ecosystems in Colombia were taken from Etter et al., [2010]. For categories not included in that reference, biomass density and combustion efficiency from other sources were used (Akagi et al., 2011; Brown et al., 1997; Hoelzemann et al., 2004).

Emission factors were taken from Andreae and Merlet [2001] and Akagi et al. [2011]. Table 2-4 and Table 2-5 show the emission factors for PM10, biomass loading, and combustion efficiency used for each land cover category for Colombia and Venezuela, respectively. Chapter 2. Fire emissions in the Llanos region 55

Table 2-4: Corine land cover categories and their associated biomass loading, combustion efficiency and PM10 emission factors Emissio Biomas Source Source n factor Source Corine land Categor s Combustio biomass combustio (g/kg emission cover category y loading n efficiency loading n efficiency biomass factor (ton/ha) burned) Etter et al., Andreae y 2010 - Merlet, Natural dense Overflow Etter et al., Alluvial 2001 TPM overflow plain 7 0.7 8.3 2010 overflow - Savanna grassland savannas plain and savannas Grassland Rice fields Cropland Andreae y Cropland/natura Akagi et al., Akagi et al., Merlet, l vegetation Crop 1.4 2011 - 0.8 2011 - 13 2001 TPM- mosaic residue Cereal crop Cereal crop Agricultura Cropland/natura l residues l vegetation mosaic Natural dense Andreae y grassland Merlet, Flat Etter et al., Etter et al., 2001 TPM Sparsely highplain 6 0.85 8.3 2010 2010 - Savanna vegetated areas savannas and Burned areas Grassland Akagi et Gallery Hoelzemann al., 2011 Gallery and and Brown et. al et al., 2004 217 0.5 13 TPM- riparian forests riparian (1997) (Tropical Tropical forest forest) forest Pastures/natural vegetation Andreae y mosaic Hoelzemann Merletl, Pastures/crops et al., 2004 - Etter et al., 2001 TPM mosaic Pastures 8 0.85 Savanna 8.3 2010 - Savanna Pastures with and and trees and grasslands Grassland shrubs Pastures Sparsely vegetated áreas dreae y Merlet, Open grassland Sandy Etter et al., Etter et al., 2001 TPM on sands 3.5 0.95 8.3 savanna 2010 2010 - Savanna Sparse and vegetation on Grassland sands Hoelzeman n et al., Akagi et Hoelzemann 2004 - al., 2011 Tropical et al., 2004 Dense forest 255.1 Tropical 0.5 13 TPM- forest (Tropical forest of Tropical forest) South forest America

56 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 2-4 (Continuation)

Emission Biomass Source Source factor Source Corine land Combustion Category loading biomass combustion (g/kg emission cover category efficiency (ton/ha) loading efficiency biomass factor burned) Open shrublands Akagi et Woody Hoelzemann Hoelzemann al., 2011 Fragmented 30.74 0.6 15.4 savanna et al., 2004 et al., 2004 TPM - forest Chaparral Wetlands

Table 2-5: Modis land cover categories and their associated biomass loading, combustion efficiency and emission factors Emissio Biomas Reference Reference n factor Reference Modis Categor s Combustio (biomass (Combustio (g/kg (Emission category y loading n efficiency loading) n efficiency) biomass factor) (ton/ha) burned) Evergreen Hoelzeman Hoelzemann Broadleaf forest n et al., FINN and et al., 2004 - Deciduous 2004 - Akagi et. al Tropical Tropical Broadleaf forest 255.1 Tropical 0.5 13 (2011) forest forest of forest of Tropical South Mixed forest South forest America America Closed shrublands Open Hoelzeman Akagi et al., shrublands Woody Hoelzemann 30.74 n et al., 0.6 15.4 2011 TPM - Woody savanna et al., 2004 2004 Chaparral savannas Permanent wetlands Etter et al., Etter et. al 2010 - (2011) - FINN and Savanna Alluvial Alluvial Andreae Savannas 7 0.7 8.3 s overflow overflow and Merlet plain plain (2001) savannas savannas Akagi et. al (2011) Etter et al., Etter et. al Grasslands Pastures 8 0.85 8.3 Pasture 2010 (2011) mainteinanc e Croplands FINN (Cropland) Akagi et al., and Cropland/Natur Crop Akagi et. al 1.4 2011 - 0.8 13 Andreae al vegetation residue (2011) Cereal crop and Merlet mosaic (2001) Crop residue

Chapter 2. Fire emissions in the Llanos region 57

Andreae and Merlet [2001] and Akagi et al. [2011] do not report PM10 emission factors for savannas. Andreae and Merlet [2001] reported emission factors of 8.3 g PM10/kg of dry matter and 5.4 g PM10/kg of dry matter. Akagi et al. [2011] presented an emission factor of 7.17 g PM10/kg of dry matter and report that “PM2.5 is usually close to 80% of PM10 or Total particulate matter (TPM) when measured on the same BB sample”, thus indicating that TPM emissions are undistinguishable from the PM10 ones. Following that assumption, FINN inventory uses a PM10 emission factor of 8.3 g PM10/kg of dry matter (Christine Wiedinmyer, personal communication). For this inventory, the same approach was followed. Black carbon and PM2.5 emissions were calculated using emission factors from Andreae and Merlet [2001] and Akagi et al. [2011] for the same categories reported for PM10 in Table 2-4 and Table 2-5.

2.4.6 Date attribution

MODIS active fires were used to attribute dates to the burned polygons in two ways. First, active fire detections that overlapped burned polygons were considered the true burned date. Then, polygons that had no active fire detection were assigned a date following a procedure similar to the one used to distribute monthly GFED3 emissions into daily time steps (Mu et al., 2011). This methodology distributes the emissions based on the daily fraction of emission using a 3-day center mean of MODIS active fire detection in 0.5° grid cells.

For the first date attribution method, active fire detections from MODIS, GOES, NPP, and NPP375 were used. First, a buffer was made to represent the size of the pixel of each product (500 m for MODIS, 1100 m for GOES, 375 m for NPP, and 187 m for NPP375). Then, a spatial join (one to many) of the MODIS and Landsat burned polygons with the layer of active fires was made. Polygons that had a difference of more than one day between the active fire detections were identified and manually inspected to assign the date or dates (some polygons had to be manually divided because corresponded to two different dates).

The polygons that could not be attributed a date by active fire detections were aggregated in 0.25° grid cells. For estimating the daily fraction of emissions, the study area was divided 58 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin in cells of 0.5° X 0.5° following the methodology proposed by Mu et al., (2011). The number of MODIS AQUA and TERRA active fires was counted within each cell for each day of the emission inventory period (January 17 – February 18, 2015). With that time series, a 3-day centered mean of fire detections was calculated for each cell and used that time series to calculate the daily fraction of emissions (dividing the value of the moving average for a given day by the sum of daily moving averages within the period).

This approach of taking the 3-day mean rather than the number of fires in the day has been useful to represent the fire activity in the tropics (better correlation with geostationary fire detections) (Mu et al., 2011), overcoming the underrepresentation of fires caused by MODIS overpass gaps. Finally, the daily fraction was disaggregated into 0.25° cells and multiplied by the emissions.

2.5 Results

Burned area in the Llanos region during the study period was 628,828 ha, corresponding to emissions of 39,204 ton of TPM, 29,986 ton of PM2.5 and 2,262 ton of black carbon. Figure 2-9 (a) shows burned area gridded to 0.25° x 0.25° cells and (b) burned polygons. Most of the burned area identified by this inventory occurred in Flat High Plains, Aeolian Plains and Colombian Dissected High Plains Colombian.

There were two regions in Venezuela where very low burned area was detected, North Eastern dissected plains and center part of the alluvial plains. The former could be explained by the low quality of both Landsat and Modis images during the study period, which limited burned area identification to the detection of active fires. The latter occurred mainly within the alluvial plains, more specifically in partial (A2) and total (A3) overflow areas of the following the classification of Hétier & Falcón, (2003). That region, according to MODIS, clearly has a different land cover and corresponds to areas classified as Cropland/Natural vegetation mosaic Figure 2-9 (d).

Number of fires, burned area and TPM emissions per land cover type are shown in Figure 2-10. Grasslands and savannas were the land cover where most of the fires and burned area occurred, representing 91% of total burned area.

Chapter 2. Fire emissions in the Llanos region 59

Figure 2-9: Burned area in the Llanos region for the period January 17 – February 18, 2015. (a) Burned area aggregated in cells of 0.25°. (b) Burned area polygons and Llanos Landscapes (c) Detailed Llanos landscape classification (Hétier & Falcón, (2003) within Venezuelan Alluvial Plains with low burned area (d) MODIS Land cover. (Source: this thesis) (a) (b)

(c) (d)

Differences in the proportion of fires between savannas and grasslands in Colombia and Venezuela should be taken with caution since different land cover information was used. Corine land cover information includes savanna ecosystems within the category of areas with herbaceous vegetation and considers pastures/grasslands as a crop (mainly introduced pastures) used in cattle grazing. On the other hand, grassland category in the Modis land cover product corresponds to lands with herbaceous types of cover, where tree and shrub cover is less than 10%, which may correspond to some savanna types like 60 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin natural dense grassland without trees (Category 321111 on Corine land cover). Savanna burned areas in Colombia corresponded mainly to natural dense grassland and natural dense overflow grassland categories with 188,086 ha and 114,629 ha respectively.

The forest in Colombia and woody savannas in Venezuela, although represented only 0.3% and 5.2% of total burned area, accounted for 7% and 23% of TPM emissions respectively, due to the biomass loading of these land cover categories. Fires in cropland areas were higher in Venezuela than in Colombia, but due to its low biomass loading (agricultural residues) did not represent a significant amount of emissions.

Figure 2-10: Number of fires, burned area and TPM emissions for each Modis land cover category. Colors in stack bars represent the more detailed land cover category from Corine land cover for Colombia. Venezuela only had Modis category and is shown at the bottom of each bar. (Source: This thesis).

Chapter 2. Fire emissions in the Llanos region 61

Most of the fires were small. In Colombia, 3585 fires were under 100 ha, corresponding to 83% of burned polygons and 32% of total burned area. Venezuela also had high number of fires under 100 ha (3151 fires corresponding to 86% of burned polygons and 39% of total burned area). For Venezuela most of the detections of fires of sizes between 50-100 ha were based on active fire detections.

Figure 2-11: Number of active fires per country (a) Colombia, b) Venezuela and percentage of area represented by each fire size interval at (c) Colombia and (d) Venezuela. (Source: This thesis)

In Colombia, 60.2% of TPM emissions corresponded to burned areas that did not have an overlapping active fire. Hence, its date distribution was based on the daily fraction of active 62 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin fires. This high percentage of emissions without overlapping active fires demonstrates that active fire detections from MODIS under-represent fire emissions in the Llanos area and adds high uncertainty when using the emission inventory for atmospheric modelling. In Venezuela, date attribution based on daily fraction of active fires was necessary for 47.2% of TPM emissions. The higher percentage of TPM emissions with a true date (overlapping burned area and active fire detections) is because, as mention above, Venezuela had a higher number of burned polygons that were based on active fire detections.

Daily distribution of emissions is shown in Figure 2-12. Temporal dynamics of emissions were different in both countries. Colombia’s highest emissions occurred during January 28, February 6 and February 17-18, days in which daily emissions were above 1000 ton. Venezuela’s higher emissions were in the period January 27-30, with a peak emission of 2281 ton on January 27.

Figure 2-12: Daily TPM emissions per country. True date corresponds to burned areas that were overlapped by active fires and assigned dates correspond to burned areas with no active fire detection that were attributed a date based on a daily fraction of active fires calculated using 3-day centered mean in 0.5° cells. (Source: this thesis).

The total burned area estimated with the methodology presented in this work are higher than reported by GFED, and total emissions are also higher than reported GFED and FINN. Table 2-6 shows the burned area and emissions of the three inventories by country. In Colombia, HDEZ burned area and emissions are higher than GFED estimates, but for Venezuela HDEZ inventory detected less burned area. In HDEZ inventory Venezuelan Chapter 2. Fire emissions in the Llanos region 63

Llanos burned area accounted for 48% of total burned area, while in GFED represents 65% of total burned area. These differences suggest limitations of the proposed methodology for burned area detection in Venezuelan Llanos.

Figure 2-13 (a) shows the difference in burned area. Green cells show cells in which HDEZ burned area was lower than GFED area. Most of this occurred in the northern part of the Venezuelan Llanos region. As previously mentioned, this may be explained by the fact that Landsat scenes over that area had bad quality and could not be used. Also, MODIS had quality problems, resulting in burned area estimations relying only on active fire detections, which lead to an underestimation of burned area in HDEZ inventory.

Figure 2-13 (b) shows differences in TPM emissions between HDEZ and GFED inventories. In general, in Colombia HDEZ emissions were greater than GFED estimates. In Venezuelan Llanos, the southern part HDEZ emissions were higher than GFED, and some areas of northern Llanos had lower emissions due to differences in burned area. In northern part, there were some cells having higher emissions in areas having lower burned area which can be explained by differences in land-cover associated factors.

Table 2-6: Burned area and emissions from the emission inventory developed in this work (Hernandez; HDEZ), the Global Fire Emission Database (GFED) and FINN inventory for the period January 17 – February 18, 2015

Inventory Colombia Venezuela Total

HDEZ (ha) 328,634 299,966 628,600 Burned area GFED (ha) 177,891 336,588 514,479

HDEZ (t) 16,953 22,251 39,204

Emissions GFED (t) 8,571 14,839 23,410

FINN (t) 4,703 7,026 11,729

For Colombia, burned area in HDEZ inventory was 1.8 times higher than GFED, and emissions were 2 times higher than GFED estimates and 3.6 times higher than FINN estimates. GFED emissions were 1.8 times higher than FINN estimates, which demonstrates that relying on MODIS active fire detection for Llanos savannas greatly underestimates burned area and its associated emissions. 64 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 2-13: Differences in (a) burned area and (b) emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED). (Source: this thesis). (a) (b)

(c) (d)

The ratio between burned areas is highly variable. Some areas in Colombian high plains HDEZ burned area was more than 5 times GFED area. This variability caused by the no- detection of small fires makes global inventories difficult to scale.

Emissions were calculated for a 1-month period (January 17 – February 28, 2015) with the purpose of implementing those emissions in an atmospheric model to understand the particulate matter enhancements observed in a measurement campaign conducted in the Llanos region. However, those monthly emissions do not represent what typically occurs in Chapter 2. Fire emissions in the Llanos region 65 the Llanos region. The 2014-2015 dry season started later than during a typical year. Usually, in Colombian Llanos, January and February are the months that exhibit the highest fire activity, but during 2015 the highest fire activity was observed in March. Figure 2-14 a shows GFED fire emissions (expressed as Tg C) for the period 2007-2015. Compared with previous years, 2015 was a year with low fire emissions, and January and February had the lowest emission of the previous years (Figure 2-14 b).

Figure 2-14: GFED carbon emissions in the Llanos region for the period 2007-2016 (a) monthly emission by category (b) Boxplot of monthly emissions for the period 2007-2015. (Source: this thesis using data from GFED).

2.6 Conclusions

A methodology for integrating different approaches for burned area identification in savanna ecosystems was presented. The methodology was mainly based on Landsat images to ensure small fires identification and areas having low quality Landsat images were complemented with MODIS-based burned area identification. Date attribution was made using active fires detection either by overlapping fires and burned polygons, or daily fraction of fire detections.

The methodology performed better over Colombian Llanos than over Venezuela, mainly due to cloud cover over some areas in Venezuela that did not allow burned area 66 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin identification using spectral index methods, resulting in burned area estimations relying only on active fire detections, which lead to an underestimation of burned area.

Small fires play an important role in burned area and fire emissions. Fires smaller than 100 ha accounted for 83% and 86% of total burned polygons in Colombia and Venezuela, respectively. The fact that small fires are not properly detected in global product methodologies makes the use of a single scaling factor difficult. Also, the high variability in the cell to cell ratio of burned area between emission inventories suggests that global emissions products are difficult to scale.

Fire emission dynamics in the Llanos area remain poorly understood, even when integrating different satellite products. The HDEZ emission inventory approach, although able to detect small fires, relied on active fire detections to temporally disaggregate the emissions. The high percentage of emissions without overlapping active fires demonstrates that active fire detections under-represent fire emissions in the Llanos area and cause high uncertainty when using the emission inventory for atmospheric modelling.

2.7 References

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Roberts, G., & Wooster, M. J. (2014). Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation. Remote Sensing of Environment, 152, 392–412. https://doi.org/10.1016/j.rse.2014.06.020

Roberts, G., Wooster, M. J., Freeborn, P. H., & Xu, W. (2011). Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory. Remote Sensing of Environment, 115(8), 2047–2061. https://doi.org/10.1016/j.rse.2011.04.006

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Seiler, W., & Crutzen, P. J. (1980). Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change, 2, 207–247.

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72 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

3. Evidences of the impact of fire emissions on air quality levels

During a biomass burning season, the Llanos region is located north of the Intertropical Convergence Zone (ITCZ), which causes the predominance of northeast trade winds over the area. High speed winds transport the emissions over longer distances which may have an impact on southwest of Orinoco savanna and may also affect highly populated areas such as Bogota in Colombian Andes. Previous studies in Venezuelan Orinoco region showed an evidence of enhanced atmospheric levels of trace gases, particles and ozone related to biomass burning (Sanhueza et al., 1985, 1987; Sanhueza & Fernández, 2000; Sanhueza & Rondón, 1988). However, no assessment had been made of the impact of biomass burning emissions on Colombian Orinoco region air quality.

The objective addressed in this chapter is to evaluate the correlation between concentration and composition of particulate matter under low and high influence of biomass burning in Colombian Llanos. To accomplish that, two measurement campaigns were made in sites with no urban sources influence at the beginning and during the dry season, and chemical composition of some of the samples was analyzed. Additionally, data from a third measurement campaign in Yopal and Arauca were analyzed. That measurement campaign was made by the environmental authority in measurement sites within the urban area of those cities during April and May 2015, after the end of dry season in Colombia but when there was still fire activity in Venezuela. The analysis of those data revealed a transboundary pollution problem that significantly deteriorates air quality in Yopal, Arauca and potentially other cities downwind. Those results are presented in the manuscript “Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Basin” in Chapter 4.

Chapter 3. Evidences of the impact of fire emissions on air quality levels 73

This project also included the installation and operation of a sun photometer from the Aerosol Robotic Network, AERONET of NASA. This equipment was installed from March 2015 to July 2016, however, due to technical problems and also because the equipment was not available during part of the time, there was no simultaneous measurements with PM in campaigns 1, 2 and 3. In spite of these limitations, biomass burning impacts were observed in the sun photometer data during April 2016, providing additional evidence on the impact of transboundary pollution from Venezuela.

This chapter is divided into 3 sections. First section presents the meteorological conditions and fire activity during the periods of the measurement campaigns. Second section presents the results from sun photometer measurements and third section presents the results of measurement campaign 1 and 2.

3.1 Fire activity and meteorological conditions during measurement campaigns

Active fires from the MCD14 product were used as a proxy of fire activity. This MODIS product combines fire detections from Terra and Aqua platforms. Although MODIS overpass times and data gaps over the equator underrepresent fire activity, it has been shown that the use of 3-day centered mean of active fires better represents fire activity in the tropics (better correlation with geostationary fire detections) (Mu et al., 2011). First panel of Figure 3-1 presents the 3-day moving average of MCD14 fires within the Orinoco River Basin for the period spanning across 3 measurement campaigns. Only fires with confidence greater than 70 were considered.

Measurement campaigns were conducted during dry seasons 2014-2015 and 2015-2016. 2015-2016 dry season had higher fire activity than 2014-2015 dry season, which was closely related to meteorological conditions. Figure 3-1 shows rain (middle panel) and wind speed (bottom panel) in Puerto Gaitan, Meta, during the two dry seasons. During 2015-2016 dry season there was almost no rain between middle December and middle March, which corresponds to the typical dry season period in Colombian Llanos. During 2014-2015 dry season there were many rain events and the longest period without precipitation was of two weeks. The differences in meteorological conditions between the two years were also reflected in the duration of the biomass burning period. During 2014-2015 dry season, the 74 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin late start of the dry season, resulted in a biomass burning period that lasted until middle May in Venezuela, while in 2016 little fire activity was observed after late April.

Figure 3-1: Active fires (3-day centered mean of MCD14ML fire detections; fires in Venezuelan Llanos are shown in gray and fires in Colombian Llanos are shown in orange), precipitation and wind speed during measurement campaigns are shown in middle and bottom panel. (Source: this thesis)

Measurement campaigns duration and sites are presented in Figure 3-1. Measurement campaigns ECALB1 and ECALB2 were divided into two parts. The first part of the campaign Chapter 3. Evidences of the impact of fire emissions on air quality levels 75

(ECALB1a and ECALB2a was conducted at the beginning of the dry season when there was little fire activity. The second part was done during the middle of the dry season when higher fire activity was expected (ECALB1b and ECALB2b).

Measurement campaign 1, ECALB1, was conducted during dry season 2014-2015. However, as mentioned before that dry season started later than expected and although the measurement campaign observed a week with considerable fire activity, higher fire counts were observed after middle February when ECALB1 was over. Therefore, ECALB1b will be referred as a “moderate” biomass burning period.

Because of the potential underrepresentation of observed concentrations due to low fire activity it was decided to conduct a second measurement campaign, ECALB2, during the dry season 2015-2016, but only focused in Libertad and Taluma sites (ECALB2 will be referred as an “intense” biomass burning period).

Table 3-1: Measurement campaigns

Measurement campaign Measurement period Measurement sites

Measurement campaign 1, 2014-2015 dry season Arauca, Libertad and ECALB1 Taluma ECALB1a 2014-12-10 - 2014-12-21

ECALB1b 2015-01-17 - 2015-02-16, Taluma site started on 2015- 01-31 Measurement campaign 2, 2015-2016 dry season Libertad and Taluma ECALB2 ECALB2a 2015-12-14 – 2015-12-21

ECALB2b 2016-01-20 – 2016-02-12

Measurement campaign 3, 2015-04-01 – 2015-05-31 Arauca and Yopal Corporinoquia Sun photometer measurement 2015-03-23 – 2015-03-24 UNC-Gaitan AERONET 2015-05-27 – 2015-11-09 site 2016-01-08 – 2016-01-18 2016-03-10 – 2016-07-21

Corporinoquia measurement campaign was done from April to May 2015. This period was characterized by high fire activity in Venezuela and almost no fire activity in Colombian Llanos, which permitted observation of the effects of fire emissions from Venezuela to Colombia. 76 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

AERONET measurements mainly corresponded to periods without biomass burning. The periods where biomass burning was observed were measurements from March 23-24, 2015, January 08-18, 2016, and March 15 – April 30, 2016. This latter period corresponded to high fire activity in Venezuela and low in Colombia.

3.2 AERONET measurements

3.2.1 AERONET data description

AERONET (Aerosol Robotic Network) is a global ground-based network of Sun/sky radiometers initiated by NASA and expanded by international collaboration (Holben et al., 1998). As part of this project, a new AERONET site was established in Colombian Llanos (site UNC-Gaitan) with the purpose of observing the impacts of biomass burning in this region.

Measurements are made by a CIMEL CE-318 sun photometer and its data are processed by AERONET. The basic principle of sun photometry is that as the solar radiation transmits through atmosphere, it is attenuated by absorption or scattering due to air molecules or solid particles suspended in the atmosphere (Dayou et al., 2014). The voltage measured by the sun photometer is proportional to the spectral irradiance reaching the instrument at the surface and the spectral irradiance at the top of the atmosphere is estimated based on sun photometer measurements at Mauna Loa Observatory (Holben et al., 1998; https://aeronet.gsfc.nasa.gov/new_web/Documents/Aerosol_Optical_Depth.pdf). The total optical depth is obtained from the Beer-Lambert-Bouguer law:

2 푉(휆) = 푉표(휆)푑 푒푥푝[−휏(휆)푇푂푇 ∗ 푚] (3-1)

Where 푉(휆) is the voltage measured at wavelength 휆, 푉표 Is the extraterrestrial voltage obtained by sun photometer measurements at Mauna Loa Observatory, d is the ratio of the average to the actual Earth-Sun distance, 휏 is the total optical depth and m is the optical air mass. The total optical depth is the sum of all atmospheric constituents that scatter light, thus, the Aerosol Optical Depth (AOD) is obtained by subtracting the optical depth due to water vapor, Rayleigh scattering and trace gases from the total optical depth. Chapter 3. Evidences of the impact of fire emissions on air quality levels 77

The photometer makes direct Sun measurements with a 1.2° full field of view every 15 min at 340, 380, 440, 675, 870, 940 and 1020 nm. These measurements are used to compute AOD at each wavelength except for the 940 nm channel that is used to retrieve precipitable water) (Holben, B.N., et al., 2001). Besides direct sun measurements the instrument performs sky measurements from which additional aerosol properties are retrieved. The basic sky observations sequences are the almucantar and principal plane that provide observations through a large range of scattering angles from the Sun through a constant aerosol profile (Holben et al., 1998) that are used by the AERONET inversion algorithm (Dubovik & King, 2000) to obtain aerosol parameters (e.g. volume size distribution) and properties (e.g. single scattering albedo) (Holben et al., 1998).

AERONET retrieved aerosol parameters and properties can be used as indicators of its composition. Table 3-2 presents the main AERONET aerosol products and how they can be used to infer aerosol type.

Table 3-2 Main AERONET products

AERONET product Product Product use for aerosol type identification algorithm Aerosol Optical Depth Direct sun High AOD values are characteristics of (AOD) algorithm atmospheres affected by biomass burning, dust Measure of aerosols plumes or urban pollution (Kambezidis & distributed within a column Kaskaoutis, 2008). of air from the instrument to the top of the atmosphere Angstrom Exponent Direct sun The value of AE increases as the particle size (AE) algorithm increases. Fine particles have AE from 1-3, while Negative slope of AOD coarse mode particles have AE near zero (Ali et with wavelength in al., 2014). logarithmic scale

78 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 3-2 (Continuation)

AERONET product Product Product use for aerosol type identification algorithm Single Scattering Inversion Dust particles exhibit strong absorption in the blue Albedo (SSA) Algorithm for wavelength region and lower absorption in the Ratio of the scattering retrieval of visible and near infrared, as a result SSA increases coefficient to the aerosol optical with wavelength for desert dust aerosols. extinction coefficient properties from Hygroscopic aerosols have near neutral SSA sun and sky spectral dependency. BC have the strongest radiance absorption in the near infrared and lower measurements. absorption in the blue wavelength region, thus, (Almucantar or SSA decreases with wavelength (Giles et al., principal plane 2012) scenarios) Absorption Aerosol Inversion Measure of the column aerosol loading of light Optical Depth Algorithm absorbing particles (Black carbon, carbonaceous AAOD=(1-SSA)*AOD (Almucantar or aerosols or mineral dust). AAOD becomes (Shin et al., 2018) principal plane ambiguous if several types of absorbing aerosol scenarios) are present in a mixed aerosol plume (Shin et al., 2018) Absorption Angstrom Inversion EAE: Indicator of the predominant aerosol size Exponent (AAE) and Algorithm because the spectral shape of the extinction is Extinction Angstrom (Almucantar or related to the particle size (Li et al., 2016) Exponent (EAE) principal plane AAE: Coarse mode particles have a smaller AAE Wavelength scenarios) than fine particles for a given aerosol mixture of dependence of aerosol species (Schuster et al., 2015). extinction and absorption. Fine and Coarse Mode Spectral De- Fraction of fine aerosols in the atmospheric AOD Convolution column Fine (sub-micron) and Algorithm coarse (super-micron) Retrievals aerosol optical depth at a (O’Neill, 2003) standard wavelength of (Based on solar 500 nm extinction data) Chapter 3. Evidences of the impact of fire emissions on air quality levels 79

Table 3-2 (Continuation)

Fine Mode Fraction (FMF) Fraction of fine mode to total aerosol optical depth Size distribution Inversion Algorithm Size Distribution of atmospheric column Vertically integrated (Almucantar or aerosols logarithmic volume principal plane distribution scenarios)

Aerosol type identification must use a combination of parameters rather than a single property value because mixed plumes can lead to ambiguous values of some aerosol properties. For example, (Schuster et al., 2016) showed that dust and carbonaceous aerosols cannot be separated with confidence using AAE when soot carbon particles that are internally mixed with other aerosol species because they do not necessarily maintain the theoretical value of AAE =1. They analyzed data from a region with a mix of dust and carbonaceous aerosols and found that cases with AAE< 1 are more likely to be dominated by dust than by carbonaceous aerosols.

Table 3-3 summarizes some aerosol classification approaches that use different AERONET products.

80 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 3-3: Aerosol classification approaches using AERONET products Reference Aerosol Aerosol classification method properties (Lee et al., FMF and Classification algorithm that first separates the dominant mode based 2010) SSA on FMF values (coarse FMF < 0.4 and fine FMF >0.6). Then, SSA is used to differentiate between absorbing or non-absorbing aerosols. SSA > 0.95 are considered non-absorbing aerosols.

Source: Lee et al., (2010) (Russell et AAE and Cluster analysis of AAE and EAE. al., 2010) EAE

Source: Russell et al., (2010)

Chapter 3. Evidences of the impact of fire emissions on air quality levels 81

Table 3-3 (Continuation)

(Giles et SSA and Clusters of aerosol types are separated based on the Relationship al., 2012) EAE between SSA and Extinction Angstrom Exponent.

Source: Giles et al., (2012)

3.2.2 UNC-Gaitan site

The AERONET UNC-Gaitan site was establish at the Yaaliakeisy school located at Puerto Gaitan, Meta, in the Llanos high plains (Figure 3-2). Three different devices were installed during the operation at UNC-Gaitan site. The photometers IDs and measurement periods are shown at Table 3-4. Monthly maintenance visits were conducted during the measurement periods and photometers were sent to calibration to NASA, as required by the AERONET network. Data used in this thesis corresponds to AERONET level 2.0 data.

Figure 3-2: AERONET UNC-Gaitan site. (Source: this thesis)

82 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 3-4: Measurement periods

Measurement period Photometer ID 2015-03-23 – 2015-03-24 305 2015-05-27 – 2015-07-13 305 2015-07-13 - 2015-11-09 9 2016-01-08 – 2016-01-18 9 2016-03-10 – 2016-07-21 977

Figure 3-3: Sun photometer installed at UNC-Gaitan. (Source: this thesis)

Sun photometer

Chapter 3. Evidences of the impact of fire emissions on air quality levels 83

3.2.3 Results

Figure 3-4 shows the time series of AOD (500 nm), Angstrom Exponent and fine mode fraction as derived from direct sun measurements, and number of fires in the Llanos Region. The latter are shown as the daily MODIS MCD14 fire counts (in bars) and as the 3-day moving average of fire detections (lines). As mentioned before, due to logistic reasons it was not possible to continuously measure during the dry season. However, there were three periods that captured part of the biomass burning period. First period was during March 21- 23, 2015, second was January 2-20, 2016, and third from March 13 to April 21, 2016.

Figure 3-4: Number of fires in the Llanos Region, AOD (500 nm), Angstrom Exponent and fine mode fraction. Active fires are presented as MODIS MCD14 fire counts (in bars) and as the 3-day moving average of fire detections (lines) in black for Venezuelan llanos and in orange for Colombian Llanos. (Source: this thesis using data from AERONET, UNC-Gaitan site)

84 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

The highest AOD values (0-5-1) were observed during March-April, 2016, which were caused by fine particles as confirmed by the high fine mode fractions, most of which were above 0.75 and AE values (around 1.5). Data from March 21-23, 2015 also had high AOD (~0.5) and high fine mode fraction. Measurements during the beginning of the dry season (January 2016 period) had lower AOD values and no fine mode fraction data are available.

The high AOD values observed during March (late dry season) highly contrast with the low AOD values of the period May – July, 2016, most of which were below 0.15. Only three days of that period had AOD higher than 0.15, May 12 (AOD =0.25) and June 4-5 (AOD=0.29), and the latter was caused by coarse particles. The other potential source of fine particles in the area is the oil extraction, but this is a year-round activity that, unlike biomass burning, is relatively constant in time. Thus, high fine mode AOD observed during the dry season is caused by biomass burning activity in the region.

During August-October, 2015, some days have AOD values higher that the observed during the rest of non-biomass burning period and were caused by fine mode particles. Although during that period there is almost no fire activity in the Llanos, there is biomass burning in the Amazon and Cerrado regions, and under certain synoptic conditions smoke can be transported to the Llanos. For example, as shown in Figure 3-5, on September 16 back trajectories from the Llanos region showed air mass coming from the southeast (black dots in Figure 3-5 panels A and B). Regionally high Carbon Monoxide (Figure 3-5 B) and AOD (Figure 3-5 B) were observed at the end-point of the simulated 72h-backward trajectories, which is related to biomass burning in Brazilian Amazon and Cerrado, as shown by active fire detections (purple dots in Figure 3-5 panels A and B).

Chapter 3. Evidences of the impact of fire emissions on air quality levels 85

Figure 3-5: Biomass burning emissions transport from Brazil to Colombia on September 16, 2015. (a) Carbon monoxide from Aqua/AIRS (b) Aerosol Optical Depth. (Source: NASA Worldview https://worldview.earthdata.nasa.gov/)

Aerosol properties that are based on almucantar and principal plane inversion are much more limited than direct sun measurements. This is due to their lower measurement frequency, higher impact of cloud conditions and also because the absorption properties are only reliably retrieved for AOD higher than 0.4, which biases the data set only to high aerosol loading periods (Dubovik et al., 2002; Giles et al., 2012).

Figure 3-6 shows the almucantar retrieved aerosol size distribution, dV(r)/dln(r), which is the vertically integrated logarithmic volume distribution. Individual size distributions were grouped into clusters named G1, G2, G3 and G4. G2 encloses most of the observations from non-biomass burning periods, characterized by low AOD values (mean AOD=0.1) and a size distribution with two small peaks at 0.1 and 1-2 µm. G4 corresponds to a specific event (June 04-05, 2015) of intermediate AOD value (0.3) caused by coarse particles. G1 and G3 correspond to biomass burning periods and are characterized by high AOD values (G1 mean AOD= 0.66 and G3 mean AOD=0.56) and a bimodal size distribution with the highest peak at 0.08-0.25 µm, and a second mode at 5 µm. G1 also included October 10, 2015, a day with an intermediate AOD (0.3) caused by fine particles (FMF=0.92). 86 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-6: (a) Clusters of Aerosol size distributions, dV(r)/dln(r). (b) Coincident Aerosol Optical Depth for the aerosol size clusters (c) Dates of the aerosol size clusters. (Source: this thesis using data from AERONET – UNC-Gaitan site) (a) (c)

(b)

Single scattering albedo is only available for a subset of the days that have size distribution information (AOD>0.4), all of them from groups G3 and G4, which corresponds to periods of biomass burning activity (2015-03-23 – 2015-03-24, 2016-03-23-2016-03-29 and 2016- 04-15). Figure 3-7 shows the spectral behavior of SSA. SSA decreases with wavelength, which is typical of biomass burning or industrial sources since black carbon (BC) particles have the strongest absorption in the near-infrared (Giles et al., 2012).

Chapter 3. Evidences of the impact of fire emissions on air quality levels 87

Figure 3-7 Single scattering albedo spectra. (Source: this thesis using data from AERONET – UNC-Gaitan site)

According to the aerosol classification clusters based on SSA and EAE values (Giles et al., 2012), and AAE and EAE values (Russell et al., 2010), the values at UNC-Gaitan correspond to an area of biomass burning and industrial aerosols (Figure 3-8). Apart from oil extraction activities, which is a year-round activity whose emissions are 1/10 of biomass burning emissions during the dry season, there are no other industrial sources in the region. Considering that, and the temporal dynamics of AOD that exhibits a striking correlation with fire activity, it can be concluded that the high AOD periods are due to biomass burning rather than industrial emissions. Figure 3-8: Aerosol classification for UNC-Gaitan based on (a) clusters based on SSA and EAE values as proposed by Giles et al., (2012) and (b) clusters based on AAE and EAE values as proposed by Russell et al., (2010)

1 3 2.5 0.95

2 870 870 nm 0.9 - 1.5 1 0.85 AAE440 0.5 0.8 0 0 0.5 1 1.5 2 2.5 Single Scattering Albedo 440 440 nm Single Albedo Scattering 0 0.5 1 1.5 2 2.5 EAE 440 - 870 nm EAE 440-870 UNC-Gaitan UNC-Gaitan 88 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

3.3 Measurement campaigns ECALB and ECALB2

3.3.1 Methods

▪ Sampling and chemical characterization

Measurement sites included Unal-Arauca, (7.013360 N, 70.743827 W), located near to Colombia’s boundary with Venezuela, Libertad (4.058335 N, 73.467467 W), located in the foothills at the southwest part of the region, and Taluma (4.375250 N, 72.231306 W), located in the high plains (Figure 3-9). Unal-Arauca and Libertad are located outskirts of Arauca and Villavicencio cities, respectively. Taluma site corresponds to a rural location in an experimental research center of CORPOICA in Puerto López, Meta.

Figure 3-9: Measurement sites campaigns ECALB1 and ECALB2. (Source: this thesis)

Those measurement sites were selected because they enable measurements the regional background concentrations and the regional enhancements caused by biomass burning events. There are no urban sources affecting concentrations in those sites. Other potential pollution sources in the region include emissions from agricultural activities and oil production. Colombian Llanos monthly-equivalent PM10 emissions on 2015 from oil production are ~2,750 ton/month (Díaz Garzón et al., 2017) and from agriculture ~550 ton/month (Jimenez et al, 2018; Tatis Bautista et al., 2018). As shown in Chapter 2, biomass Chapter 3. Evidences of the impact of fire emissions on air quality levels 89 burning emissions that occur during the dry season are much higher (~39.000 ton/month for the period January 17 -February 18, 2015).

ECALB2 was only done in Villavicencio and Taluma, sites where higher enhancements were observed during the first campaign, ECALB1. Measurement campaigns were divided into two parts. The first part of the campaign was done at the beginning of the dry season when there was little fire activity (from December 5-18, 2014 for ECALB1 and December 14 -19, 2015 for ECALB2). The second part was done during the middle of the dry season when higher fire activity was expected (from January 17 – February 15, 2015 for ECALB1 and January 17-February 16, 2016 for ECALB2).

For Arauca and Libertad sites, PM10 samples were collected on 37 mm quartz and teflon filters using low volume samplers (Harvard impactors). Filters were conditioned for 24 hours and then were weighted in a microbalance (Mettler Toledo XP26) prior to and after sampling. In Taluma different equipment were used in each campaign. For ECALB1 one Harvard impactor (using Teflon filters) and a Beta attenuation sampler (MP101M, Environnement SA) were used. The latter was only available during the second part of ECALB1. For ECALB2 2 Harvard impactors (quartz and Teflon filters) were used.

Meteorological information was taken from a station located 5 km away from Taluma (Fazenda site operated by the Air Quality Research Group of Universidad Nacional de Colombia). Although there are other meteorological sites from the Colombian Meteorological Network, after conducting quality control of the data it was decided to use information of Fazenda as representative of the flat high plains area.

90 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-10: Measurement sites (A) Taluma and (B) Libertad. (Source: this thesis)

(A) (B)

Selected samples were analyzed for ions by ion chromatography, elements by XRF, and black carbon by an OP-21 Transmissometer. Analysis were conducted at the CARES Lab of Clarkson University. Special focus was made on samples from ECALB2 since during that campaign there was higher fire activity and higher PM10 concentrations than during ECALB1. Analyzed samples included 16 samples from Taluma (ECALB2), 4 samples of low concentration from ECALB2a and the rest from ECALB2b. Some samples of Libertad were also analyzed (10 from ECALB1 and 6 from ECALB2b). Unfortunately, carbon analysis (EC- OC) could not be conducted since the Lab lost our quartz filters.

Table 3-5: Chemical analysis performed on the samples

- - 2- - 3- Ion Cromatography F , Cl , SO4 , NO3 , PO4 + + + 2+ Na , NH4 , K , Mg X-ray fluorescence, XRF Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sn, Sb, Te, I, Cs, Ba, La, Ce, Sm, W, Au, Hg, Pb, Bi OT21 Transmissometer BC

▪ Exploratory analysis of chemical composition data

Before applying source apportionment techniques, chemical composition data was analyzed to observe correlation between elements that might indicate source categories, how they change from sample to sample and from site to site. This was achieved by correlation Chapter 3. Evidences of the impact of fire emissions on air quality levels 91 matrices, scatter plots, linear regression between variables, daily time series of the main components and comparison of species concentration between sites.

▪ Mass reconstruction

The fact of not having quartz filters analyzed limited mass reconstruction. Also, during the data validation process, it was observed that for some elements analyzed by XRF, the concentrations measured in the reference material (SRM 2783) were higher than the certificate values. This was fully discussed with the lab that made the analysis and there were two reasons that caused the elevated values. First, the lab reported that the tube of the instrument failed shortly after running the samples, which could have affected the performance of the equipment. The lab reprocessed the values using Micromatter standards calibration data and recommended correcting the data using the ratio of certified to measured value for each element reported in the reference material certificate. Second, there was still high values for some elements because the filter holder in the instrument was producing high transition metal blank values. These analytical limitations reduced the number of elements available for analysis and the mass reconstruction.

Reconstructed mass was calculated as the sum of inorganic ions, black carbon and geological minerals.

−2 − Inorganic ion mass was calculated assuming that sulfates (푆푂4 ) and nitrates (푁푂3 ) are neutralized to ammonium sulfate ((푁퐻4)2푆푂4) and ammonium nitrate 푁퐻4푁푂3, with the ammonium fraction accounted for by their stoichiometric multipliers (Chow et al., 2015).

−2 − 퐼푛표푟푔푎푛𝑖푐 𝑖표푛푠 = 1.375푆푂4 + 1.29푁푂3 (3-2)

Geological mineral mass was calculated using the IMPROVE formula (Chow et al., 2015):

퐺푒표푙표푔𝑖푐푎푙 푚𝑖푛푒푟푎푙푠 = 2.2Al + 2.49Si + 1.63Ca + 1.94Ti + 2.42Fe (3-3)

92 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

▪ Source apportionment

There are two main approaches for receptor modelling, chemical mass balance (CMB) and multivariate techniques (including Absolute Principal Component Scores (APCS), UNMIX and Positive Matrix Factorization, (PMF)). The principle of both approaches is that mass conservation between the source and the receptor can be assumed, and a mass balance can be used to identify and apportion sources (Belis et al., 2014). Besides receptor profiles, CMB requires to know the sources affecting the receptor and their profile to solve the mass balance on a sample by sample basis (Hopke, 2016). Multivariate methods derive the number and nature of sources from ambient data, but require a high number of samples (Watson et al., 2008).

EPA recommended multivariate models include PMF and UNMIX. Both have constraints to ensure the non-negativity source composition and contributions. UNMIX requires finding edge points, which means that for each source there are some samples with little or no contribution of that source (Norris et al., 2007). PMF is considered to achieve better results since it processes each point separately and adjusts the data point influence depending on its uncertainty (Jain et al., 2017).

The lack of some tracers (due to the analytical problems mentioned above) and local source profiles limits the application of CMB model. Also, the number of observations required by UNMIX and PMF limits its application for this study. For example, PMF requires at least 100 samples. Because of the limited number of samples, it was decided to use Absolute Principal Component Scores (APCS), which is considered an exploratory source apportionment technique.

The APCS is a technique based on principal component analysis (PCA), that scores an extra day of “zero” pollution in order to derive absolute scores on which PM concentrations can be regressed to apportion sources (Thurston & Spengler, 1985). Since the main interest in this study is to analyze the influence of biomass burning, the “zero” pollution day was selected as the average of the days of the lowest concentrations measured at the beginning of the dry season. This approach discounts the effect of other sources (e.g. natural sources) Chapter 3. Evidences of the impact of fire emissions on air quality levels 93 that are always impacting concentrations in this area and allows to find the sources that contribute to the enhancements of particulate matter found during the dry season.

Principal component analysis reduces the dimensionality of a set of data by replacing a large set of intercorrelated data by a small number of independent variables, named components. Sometimes, those components are not easily interpretable, but their rotation simplifies them and results in more representative and interpretable factors (Thurston & Spengler, 1985). Those factors are interpreted as sources based on comparison of elements having the highest loadings (correlation between the element and the component) with elements emitted in large amounts by known sources (relative to other elements or sources) (Thurston 1994).

A pre-selection of variables was made including the main tracers from source categories identified by correlation analysis. Data from Libertad and Taluma were analyzed as a single site in order to increase the size of the data set. This approach has proven to be valid under the assumption that there is little variance in the composition of sources impacting the sites and the main variability is caused by the contribution of each source (Mooibroek et al., 2016)

APCS procedure as described by Thurston & Spengler, (1985) was applied. First, variables were standardized as:

퐶푖,푘 − 퐶̅푖 푍푖,푘 = (3-4) 𝜎푖

Where 푍푖,푘(termed Z-score) is the standardized value of the element i for the sample k; 퐶푖,푘 is the concentration of element i for the sample k; and 퐶̅푖 and 𝜎푖 are the arithmetic mean concentration and the standard deviation for element i over all samples, respectively.

Then PCA was applied over the standardized variables. The number of factors was determined based on the eigenvalues; only principal components with eigenvalues greater than 1 were retained as factors. Factors were rotated using Varimax method, which makes the large loadings larger and the small loadings smaller within each component resulting in more interpretable factors.

Rotated principal component scores are computed as: 94 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

∗ ∗ [푃]푝 × 푚 = [퐵]푝 × 푛 [ 푍]푛 ×푚 (3-5)

Where B* is the rotated PC scoring matrix, and Z is the matrix of normalized concentration.

As PCA uses normalized values, its factor scores are also normalized, with mean zero and standard deviation of 1. In order to derive absolute scores, the Z-score for the artificial sample is calculated as:

퐶푖,푚푖푛 − 퐶̅푖 (푍0)푖 = (3-6) 𝜎푖

The scoring coefficients from the principal component analysis are used to score the artificial sample and the APCS for each element was estimated by subtracting the factor scores for the artificial sample from the factor scores of each true sample. Then, the PM10 enhancement mass was regressed onto the APCS 푁

∆푃푀10푘 = 훽0 + ∑ 훽푝 퐴푃퐶푆푝푘 (3-7) 푝=1

Where 훽0 is the constant term of multiple regression; 훽푝 is the coefficient of multiple regression of the source p; APCSp is the rotated absolute component score for component j on observation k; and 훽푝 퐴푃퐶푆푝푘 is the particle mass contribution on observation k made by the source p. The mean of 훽푝 퐴푃퐶푆푝푘 on all observations represents the average contribution of the sources.

3.3.2 Results

▪ PM10 concentrations, fire activity and meteorological conditions

Figure 3-11 shows PM10 concentrations during December 2014, ECALB1a and December 2015, ECALB2a. Weighted average concentrations during ECALB1a were 13.1±1.4 µg/m3, 11.2 ±3.2 µg/m3 and 17.3±1.1 µg/m3 for Libertad (LAL), Arauca (ARA) and Taluma (TAL) respectively. PM10 concentration at Taluma during ECALB2a was 18.1±2.9 µg/m3.

Chapter 3. Evidences of the impact of fire emissions on air quality levels 95

Figure 3-11: PM10 concentration during beginning of the dry season (ECALB1a and ECALB2a). (Source: this thesis).

During middle dry season, higher concentrations were found depending on meteorological conditions and fire activity. Figure 3-12 shows 6-hourly PM10 concentrations at Taluma, half-hourly relative humidity, half-hourly rain and 3-day centered mean of active fire detections in the Colombian and Venezuelan Orinoco River Basin during ECALB1b. Three different meteorological periods were observed in Taluma during ECALB1b. Period 1 from January 31 to February 4 (P1), and period 3 from February 11 to February 14 (P3), had some rain events and high relative humidity (55-98%). From February 5th to February 10th (Period 2- P2) dryer conditions were observed with no rain, low relative humidity (36-85%) and high wind speeds (not shown). Fire activity was higher during this dry period and little fire activity in Colombia ORIB was detected during periods P1 and P3.

96 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-12: PM10 concentrations at Taluma, relative humidity, precipitation and 3-day centered mean of active fires during ECALB1 (fires in Venezuelan Llanos are shown in gray and fires in Colombian Llanos are shown in orange) (Source: this thesis; active fires from the MCD14 product)

PM10 concentrations at Taluma were highly related to meteorological periods. P1 and P3 had average concentrations of 15.1 and 20.4 µg/m3, respectively. Fire activity significantly decreased on February 12, but PM10 concentrations only decreased until the night of that day. P2 concentrations were higher and their average was 31.6 µg/m³. PM10 levels were high during both day and night. According to GOES fire product, fire activity in Colombian Orinoco was higher between 14:30 and 17:30 LT and these emissions occurring at the end of the day can affect air quality at night when mixing height is lower as shown by the 3-hourly concentration profile shown in Figure 3-13.

Chapter 3. Evidences of the impact of fire emissions on air quality levels 97

Figure 3-13: 3-Hourly concentrations at Taluma. (Source: this thesis).

Figure 3-14 shows PM10 concentrations at Arauca, Libertad and Taluma. Libertad and Taluma presented similar behavior, which shows that regional sources are controlling particulate matter levels. PM10 increased up to 41 μg/m³ at Libertad and 36 μg/m³ at Taluma (6-hour average) during a period with no rain, high wind speed and low relative humidity. Arauca had lower PM10 concentrations. Due to its location, when wind direction is north east, this site was not affected by emissions occurring in Colombian Orinoco region.

98 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-14 PM10 concentrations during ECALB1b. (Source: this thesis).

Figure 3-15 PM10 concentrations at Taluma and Libertad, half-hourly relative humidity, half- hourly rain and 3-day centered mean of active fire detections in the Colombian and Venezuelan Orinoco River Basin during ECALB2b. Fire activity was considerable higher during ECALB2b than during ECALB1b and there was almost no rain during ECALB2, except for two light rain events on February 7 and February 11. Average concentration was 43.6 μg/m³ at Taluma and 34.2 μg/m³ at Libertad, which corresponded to enhancements of 26.3 μg/m³ and 21.1 μg/m³ respect to the concentrations measured during ECALB1a, with little fire activity. Maximum PM10 concentrations were 68.8 μg/m³ at Taluma and 42.7 μg/m³ at Libertad. Chapter 3. Evidences of the impact of fire emissions on air quality levels 99

Figure 3-15 Active fires, precipitation, relative humidity and PM10 concentrations during ECALB2b

▪ Chemical composition

Correlation matrices for Taluma ECALB2 and Libertad ECALB1 showed two groups of inter- correlated species that suggest the presence of equal number of sources. One group + -2 includes PM10, K , BC and SO4 and might be related to biomass burning (Reid & Koppmann, 2005). BC is produced in incomplete combustion process, soluble potassium (K+) is a major electrolyte within plant cytoplasm (Andreae et al., 1998) and is the most 100 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin abundant trace element in savanna BB aerosols (Cachier et al., 1995) while sulfates are produced through heterogeneous reactions on smoke particles (Buzcu et al., 2006).

Second group includes Si, Al, and Fe all of which are considered tracers of soil dust and/or crustal re-suspension (Pant & Harrison, 2012). Figure 3-16 shows the correlation coefficients of BB and soil dust tracers with PM10 for Taluma ECALB2 and Libertad ECALB1. Soil dust tracers had good correlation with PM10 concentrations at Libertad ECALB1. For Taluma ECALB2, dust tracers had good correlation among them suggesting they came from a single source but their correlation coefficient with Taluma PM10 was low. Observation of daily values of those elements and PM10 concentration provided explanation for the apparent bad correlation.

Libertad ECALB2 did not show strong correlations with any element, however, it should be noted that it only had 6 samples that had variable contribution of pollution sources as observed from daily element concentrations. These six samples were excluded from ACPS analysis.

Figure 3-16: Correlation matrix for (a) Taluma ECALB2 and (b) Libertad ECALB1 (Source: this thesis)

(a) (b)

Figure 3-17 shows concentrations of PM10 and a biomass burning tracer (soluble potassium (Figure 3-17a)), and PM10 and a soil tracer (silicon (Figure 3-17b)) for all analyzed samples. February 3 and February 5 samples had high values of PM10, high Chapter 3. Evidences of the impact of fire emissions on air quality levels 101 concentrations of soluble potassium but low silicon values, from which it can be inferred that these samples had low influence of soil dust and high influence of biomass burning emissions.

Figure 3-17: PM10 and concentrations of soluble potassium (a) and silicon (b). (Source: this thesis)

(a) (b)

Figure 3-18 shows scatter plots of soluble potassium vs. PM10 (Figure 3-18a) and Silicon vs. PM10 (Figure 3-18b) for all samples (measurement campaigns are shown in color and shape of points). For Silicon, it is evident that the low correlation coefficient found in Taluma ECALB2 between soil dust tracers and PM10 was caused by the two samples of February 3 and February 5. R-square for all samples was 0.38 (gray dotted line on Figure 3-18b), but when those 2 samples are not considered, r-square increases to 0.79 (black line on Figure 3-18b) showing that for most of the samples silicon do have good correlation with PM10. Libertad ECALB1 and Taluma ECALB2 PM10 concentration had a strong relation with soluble potassium as was also evidenced in the high correlation coefficient (r2 =0.68).

102 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-18: Scatter plots and linear regression of (a) soluble potassium vs. PM10 and (b) silicon vs. PM10. (Source: this thesis)

(a) (b)

Figure 3-20 compares the magnitude of PM10 components between sites, measurement campaigns and with levels measured in Bogota with the aim of comparing the enhancements observed during the dry season with levels typically observed in a highly populated and polluted urban areas. Table 3-6 describes the sites, period and potential emission sources of each group presented in Figure 3-20.Taluma ECLAB2a (beginning of the dry season 2015-2016) was divided in two groups since the observation of daily values showed that two samples had higher influence of soil dust than the other two samples.

During high biomass burning periods Taluma might be as polluted as urban areas of Bogota. PM10 average concentration at Taluma during ECALB2 was 38 µg/m3, which is very close than PM10 levels observed at Suba (41.4 µg/m3)(Vargas et al., 2012) and a background site at the geographic center of the city (37.5 µg/m3 whole year, 41.4 µg/m3 rainy season, 34.6 µg/m3 dry season) (Ramírez et al., 2018).

Chapter 3. Evidences of the impact of fire emissions on air quality levels 103

Figure 3-19: Location of Bogota sites used for comparison with Taluma and Libertad measurements (Source: this thesis, coordinates of Bogota sites from (Ramírez et al., 2018) y (Vargas et al., 2012)

BC concentrations registered at Taluma and Libertad during the dry season are similar to EC concentrations at Bogota where there are multiple urban sources of this pollutant. For example, BC average concentration at Taluma during ECALB2 was 3.1 µg/m3 Taluma, which is very close to EC levels reported by (Ramírez et al., 2018) for the urban background site (3.1 µg/m3 dry season, 3.4 µg/m3 rainy season). Soluble potassium, considered a biomass burning tracer, had much higher concentrations at Taluma (0.47 µg/m3) and Libertad (0.37 µg/m3) than at Bogota (0.15 µg/m3). Those concentrations of K+ agree well with the ones measured at Calabozo (Venezuelan Llanos) where average concentration of K+ (in TSP; total suspended particulate matter) was 0.42 ±0.16 µg/m3 during dry-burning season and 0.18 ±0.06 µg/m3 during rainy-non burning season (Sanhueza, 1988).

Soil dust tracers also had higher levels than Bogota. Taluma had higher influence of soil dust than Libertad; even some concentrations measured on December 2015 at Taluma (ECALB2a1) had higher concentration of soil dust tracers than Libertad during ECALB1.

104 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 3-6 Description of sites, period and potential emission sources of each group presented in Figure 3-20

Group Site Period Emission sources Libertad_ECALB1a1 Libertad December 12-18, Low impact of fire 2014 and soil dust emissions Taluma_ECALB2a1 Taluma December 15-19, Low impact of fire 2015 and soil dust emissions Taluma_ECALB2a Taluma December 14-15, Low impact of fire December 19-21, emissions. Some 2015 impact of soil dust emissions. Libertad_ECALB1 Libertad January 20 – Moderate biomass February 16, 2015 burning period and influence of soil dust emissions. Libertad_ECALB2 Libertad January 25- Intense biomass February 13, 2016 burning period and influence of soil dust emissions. Taluma_ECALB2 Taluma Intense biomass Intense biomass burning period and burning period and influence of soil dust influence of soil dust emissions. emissions. Bog_DS Bogota, Urban background Urban background Universidad site. site. Libre, (Ramírez et al., 2018) Bog_DS2 Bogota, Suba, Commercial activities Commercial activities urban site, and paved and and paved and (Vargas et al., unpaved roads unpaved roads 2012) Bog_RS Bogota, Urban background Urban background Universidad site. site. Libre, (Ramírez et al., 2018)

Chapter 3. Evidences of the impact of fire emissions on air quality levels 105

Figure 3-20 Average concentrations of PM10 and its main components and comparison with ambient air values at Bogota

▪ Mass reconstruction

Figure 3-21 shows mass reconstruction in terms of concentration (a) and percentage (b). During the dry season the average soil and black carbon contribution were 32% and 8%, respectively. Inorganic ions made up 13.7% of the mass, leaving a 46% of unknown compounds among which are the organic carbon and trace elements.

106 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-21 (a) Mass reconstruction (b) Mass reconstruction percentage

(a)

(b)

▪ Absolute Principal Component Scores

Dust is a component that makes up around 30% of total PM10. Part of this dust may be related to fire activity. Recent research has shown that fire can induce wind erosion by two mechanisms. First, by the fire-driven turbulence generated during the fires (Wagner et al., 2018) and second, by wind erosion of soils burned by fires (Wagenbrenner, 2017). Chapter 3. Evidences of the impact of fire emissions on air quality levels 107

Wind erosion occurs when wind speeds exceeds a threshold of around 6-7 m/s, values that are not typically registered in the area as shown in Figure 3-1. However, when a fire occurs the heated air in the near surface layer begins to raise and as a result, surrounding air flows to replace the rising air. The accelerated horizontal winds and atmospheric turbulence induced by the fire over a terrain whose vegetation have been partly removed may exceed the threshold for dust emission (Wagner et al., 2018).

Absolute Principal Component Scores (APCS) analysis applied to data of Taluma ECALB2 and Libertad ECALB1 resulted in two rotated factors that because of its loadings represent soil dust and biomass burning (Table 3-7). The average contribution of the two factors to the PM10 enhancements (as estimated from the regression of PM10 enhancements on APCS) observed during the dry season were 19.6% of soil factor and 80.6% of biomass burning factor. There was an overestimation in the Libertad data, part of which may be explained by the fact that the mean concentration assumed as the “zero air pollution day” was the average of Taluma and Libertad, with Taluma being higher than Libertad.

Table 3-7: Factors identified by the APCS analysis

C2 - biomass Component C1 - soil dust burning K_ion 0.34 0.89 Si 0.94 0.3 BC 0.15 0.96 Al 0.95 0.29 Ti 0.97 0.18

108 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 3-22: Contribution of each factor to the simulated contribution using APCS. The “zero pollution day” was assumed as the baseline concentration which corresponds to the average of the days of the lowest concentrations measured at the beginning of the dry season at Taluma and Libertad. (Source: this thesis)

3.4 Conclusions

Air quality in the Llanos region significantly deteriorates during the biomass burning period. PM10 concentrations with little fire activity range between 11-17 µg/m³. During the intense biomass burning period observed during the dry season 2015-2016 there were concentrations up to 68 µg/m³, and enhancements on average concentrations that range from 23 – 26 µg/m³.

PM10 enhancements were closely related enhancements of biomass burning tracers (BC, K+) and dust tracers. Exploratory source apportionment techniques demonstrated that around 80% of the observed PM10 enhancements were due to biomass burning emissions and 20% are caused by soil dust. Although wind speed threshold for wind erosion is rarely reached in the region, biomass burning might cause dust emissions through fire-induced turbulence.

Chapter 3. Evidences of the impact of fire emissions on air quality levels 109

The levels of PM10 and BC reached during the biomass burning period in the Llanos region were similar of even higher to the levels observed in an urban area like Bogota, where multiple industrial and mobile sources exist. The average PM10 concentration at Taluma during ECALB2 was 38 µg/m3, which is very close than PM10 levels observed at a background site at the geographic center of Bogota (37.5 µg/m3). Also, the average BC concentration at Taluma during ECALB2 was 3.1 µg/m3, which was very close to EC levels reported for that urban background site (3.24 µg/m3).

The high load of fine particles in the atmospheric column measured by the AERONET photometer during the biomass burning periods, although not coincident with ground level PM10 observations, demonstrated the effects of regional biomass burning on atmospheric pollution. During periods of high biomass burning in the Llanos AOD values were between 0.5-1, while values during a non-biomass burning period where mostly below 0.15, except for some days of the period August – October, 2015, when synoptic scale transport of biomass burning emission from Amazonia and Cerrado regions, might have caused some AOD values of around 0.25. The AOD values during the biomass burning period were much higher than AOD values observed in Bogota that had an average AOD (500 nm) of 0.161 during 2013, which demonstrates the relevance of biomass burning as a regional source of pollution.

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4. Transboundary pollution

While conducting this project, the Colombian national environmental authority (IDEAM) asked the Air Quality Research Group to analyze pollution measurements in Yopal and Arauca, 2 Orinoco River Basin cities (located ~244 km apart from each other). That measurement campaign was made by the local environmental authority (Corporinoquia), as a part of indicative regulatory measurements that environmental authorities must conduct every two years in cities with population between 50.000 and 150.000 inhabitants. The measurement campaign was conducted during April and May 2015, which coincided with the period of low fire activity in Colombia (end of Colombian dry season) and high fire activity in Venezuela.

The analysis of those measurements revealed air quality impacts of BB over Colombian Llanos region. Because of that, a detailed analysis of the data is included in this thesis and was published in the manuscript “Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Basin”, which is reproduced below.

TRANSBOUNDARY TRANSPORT OF BIOMASS BURNING AEROSOLS AND PHOTOCHEMICAL POLLUTION IN THE ORINOCO RIVER BASIN

Andrea J. Hernandez a, Luis A. Morales-Rincon a, Dien Wu b, Derek Mallia b, John C. Lin b, Rodrigo Jimenez a,* a Universidad Nacional de Colombia – Bogota, Department of Chemical and Environmental Engineering, Air Quality Research Group, Bogota, DC 111321, Colombia b Land-Atmosphere Interactions Research Group, Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT 84112, USA 114 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

* Corresponding author: [email protected]; Carrera 30 No. 45-03, Edificio 453, Oficina 316, Bogota, DC 111321, Colombia; (+57-1) 316-5000 ext. 14099

Abstract

The “Llanos” is a complex savanna ecosystem that occupies most of the Orinoco River Basin, from the Colombian Andes foothills almost to the Orinoco River Delta at the Atlantic Ocean in Venezuela. It undergoes periodic, human-induced and natural biomass burning during the dry season, which is shorter in Colombia. Northeast trade winds that prevail during the dry season can transport biomass burning plumes from Venezuela to Colombia, even during the Colombian Llanos wet season. Our analysis is based on ambient air measurements at two middle size Colombian Llanos cities located 266 km apart, during April-May 2015, right after the end of the dry season in Colombia, but when there was still high fire activity in Venezuela. Observed 24-h averaged PM10 concentrations in the two cities were unexpectedly high (up to 112 μg/m3), particularly when considering their relatively small size, and low industrial and road traffic activity, and their trends were very similar, which indicates a common remote source. Ozone enhancements of up to ~94 ppbv (7-day average) were also observed. A reasonably good correlation was found between PM10 and a proxy of fire emissions within footprints calculated using the Stochastic Time-Inverted Lagrangian Transport model. There is currently no permanent air quality surveillance system in the Colombian Llanos. Our findings imply that such a system should be established and incorporate near real-time, synoptic scale biomass burning remote sensing information.

Keywords: Biomass burning, Orinoco River Basin, Transboundary pollution, Aerosols, Ozone, Lagrangian simulation

4.1 Introduction

Biomass burning (BB) is a significant source of trace gases and aerosols that affects human health, atmospheric chemistry and climate (Crutzen & Andreae, 1990; Langmann, Duncan, Textor, Trentmann, & van der Werf, 2009; van der Werf et al., 2010). Health effects are mainly caused by particulate matter (PM) and ozone. Most of BB emitted particles are within Chapter 4. Transboundary pollution 115 the accumulation mode (particle diameter < 1μm) (Reid & Koppmann, 2005), which implies low deposition velocities, long residence times in the atmosphere and potential for long- range transport. BB-derived organic particles contain mutagenic and carcinogenic components that lead to genotoxic effects on human alveolar cells, even when ambient air 3 PM10 concentrations are below the World Health Organization (WHO) standard (50 µg/m , 24-hour average) (De Oliveira Alves et al., 2014).

BB emissions can be transported at regional and synoptic scales, up to a few thousand kilometers, crossing state, national or even continental boundaries (Bergin, West, Keating, & Russell, 2005; Heilman, Liu, Urbanski, Kovalev, & Mickler, 2014), and are among the most common transboundary pollution sources (Bergin et al., 2005; Targino et al., 2013), which may affect urban areas, either from episodes resulting from exceptionally massive forest fires or as the result of periodic burnings in ecosystems. An example of the first type are the North American forest fires that affected U.S. cities (DeBell et al., 2004; Mallia, Lin, Urbanski, Ehleringer, & Nehrkorn, 2015; Sapkota et al., 2005). Examples of periodic burning are found in Asia, where forest and peat fires used to clear land in Indonesia have caused multiple episodes in urban areas of Singapore (Betha, Zhang, & Balasubramanian, 2014; Velasco & Rastan, 2015), Malaysia (Othman, Sahani, Mahmud, & Sheikh Ahmad, 2014) and other Southeast Asian countries (Lee et al., 2016), and in Brazil, where savanna fire emissions are transported to Argentina, Paraguay and Uruguay (Freitas et al., 2005). BB-derived pollution is difficult to mitigate, particularly for downwind countries that lack control of drivers and conditions in the upwind source regions (Lee et al., 2016). Thus international cooperation is necessary (Bergin et al., 2005; Targino et al., 2013).

The “Llanos” is a half a million km2 savanna ecosystem (501,075 km2 as estimated from Sarmiento, 1983), that occupies most of the Orinoco River Basin (ORIB) in Northern South America, from the Colombian Andes foothills almost to the Orinoco River delta at the Atlantic Ocean in Venezuela. This binational savanna ecosystem undergoes periodic and extensive BB. Although the presence of fire-tolerant plants in the Llanos demonstrates that fire is part of its natural dynamics (Medina & Silva, 1990; Sarmiento & Monasterio, 1975), most of the current BB is human induced (Armenteras, Romero, & Galindo, 2005). Fire is used by Native American communities for land clearing and hunting, and it is also an extensive cattle grazing management tool, as it allows to replace low-palatability mature natural pastures by fresh regrowth (Hernández-Valencia & López-Hernández, 2002; López-Hernández, 2012; 116 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Rippstein, Amézquita, Escobar, & Grollier, 2001). Romero-Ruiz et al. (2010) estimated that an average of 2.75 million hectares are burned each year during the dry season just in the Colombian side of the Llanos.

The BB period is determined by the dry season length, which although is mainly determined by the position of the Intertropical Convergence Zone (ITCZ) (Pulwarty, Barry, Hurst, Sellinger, & Mogollon, 1998) shows differences through the Llanos. Excluding the Andes foothills subregion, most of the Colombian Llanos have a unimodal precipitation regime, with a dry season that typically occurs from December to March (Pacheco & León-Aristizábal, 2001). The Eastern Llanos of Venezuela have a longer dry season that goes from November to April-May (Huber, Duno de Stefano, Aymard, & Riina, 2005). During the dry season (austral summer), the continental ITCZ migrates southward towards the Amazon basin, southern Colombia and Ecuador (Poveda, Waylen, & Pulwarty, 2006), intensifying the Northeast trade winds over the Llanos region, which can potentially transport emissions from Venezuela to Colombia. Also the Orinoco low-level jet, a persistent meteorological feature, transports air masses from the Orinoco River Delta to the Colombian Llanos (Torrealba, Amador, & Rica, 2010).

Several studies on the air quality effects of BB in the Venezuelan Llanos were conducted during the 1980-1990s, including on resulting enhancements of total suspended particle (TSP) concentrations (Sanhueza & Rondón, 1988; Sanhueza, Rondon, & Romero, 1987) and ozone production (Sanhueza, Crutzen, & Fernández, 1999). Only recently, the air quality in the Colombian Llanos has become of concern after observing significantly elevated

PM10 concentrations linked to BB in a remote rural area (Hernandez et al., 2015).

Furthermore, the simultaneousness and high correlation of elevated PM10 concentrations in 3 Colombian Andes cities that are distant from the ORIB have been attributed to BB in the Colombian ORIB (Belalcázar, Beckert, Rojas, & Chacon, 2015). While these studies analyzed air quality during the Colombian Llanos dry season, the transport of emissions from Venezuela to Colombia during the wet season in Colombia has not been reported before.

Here we focus on observed enhancements of aerosol and ozone concentrations in two middle size Colombian Llanos cities and the observational and atmospheric modeling Chapter 4. Transboundary pollution 117 evidence that bridges these enhancements with transboundary transport of savanna fire emissions during a period with fire activity in Venezuela, but not in Colombia.

4.2 Materials and Methods

4.2.1 Study area and emissions estimates

Measurements were made in Yopal (5°20’04” N, 72°23’40” W, 340 m ASL) and Arauca (7°45’12” N, 70°45’26” W, 130 m ASL), two middle size Colombian Llanos cities (~100 thousand inhabitants each) during 2015. The Llanos limit to the west with the Eastern branch of the Andes (Figure 3-1), which constitutes a significant barrier to atmospheric transport, creating multiple, rather decoupled airsheds, particularly in Colombia.

Air quality in the Llanos is affected by 1) local sources, mainly road traffic, dust resuspension, and other combustion and minor industrial sources; 2) regional sources, particularly biomass burning, eolian erosion, and the associated with oil production and agriculture, the main economic activities in the Colombian Llanos region; and could eventually be affected by 3) synoptic scale sources, such as Saharan dust.

In order to compare the relative importance of regional PM10 emissions, we used estimations of anthropogenic PM10 emissions from the Emission Database for Global Atmospheric Research (EDGAR v.4.3 - Olivier et al., 1994), of BB emissions from the Global Fire Emission Database (GFED4s - van der Werf et al., 2010), and our own estimations. GFED does not report PM10, so we used total particulate matter (TPM) and PM2.5 to bracket PM10 emissions. Considering that EDGAR is a global inventory that may not accurately represent emissions in the region, we also made first order calculations of PM10 emissions from agriculture and oil production in the Colombian Llanos, and compare them with EDGAR estimates. Details of our emission estimations are presented in the Supplementary Material.

118 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 4-1: Study area, including the Llanos region, the 2 cities were measurements were conducted, and the temporary AERONET UNC-Gaitan site (https://aeronet.gsfc.nasa.gov/).

4.2.2 PM10 and O3 measurements.

The PM10 and O3 measurements at urban areas of Arauca and Yopal were carried by K2 Ingenieria for Corporinoquia, one of two regional environmental authorities in the Colombian

Llanos. The PM10 measurements in the rural area of Arauca were carried out by the Air Quality Research Group of Universidad Nacional de Colombia – Bogota. MODIS aerosol optical thickness (AOT) measurements (Remer, Mattoo, Levy, & Munchak, 2013) were validated with AERONET measurements in the Colombian Llanos (UNC-Gaitan site)

(Llorente et al, 2018). During the 2015 Corporinoquia monitoring campaign, 24-hour PM10 samples were obtained every other day using standard high-volume samplers (Tisch Environmental, TE-6070V). Six monitoring stations were installed and operated in Yopal from March 30 to May 28. Three monitoring stations were installed in Arauca, two of which operated from April 1 to May 30 (designated as A1 and A2) and the other one from April 6 to May 5 (A3). Meteorological stations (Vantage PRO2, Davis) were deployed in Arauca’s A1 site and Yopal’s Y3 site.

Chapter 4. Transboundary pollution 119

The 3 Arauca sites are all urban, surrounded by roads, and were placed along the transverse axis of the city (Supplementary Material Figure S1). Five out of the 6 sites in Yopal were urban and 1 suburban (Y0 – see Supplementary Material). Ozone measurements were made using passive samplers (Passam). Four, duplicate samples with weekly exposure time were taken at each station. Measurement periods for Yopal were March 28-April 4, April 12-19, April 27-May 5, May 12-19, 2015 and for Arauca were April 1-9, April 16-24, May 1-9 and May 1-24, 2015.

Both, PM10 and O3 concentrations, are here reported at the Colombian reference conditions (1 atm, 25 °C), and are typically reported as mean ± standard deviation (1-sigma).

We also used data from a more recent measurement campaign conducted by Corporinoquia during September – December 2017 (data available at http://www.sisaire.gov.co:8080/faces/mediciones/medicionesDep.jsp). We used the 2017 measurements as baseline for PM10 and O3 as these were not significantly affected by biomass burning. We therefore report concentrations enhanced by a multitude of emission sources without (2017 measurements) and with (2015 measurements) the influence of BB.

4.2.3 Atmospheric transport and fire activity.

We estimated the origin of particles arriving at the measurement sites using the Stochastic Time-Inverted Lagrangian Transport (STILT) model (Lin et al., 2003), which simulates the transport of air parcels backward in time influenced by mean (advective) and turbulent wind components. From these air parcels’ backward trajectories, STILT calculates “footprints”, which represent the upstream influence of an upwind source located at (xi,yi), prior time tm, on concentrations at the receptor site, located at the 3-dimensional position 푥⃗⃗⃗⃗푟 , at time tr

푁푡표푡 푀푎푖푟 1 (4-1) 푓(⃗푥⃗⃗⃗푟 , 푡푟|푥푖, 푦푗, 푡푚) = ∑ ∆푡푝,푖,푗,푘 ℎ ⋅ 𝜌̅(푥푖, 푦푗, 푡푚) 푁푡표푡 푝=1

where Mair is the molecular weight of air, h is the atmospheric height in which surface fluxes are diluted (assumed as 50% of the boundary layer height), 𝜌̅ is the average air density below h, Ntot is the total number of particles backward-released from the receptor, and 120 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

∆푡푝,푖,푗,푘 is the period of time a particle p spends at time tm in a volume element (∆푥, ∆푦, ℎ)

o located over (xi, yi). The footprints were generated at a grid spacing of 0.1  0.1°. We drove STILT with GDAS meteorological fields (1o  1o grid spacing). In previous investigations on ORIB, we drove STILT with GDAS and WRF, and found good agreement between them, which could be expected bearing in mind the flat terrain predominance in the Llanos region. We run STILT releasing 100 air parcels every 3 hours during sampling days. Air parcels were tracked backwards in time for 72 hours, with positions calculated every 15 minutes. We estimated daily footprints as the average of footprints resulting from the 8 simulations of each day.

As the footprint value at a cell represents the sensitivity of the receptor to an emission occurring in that cell, then the footprint value multiplied by a proxy of emissions should be indicative of the contribution of these emissions to the concentration enhancement at the receptor. Our indicator of the contribution at the receptor of particles from fire emissions (CFER) is thus the sum of the product of footprint times emission proxy over all the cells. If

BB was an important aerosol source, then observed PM10 concentrations should be well correlated with CFER. We chose a proxy of emissions instead of a generic BB inventory because of 3 reasons: 1) Global BB inventories largely miss small fires, which are predominant in the Northern South America savanna ecosystems. Thus, global inventories largely underestimate BB emissions in the Llanos, as discussed above. Furthermore, making a detailed local emission inventory, that detects most of the small fires, is a complex and expensive task (Hernandez, Morales-Rincon, & Jimenez, 2017); 2) Our goal at this stage of the research was not to reproduce the pollution episode but to better understand it; 3) CFER was worth testing as a potential air quality forecasting alert tool.

We used Fire Radiative Power (FRP) as a proxy of fire emissions, as FRP has been found to be proportional to burned biomass (Wooster, Roberts, Perry, & Kaufman, 2005). We obtained FRP data from the Global Monthly Fire Location Product active fire product (MCD14ML, Collection 6; (Giglio, Csiszar, & Justice, 2006)), which combines active fires detected by MODIS sensors in Aqua and Terra platforms. There are some disadvantages regarding the use of polar orbit satellites like MODIS. They could underrepresent fire emissions since observations are limited to the overpass times (10:30 and 22:30 local time Chapter 4. Transboundary pollution 121

– LT for Terra, and 13:30 and 01:30 LT for Aqua), which misses fires occurring late afternoon, when a large fraction of fires occur in the Llanos as per the diurnal cycles in GFED (based on geostationary satellites) (van der Werf et al., 2010). Also, over the Equator there are data gaps between adjacent swaths. However, we chose MODIS over geostationary satellites (GOES) because its spatial resolution allows detecting smaller fires than GOES.

For each PM10 sampling day, we selected the fires detected with a confidence higher than 70% (as reported by MODIS), occurring during the sampling day and two days prior (as trajectories go back in time by 72 h), and calculated the sum of their FRPs in cells of 0.1o  0.1° (same spatial resolution than the 72-h footprint). We then calculated the CFER indicator by multiplying this value by the footprint value at the cell, and as

푛 퐹 (4-2) 퐶퐹퐸푅 = ∑(푓푎 ∙ ∑ 퐹푅푃푏) 푎=1 푏=1

where FRPb is the Fire Radiative Power of the fire b within the cell a, F is the total number of fires within the cell a, and fa is the footprint value at the cell a.

4.3 Results

The Emission Database for Global Atmospheric Research (EDGAR v.4.3 - Olivier et al.,

1994) reports total anthropogenic PM10 emissions of 1,355 tons/month for the Colombia- Venezuela Llanos during May 2010, the most recent year available. Agriculture contributes with 86% of those emissions (62% from agriculture activities not including fuel use and 24% from fuel combustion in the agriculture sector). The rest of the emissions correspond to road transportation (10%) and other sectors (3%). Emissions from oil production are included within the power industry category (1A1cii category of IPCC 1996), which accounts for 0.9% of EDGAR PM10 emissions. Our preliminary estimate of agricultural emissions (6,637 tons per year – see Supplementary Material) is of the same magnitude than EDGAR’s but has not been disaggregated by month yet.

Crop area increased by 21% between 2010 and 2014. Since there were no substantial technological changes in management practices, and most of EDGAR’s emissions are from the agriculture sector, PM10 anthropogenic emissions expectedly increased in a similar 122 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin proportion, which gives an estimate of 1,640 tons/month for May 2015 based on EDGAR’s estimate.

Our first order estimate of PM10 emissions from oil production gives 147 tons/month, and although this is an order of magnitude higher than EDGAR emissions for that sector (12 tons/ month), it accounts for less than 10% of the anthropogenic emissions. Our preliminary estimate of the eolian erosion PM10 emissions is 4,936 tons per year, ~93% of which climatologically occurs in the January-February period. Climatological eolian erosion emissions in April-May are negligible (less than 2 tons/month) (Gao, 2015).

BB emissions are much higher. For the period of the BB events here reported, the Global Fire Emissions Database version 4 (GFED4s – van der Werf et al., 2010) estimates total particulate matter (TPM) emissions at 16,376 ton/month for the Llanos region (May 2015). BB burning emissions could have been even higher. We developed a highly spatiotemporal disaggregated BB emission inventory for another 1-month period (January 17- February 18, 2015) just for the Colombian Llanos. Our estimates for the 2015 period were 1.9 times larger than GFED for that period (Hernandez et al., 2017). Even disregarding the potential GFED underestimation, BB emissions were one order of magnitude higher than all the other emissions put together during the period of the measurements here analyzed.

A combined analysis of the precipitation and active fires indicates that between 2001 and 2014, the dry seasons in the Llanos run from December to March in Colombia and from December to April in Venezuela. Typically, the highest fire activity in Colombia occurs during January and February, and from January to March in Venezuela. The 2014-2015 dry season was longer than the average for both countries, and so was the burning period. During 2015, fire activity lasted until March in Colombia and until May in Venezuela (Figure 4-2).

Therefore, the PM10 measurements here reported (April-May 2015) were conducted during a period of high BB activity in Venezuela and low BB activity in Colombia.

Chapter 4. Transboundary pollution 123

Figure 4-2: Monthly precipitation and fire activity statistics for the Venezuelan and Colombian Llanos for the period 2001-2014. The precipitation data were obtained from the Climatic Research Unit (CRU, Harris et al., 2014). B) Daily TPM emissions as estimated by GFED during the measurement campaign and the dry season of December 2014 – May 2015.

PM10 concentrations monitored at Arauca and Yopal were high, particularly when taking into account that these are comparatively small cities with small industrial and road traffic activity. Some concentrations even exceeded the Colombian daily air quality standard at the time of the measurements (100 µg/m³). Most of the days, PM10 concentrations were higher than the average concentration during Corporinoquia’s 2017 campaign, a period not significantly affected by biomass burning. The Yopal (site Y2) average concentration during the 2017 campaign was 37.6 ± 14.5 µg/m³. For Arauca (site A4, close to site A1), the 2017 average was 33.2 ± 13.6 µg/m³. PM10 enhancements of up to ~100 µg/m³ were observed during the 2015 campaign relative to the 2017 campaign averages.

Levels and trends of average PM10 at Arauca and Yopal, located 266 km apart from each other, were quite similar during April 1 – May 7 (r2 = 0.85), which indicates that a common regional source was controlling the aerosol concentration over a wide area (Figure 4-3). There is a clear, although nonlinear, relationship between TPM emissions, as calculated by GFED (Figure 4-3 B), and the observed concentrations at the two distant receptors. After May 9, the concentration synchrony between the receptors fades. Arauca experienced higher concentrations during the decoupled period, most of which were above 80 µg/m³. Error bars reflect within city variations due to local emissions.

124 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Mean back trajectories during the decoupled period show a significantly different origin for air masses arriving at Yopal and Arauca (Supplementary Material Figure S2). For instance, on May 9 and May 15, Yopal was influenced by Southeast (Amazonian) air masses, while Arauca kept receiving air masses from the Llanos. Other days, air masses arriving at Arauca came from the north, along the Maracaibo Lake region mixed layer, while Yopal received air masses from the Llanos but above the mixed layer. Back trajectories also reveal very complex circulation patterns that are frequent in the tropics, particularly under moist convection conditions.

Figure 4-3: A) PM10 concentrations and B) GFED TPM emissions during the measurement period. Blacks lines in panel A) denote the average concentration (solid line) and standard deviation (dotted line) measured in site Y3 during a period not affected by biomass burning (September – December 2017).

Chapter 4. Transboundary pollution 125

The precipitation was quite different in the 2 cities during the measurement period (Supplementary Material Figure S3). Most of the rain in Yopal fell during April while in Arauca it did in May. Despite this difference, the concentrations in both cities were quite similar before May, which further strengthen the hypothesis of a regional, common aerosol source not significantly abated by local wet deposition. As discussed below, total precipitation along the trajectory appears to be a more important aerosol controlling factor than local precipitation.

Figure 4-4: Ozone concentrations in Arauca (left side) and Yopal (right side) during the measurement period. Black lines denote the average concentration (solid line) and standard deviation (dotted line) of ozone concentrations measured in Arauca (site A4) and Yopal (Y3) during a period not affected by biomass burning (September – December 2017).

BB not only increased PM10 levels, but also caused photochemical pollution (Figure 4-4). Ozone levels observed at Arauca and Yopal were almost a factor 2 higher than those measured by Corporinoquia on a period not significantly affected by biomass burning (2017 campaign). The average O3 concentration during September – December 2017 was 67.111.3 µg/m³ at Arauca and 71.516 µg/m³ at Yopal, while the average weekly concentration during the April-May 2015 period was 12731 µg/m³ at Arauca and 14742 µg/m³ at Yopal. Ozone concentrations were lower at sites located close to main roads (Y5 at Yopal and A3 at Arauca) and higher at sites less influenced by road traffic (Y0 at Yopal and A1 at Arauca), as expected from ozone titration by nitric oxide from mobile sources.

The baseline O3 concentrations (2017 campaign) are consistent with the average of in situ aircraft-borne measurements in the boundary layer (BL) made on NASA's DC-8 while flying 126 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin over the Colombian Llanos during TC4 in 2007 (Avery et al., 2010; https://espo.nasa.gov/tc4). The transect selected as BL average (Figure S4) was not contaminated by BB plumes as confirmed by simultaneous airborne measurements of HCN, an excellent BB tracer (Simpson et al., 2011). The TC4 average background is estimated as 56 ± 6.2 µg/m3.

There are no studies reporting ozone concentrations in the Colombian Llanos, but studies in the Venezuelan Llanos showed ozone enhancements attributable to BB. Sanhueza et al. (1985) measured ozone at 4 sites on the Northeastern Venezuelan Llanos and reported an episode with ozone concentrations above 32 ppbv (a factor ~2 higher than the regional background at that time) during a BB period. Sanhueza & Fernández (2000) measured ozone concentrations at several sites in the Orinoco River Basin and reported that ozone concentrations increased from 13-17 ppbv during the wet season, to 20-30 ppbv during the dry season, likely due to photochemical production from BB emissions. More, recently, Calderon et al. (2008) analyzed ozone measurements at a remote, high altitude site on the Venezuelan Andes. They observed episodes with ozone mixing ratios higher than 60 ppbv associated with the arrival of BB air masses from the Barinas State plains in the Venezuelan Llanos. These last concentration levels are very similar to the observed in Arauca and Yopal during April-May 2015 (Figure 4-4).

Mauzerall et al. (1998) showed that the substantial, regional tropospheric O3 enhancements over the tropical South Atlantic can be explained by photo-oxidation of BB emissions. Their calculations are based on a simple box model, from which they estimate the synoptic scale ozone production from BB CO emissions, using an incremental reactivity ratio of ∆O3/∆CO = 0.59 mol/mol, as observed in aged plumes. In order to further substantiate the BB origin of the observed O3 enhancements at Arauca and Yopal, we used the same approach to estimate the regional ozone production from BB emissions in the Venezuelan Llanos. Using GFED’s daily average TPM and CO emissions of ~1000 tons and ~ 7411 tons, respectively, and assuming an average mixing height of ~1200 m, and an air basin residence time of ~3.5 days, we reckon the ozone enhancement at ~125 µg/m3. Thus, the enhancements observed in Yopal and Arauca can be fully explained by photo-oxidation of biomass burning emissions from the Venezuelan Llanos.

Chapter 4. Transboundary pollution 127

Bearing in mind the complexity involved, PM10 concentrations in both cities display rather reasonable correlations with CFER (r2 = 0.34 for Yopal and r2= 0.12 for Arauca) (Figure 4-5). Concentrations after May 9 were not included as they appear to be controlled by local rather than regional sources, as mention above. In addition, some of the trajectories after May 9 are spatially segregated. The average precipitation along the air parcel trajectories appears to play a major control role in PM10 concentrations. Intervals of high (>150 mm), middle (50- 150 mm) and low (0-50 mm) precipitation reveal data point clusters of low, middle and high fire influence and PM10 concentrations. Although useful, this accumulated precipitation parameter does not fully account for the complex spatiotemporal effects of wet deposition on variable BB emissions.

Figure 4-5: Correlation of PM10 at Arauca and Yopal with the indicator of the contribution at the receptor of particles from fire emissions (CFER). The data shown corresponds to the daily average at each city during the coupled period of time (April 1 – May 7, 2015). The color and fill of the points denotes the city (Arauca in gray/empty, Yopal in orange/full). Point size denotes accumulated rain along the 72-h backward trajectory.

May 1-6 was the period of time with the highest number of active fires in Venezuela, during which high PM10 concentrations were observed in both Colombian cities (average PM10 of 91.6 µg/m³ and 89.5 µg/m³ at Yopal and Arauca, respectively). Figure 4-6 shows (a) MODIS AOD, (b) MODIS Angstrom exponent and (c) active fires and footprints for Yopal on 3rd May. 128 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

On this day, two sites in Yopal and one in Arauca experienced concentrations above the Colombian daily air quality standard (100 µg/m³), and footprints showed air masses coming from the Venezuelan Orinoco Region.

Figure 4-6: A) MODIS-Terra Aerosol Optical Depth (AOD), B) MODIS-Terra Angstrom exponent, and C) active fires and footprints (for Yopal) for 3rd May 2015. Footprints were calculated from 72-h backward trajectories. Active fires included in C) are fire detections from May 1-3. Fires in (a) are the ones detected by Terra in the morning overpass time (10:30 LT) when AOD was measured. Terra morning does not capture the hours of high fire activity (15:00-21:00 LT), but its AOD provides an indicator of the advection of emissions from Venezuela to Colombia.

Regionally high AOD was observed in the Colombian Llanos. It is important to highlight that this AOD measurements come from the Terra platform, which has an overpass time of 10:30 LT, when little fire activity is observed. According to data from GOES, most of the fires in the region occur between 15:00 and 21:00 LT. Therefore, the AOD observed by Terra indicates the propagation of emissions that occurred over 12 hours prior the observation, which may explain why the high AOD region is located over the Colombian Llanos rather than over Venezuela. The Angstrom exponent (AE), which is an indicator of aerosol size and type, indicates that the high AOD over the Colombian Llanos is caused by fine mode aerosols, very likely generated by biomass burning. The AE was higher than 1.3 for Colombian Llanos, a threshold value that has been used to separate fire events (fine mode particles) from dust events (coarse mode particles) (Hallar et al., 2015). Therefore, it rules out Saharan dust as a potential source impacting the Colombian Llanos. Chapter 4. Transboundary pollution 129

Figure 4-7: A) Time series of active fires in Colombia and Venezuela as observed by MODIS and AERONET measurements at the UNC-Gaitan site. Top panel shows the MODIS 3-day mean of active fires in the Colombian (orange) and Venezuelan (black) Llanos. Panels below show AOD, AE and fine mode fraction measured at UNC-Gaitan. B) Monthly MODIS AOD for the region shown in Figure S6. Boxes represent percentile 25, 50 and 75 of daily AOD for each month during the period 2010-2016. The thick red line corresponds to the monthly average of daily AOD for 2015.

AERONET AOD measurements in Puerto Gaitan – Colombia (UNC-Gaitan; https://aeronet.gsfc.nasa.gov/ –Figure 4-1), located at ~115 km Southwest Yopal (and ~350 km Southwest Arauca), reveal a strong yet complex relation between fire counts, most of them in Venezuela, and AOD over the central Colombian Orinoco River Basin (Figure 4-7A). Simultaneously high AOD and Angstrom exponent values are observed during March-April 2016, while lower AOD and Angstrom exponent values are observed during the low fire activity period of May-July 2016. Unfortunately, for logistic reasons we could not measure during the Colombian North ORIB (Arauca-Yopal) campaign period (April-May 2015) here reported. It must be mentioned that the high AOD levels observed during September 2015 are due to synoptic-scale transport of aerosols from biomass burning in the Brazilian 130 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Amazonia and Cerrado at ~2000 km from the Colombian ORIB, Figure S5. Figure 4-7B shows the monthly average of MODIS Aerosol Optical Depth over the Colombian Llanos during the 2010-2016 period. The highest AOD values are typically observed during March- April. The dry season started late on 2015 and led to higher-than-normal peak AOT on April and May.

4.4 Conclusions

We analyzed campaign PM10 and ozone measurements in two middle-size Colombian Orinoco cities with little industrial and road traffic activity, and explored their relation with fire activity in the Venezuelan Orinoco savannas. PM10 and ozone concentrations were surprisingly high with even some concentrations above the PM10 Colombian air quality standard.

We have found strong evidence of transboundary transport of aerosols and secondary pollution from Venezuela to Colombia, including the 1) striking similarity of the PM10 time series of the two cities, located ~266 km apart, 2) large ozone enhancements, only explained by long-range transport, 3) regionally high AOD, 4) back trajectories showing strong influence of areas with BB, particularly in Venezuela, and finally a 5) reasonable correlation between PM10 concentration and a fire contribution indicator.

3 The two cities have a very similar average daily baseline PM10 concentration of ~35 µg/m . This is not a background concentration but the result of local and regional emissions other than BB, which can episodically add up to ~100 µg/m3 to this baseline level.

The fire emission contribution indicator derived from stochastic Lagrangian simulation is a useful indicator of the influence of BB emissions, particularly remote ones, on aerosol levels and may be used as a short-term air quality forecasting alert tool. We also found that the accumulated precipitation along the trajectories significantly controls both, the indicator and the PM10 concentrations.

Given its transboundary characteristics, the regional environmental authorities in Colombia must develop an air quality surveillance system that, besides standard in situ monitoring, Chapter 4. Transboundary pollution 131 includes remote sensing of BB, both in Colombia and Venezuela, and potentially near real- time forecasting simulation. Also, further research is needed to understand the BB plumes fate and their impact on the Andean region.

Acknowledgments, Samples, and Data

This research was funded by Colciencias (Colombian Administrative Department of Science, Technology and Innovation), through grant number 1101-569-35161 for the project “Atmospheric emissions and impact of land use change and intensive agriculture on air quality in the Colombian ORIB”. Colciencias also funded this research through a Ph.D. grant for Andrea Juliana Hernandez-Villamizar. We also gratefully acknowledge Corporinoquia, K2 Ingenieria (Henry Castro), and IDEAM (Ana M. Hernandez) for providing the data used in this research, and Fulbright Colombia for funding the research stay of Andrea J. Hernandez at The University of Utah. We also thank the anonymous reviewers, whose insightful comments and questions contributed to clarify and improve this paper. Finally, we acknowledge the use of data from LANCE FIRMS operated by the NASA/GSFC/Earth Science Data and Information System (ESDIS) with funding provided by NASA/HQ. The MODIS/Terra Aerosol 5-Min L2 Swath 3 km dataset was acquired from the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/). Data used in this publication are available upon request.

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Atmospheric Chemistry and Physics, 15(23), 13665–13679. https://doi.org/10.5194/acp-15- 13665-2015 Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711 Heilman, W. E., Liu, Y., Urbanski, S., Kovalev, V., & Mickler, R. (2014). Wildland fire emissions, carbon, and climate: Plume rise, atmospheric transport, and chemistry processes. Forest Ecology and Management, 317, 70–79. https://doi.org/10.1016/j.foreco.2013.02.001 Hernández-Valencia, I., & López-Hernández, D. (2002). Pérdida de nutrimentos por la quema de la vegetación en una sabana de Trachypogon. Revista de Biologia Tropical, 50(3–4), 1013–1019. Hernandez, A. J., Morales-R, L. A., Tatis Bautista, A. D., Rodríguez, N., Cañas Soler, F., & Jimenez, R. (2015). Entendiendo la calidad del aire en la Orinoquia colombiana durante la temporada de quema de biomasa. In V Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública. Bucaramanga. Hernandez, A. J., Morales-Rincon, L. A., & Jimenez, R. (2017). Desarrollo de una nueva metodología para la estimación de inventarios de emisión por quema de biomasa apropiados para simulación Lagrangiana, y su aplicación a la Orinoquia colombiana. In VI Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública. Cali, Colombia. Retrieved from https://www.researchgate.net/profile/Rodrigo_Jimenez2 Huber, O., Duno de Stefano, D., Aymard, G., & Riina, R. (2005). Flora and Vegetation of Venezuelan Llanos: A Review. In T. Pennington, G. P. Lewis, & J. A. Ratter (Eds.), Neotropical savannas and seasonally dry forests : plant diversity, biogeography, and conservation (pp. 95–120). CRC/Taylor & Francis. Langmann, B., Duncan, B., Textor, C., Trentmann, J., & van der Werf, G. R. (2009). Vegetation fire emissions and their impact on air pollution and climate. Atmospheric Environment, 43(1), 107–116. https://doi.org/10.1016/j.atmosenv.2008.09.047 Lee, J. S. H., Jaafar, Z., Tan, A. K. J., Carrasco, L. R., Ewing, J. J., Bickford, D. P., … Koh, L. P. (2016). Toward clearer skies: Challenges in regulating transboundary haze in Southeast Asia. Environmental Science and Policy, 55, 87–95. https://doi.org/10.1016/j.envsci.2015.09.008 Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., & Grainger, C. A. (2003). A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model. Journal of Geophysical 134 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Research, 108(D16), ACH 2-1-ACH 2-17. https://doi.org/10.1029/2002JD003161 Llorente, A. M. (2017). Evaluación de la variabilidad espaciotemporal de la profundidad óptica de aerosoles en la zona carbonífera del Cesar a partir de observaciones satelitales. Universidad Nacional de Colombia. López-Hernández, D. (2012). Earthworm Populations in Savannas of the Orinoco Basin. A Review of Studies in Long-Term Agricultural-Managed and Protected Ecosystems. Agriculture, 2(4), 87– 108. https://doi.org/10.3390/agriculture2020087 Mallia, D. V, Lin, J. C., Urbanski, S., Ehleringer, J., & Nehrkorn, T. (2015). Impacts of upwind wildfire emissions on CO, CO2, and PM2.5 concentrations in Salt Lake City, Utah. Journal of Geophysical Research : Atmospheres, 120, 147–166. https://doi.org/10.1002/2014JD022472.Received Mauzerall, D. L., Logan, J. A., Jacob, D. J., Anderson, B. E., Blake, D. R., Bradshaw, J. D., … Talbot, B. (1998). Photochemistry in biomass burning plumes and implications for tropospheric ozone over the tropical South Atlantic. Journal of Geophysical Research-Atmospheres, 103(D7), 8401–8423. Retrieved from //000073172100016 Medina, E., & Silva, J. F. (1990). Savannas of northern South America: a steady state regulated by water-fire interactions on a backround of low nutrient availability. Journal of Biogeography, 17(4), 403–413. https://doi.org/10.2307/2845370 Olivier, J. G. J., Bouwman, A. F., van der Maas, C. W. M., & Berdowski, J. J. M. (1994). Emission database for global atmospheric research (Edgar). Environmental Monitoring and Assessment, 31–31(1–2), 93–106. https://doi.org/10.1007/BF00547184 Othman, J., Sahani, M., Mahmud, M., & Sheikh Ahmad, M. K. (2014). Transboundary smoke haze pollution in Malaysia: Inpatient health impacts and economic valuation. Environmental Pollution, 189, 194–201. https://doi.org/10.1016/j.envpol.2014.03.010 Pacheco, Y., & León-Aristizábal, G. (2001). Clasificación climática de la orinoquia colombiana a partir de los patrones de circulación atmosférica. Meteorología Colombiana, 4, 117–120. Poveda, G., Waylen, P. R., & Pulwarty, R. S. (2006). Annual and inter-annual variability of the present climate in northern South America and southern Mesoamerica. Palaeogeography, Palaeoclimatology, Palaeoecology, 234(1), 3–27. https://doi.org/10.1016/j.palaeo.2005.10.031 Pulwarty, R. S., Barry, R. G., Hurst, C. M., Sellinger, K., & Mogollon, L. F. (1998). Precipitation in the Chapter 4. Transboundary pollution 135

Venezuelan Andes in the context of regional climate. Meteorology and Atmospheric Physics, 67(1–4), 217–237. https://doi.org/10.1007/BF01277512 Reid, J., & Koppmann, R. (2005). A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmospheric Chemistry and Physics, 5, 799–825. Retrieved from http://www.atmos-chem-phys.net/5/799/2005/ Remer, L. a., Mattoo, S., Levy, R. C., & Munchak, L. a. (2013). MODIS 3 km aerosol product: Algorithm and global perspective. Atmospheric Measurement Techniques, 6(7), 1829–1844. https://doi.org/10.5194/amt-6-1829-2013 Rippstein, G., Amézquita, E., Escobar, G., & Grollier, C. (2001). Condiciones naturales de la sabana. In G. Rippstein, G. Escobar, & F. Motta (Eds.), Agroecología y biodiversidad de las sabanas en los Llanos Orientales de Colombia. Cali, Colombia: Centro Internacional de Agricultura Tropical. Romero-Ruiz, M., Etter, a., Sarmiento, a., & Tansey, K. (2010). Spatial and temporal variability of fires in relation to ecosystems, land tenure and rainfall in savannas of northern South America. Global Change Biology, 16(7), 2013–2023. https://doi.org/10.1111/j.1365- 2486.2009.02081.x Sanhueza, E., Crutzen, P. J., & Fernández, E. (1999). Production of boundary layer ozone from tropical American Savannah biomass burning emissions. Atmospheric Environment, 33, 4969– 4975. Retrieved from http://www.sciencedirect.com/science/article/pii/S1352231099003015 Sanhueza, E., & Fernández, E. (2000). Boundary layer ozone in the tropical America northern hemisphere region. Journal of Atmospheric Chemistry, 35, 249–272. Retrieved from http://link.springer.com/article/10.1023/A:1006270911796 Sanhueza, E., Octavio, K. H., & Arrrocha, A. (1985). Surface Ozone Measurements in the Venezuelan Tropical Savannah. Jornal of Atmospheric Chemistry, 2, 377–385. Sanhueza, E., & Rondón, A. (1988). Particle-size distribution of inorganic water soluble ions in the Venezuelan savannah atmosphere during burning and nonburning periods. Journal of Atmospheric Chemistry, 7, 369–388. Retrieved from http://link.springer.com/article/10.1007/BF00058711 Sanhueza, E., Rondon, A., & Romero, J. (1987). Airborne particles in the Venezuelan Savannah during burning and non-burning periods. Atmospheric Environment, 21(10), 2227–2231. Sapkota, A., Symons, J. M., Kleissl, J., Wang, L., Parlange, M. B., Ondov, J., … Buckley, T. J. (2005). 136 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Impact of the 2002 Canadian forest fires on particulate matter air quality in Baltimore city. Environmental Science & Technology, 39(1), 24–32. https://doi.org/10.1021/es035311z Sarmiento, G. (1983). The savannas of tropical America. In F. Bourliere (Ed.), Ecosystems of the World XIII. Tropical Savannas (pp. 245–288). Amsterdam: Elsevier. Sarmiento, G., & Monasterio, M. (1975). A critical consideration of the environmental conditions associated with the occurrence of savanna ecosystems in tropical America. In Tropical Ecological Systems (pp. 223–250). Simpson, I. J., Akagi, S. K., Barletta, B., Blake, N. J., Choi, Y., Diskin, G. S., … Blake, D. R. (2011). Boreal forest fire emissions in fresh Canadian smoke plumes: C1-C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN. Atmospheric Chemistry and Physics, 11(13), 6445–6463. https://doi.org/10.5194/acp-11-6445-2011 Targino, A. C., Krecl, P., Johansson, C., Swietlicki, E., Massling, A., Coraiola, G. C., & Lihavainen, H. (2013). Deterioration of air quality across Sweden due to transboundary agricultural burning emissions. Boreal Environment Research, 18(1), 19–36. Torrealba, E. R., Amador, J. a., & Rica, C. (2010). La corriente en chorro de bajo nivel sobre los Llanos Venezolanos de Sur América. Revista de Climatología, 10(July), 1–10. van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., … van Leeuwen, T. T. (2010). Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10(23), 11707–11735. https://doi.org/10.5194/acp-10-11707-2010 Velasco, E., & Rastan, S. (2015). Air quality in Singapore during the 2013 smoke-haze episode over the Strait of Malacca: Lessons learned. Sustainable Cities and Society, 17, 122–131. https://doi.org/10.1016/j.scs.2015.04.006 Wooster, M. J., Roberts, G., Perry, G. L. W., & Kaufman, Y. J. (2005). Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research, 110(D24), D24311. https://doi.org/10.1029/2005JD006318

Chapter 5. Understanding atmospheric transport of biomass burning emissions 137

5. Understanding atmospheric transport of biomass burning emissions

As shown in previous chapters biomass burning is a significant source of pollution in the Llanos region. The extent to which emitted particles impact the receptors are determined by complex atmospheric transport processes that can be simulated using atmospheric dispersion models.

The objectives addressed in this chapter are to understand the atmospheric transport of the emitted particles and to estimate the contribution to biomass burning emissions to levels of particulate matter observed in the Llanos region.

First section describes the atmospheric models that have been used to study the impact of biomass burning emissions on air quality and describes the Lagrangian modeling framework that was selected for this thesis. Section two presents the results of simulating the impact of biomass burning emissions (estimated in chapter 2) on the air quality of Taluma. These results are presented in the manuscript: “Understanding the impact of biomass burning on air quality pollution in Northern South America savannas through Lagrangian simulations and a tailor-made emission inventory”

5.1 Atmospheric dispersion modeling

There are two reference frames for modeling the atmospheric flow: the Eulerian, which describes concentrations in fixed points using the continuity equation, and Lagrangian, in which air parcels or particles are followed as they move in the turbulent atmosphere (Arya, 1999). Each approach has advantages and disadvantages some of which are described in Table 5-1.

138 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 5-1: Advantages and disadvantages of Eulerian and Lagrangian approaches (Based on (John C. Lin, 2012; Whelpdale, 1991) Modeling Advantages Disadvantages approach Eulerian • Able to teat complex processes • Complex models that require including the 3-dimensional large computation time, advection/diffusion and chemical storage and input data. reactions. • Computational dispersion • Operate on a fixed grid in which associated to numerical input data fields are defined integration of the advection • Automatically provides the equation geographical distribution of • Stability constraints pollutants in defined grid cells over the entire model domain Lagrangian • Availability of trajectory • Non-linear chemical processes information cannot be incorporated. • Physical realism: the atmospheric • Uneven spatial resolution that flow consists of molecules being requires additional steps to be transported and thus transport interpolated to a fixed grid. phenomena are better • Requieres external approximate using the meteorological data. Lagrangian approach • Ignore coupling processes • Can be used to estimate (e.g. aerosol impact on contributions from individual meteorology) sources or at individual receptors • Numerically stable • Minimal numerical diffusion • Parametrizes subgrid scale variability

The formulation of these approaches can be formally interchanged as described in (Brasseur & Jacob, 2017; John C. Lin, 2012). The Lagrangian perspective is expressed as the total derivative:

Chapter 5. Understanding atmospheric transport of biomass burning emissions 139

퐷휓 = 푆 (5-1) 퐷푡

Where 휓 is any state variable of the parcel, for example velocity, temperature, humidity, or pollutant concentration, S denotes sources or sinks and D휓/Dt represents the rate of change of 휓 following the air parcel. On the other hand, the continuity equation from an eulerian perspective is expressed as:

휕휓 ∂ψ ∂ψ ∂ψ 휕휓 + 푢 ∙ + 푣 ∙ + 푤 ∙ = + 풖 ∙ ∇ψ = S (5-2) 휕푡 ∂x ∂y ∂z 휕푡

Where 휕휓/휕푡 represents the rate of change of 휓 in a fixed point, u, v and w are the components of velocity u, ∂ψ/ ∂x, ∂ψ/ ∂y and ∂ψ/ ∂z are the gradients of 휓 in each direction at the fixed point and ∇ is the spatial gradient operator at the same position.

Lagrangian modeling is also known as trajectory modeling since it follows air parcels position, x. Most Lagrangian models do not solve the Lagrangian equation for u, rather, they obtained it from Eulerian models such us numeric weather prediction models and then used it to calculate particle trajectories (John C. Lin, 2012).

Lagrangian models include mean trajectories models, box models, puff models and Lagrangian particle dispersion models, which differ in the way of representing air parcels. Mean trajectories models are the simplest representation of air parcel trajectories, in which the turbulent component is neglected and a single line resulting from the mean wind is considered enough to describe the parcel motion (John C. Lin, 2012). Box models aggregate parcels in boxes that are advected horizontally and whose volumes are dependent on the mixing height (Zannetti, 1990). Puff models represent parcels as puffs that grow in size taking Gaussian distributions in the three directions and are advected horizontally (Brasseur & Jacob, 2017; John C. Lin, 2012).

Particle dispersion models (LPDM) are the most sophisticated Lagrangian models. These models represent parcels as particles that are transported by advective wind fields (obtained from meteorological models) and by a sub-grid turbulent velocity that is simulated as an stochastic component (Brasseur & Jacob, 2017). This approach of following particles in a 140 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin turbulent flow is considered a more natural way of describing dispersion than the Eulerian approach (Luhar, 2012).

Lagrangian modeling has the advantage of allowing either forward or backward simulations. Forward modeling describes the impact of a given source in downwind concentrations while backward modeling determines the influence of sources upwind on a receptor of interest (J. C. Lin et al., 2003).

Both approaches, Lagrangian and Eulerian, have been used to study the dispersion and the impact of biomass burning emissions. Some examples of the application of Eulerian modeling include: the study of specific wildfire events, such as the registered in Santiago de Chile that was simulated using WRF/Chem (Cuchiara et al., 2017); the study of the transport of biomass burning emissions and emission inventories assessment in the Brazilian Amazon and Cerrado by using the model CCAT-BRAMS (Pereira et al., 2016); and the study of the contribution of biomass burning to urban air quality in Hong Kong using GEOS-Chem (Chan, 2017).

Applications of Lagrangian modeling for studying the biomass burning impacts include: the study of the transport of biomass burning emissions from North America to the Mediterranean Basin using the Flexible Particle (FLEXPART) model (Ancellet et al., 2016; Brocchi et al., 2018) and the study of the impact of western U.S wildfires on CO, CO2 and PM2.5 levels in Salt Lake City in which the Stochastic Time-Inverted Lagrangian Transport (STILT) model was used (Mallia et al., 2015).

For this thesis the backward Lagrangian approach was selected considering the following aspects. First, the main interest was to estimate the contribution of biomass burning to selected receptors in the Orinoco River Basin in which measurements were conducted, for which the Lagrangian backward modeling approach is more appropriate since it focuses only on the trajectories that influence the receptors. Second, the Lagrangian approach also allows to calculate the surface influence which is also useful to understand the transport of fire emissions and the area of influence over the receptor of interest.

Chapter 5. Understanding atmospheric transport of biomass burning emissions 141

The most widely used Lagrangian particle models, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), FLEXPART and STILT were evaluated with measurements from controlled tracer releases and they all showed comparable skills in simulating tracer plumes when using the same meteorological fields (Hegarty et al., 2013b).

For this thesis, the STILT model was chosen since there were ongoing projects for implementing plume injection height (J. Lin, Mallia, Urbanski, & Kochanski, 2015), which is important when modeling biomass burning emissions. However, by the time of conducting the simulations the module of fire injection was still under development and could not be used for this thesis.

5.1.1 Lagrangian particle dispersion models (LDPM)

LDPM calculate particle positions as:

′ 푿푖(푡 + ∆푡) = 푋푖(푡) + [풖푖(푡) + 풖푖(푡)]∆푡 (5-3)

Where the stochastic component ui’ is estimated as:

′ ′ ′′ 풖푖(푡) = 풖푖(푡 − ∆푡)푅푖(∆푡) + 풖푖 (푡 − ∆푡) (5-4)

’’ Where ∆푡 is the time step, ui is a random vector and R is an autocorrelation coefficient that is calculated as exp(-∆푡 /TL), where TL is the Lagrangian time-scale (J. C. Lin et al., 2003), which determines “the degree to which particles keep the memory of previous motion” (Pillai et al., 2012).

Concentration fields are estimated based on the predicted particle positions through the particle position probability density function (Pielke et al., 1991). This probability density function, also known as transition probability density, is defined as the probability that the air parcel moves from X’ at t’ to X at t. By mass conservation, when chemical or deposition are not considered, the integration of the probability density over the entire atmosphere is equal to 1 (Daniel J. Jacob, 1999):

142 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

∫ 푝(푿, 푡|푿′, 푡′) 푑푥 푑푦 푑푧 = 1 (5-5) 푎푡푚

The concentration field at X, t, is given by:

푡 퐶(푋, 푡) = ∫ ∫ 푝(푿, 푡|푿′, 푡′) 푆( 푿′, 푡′) 푑푥′푑푦′푑푧′푑푡′ (5-6) −∞ 푎푡푚

Where S(X’,t’) represents the source term (mass volume-1 time-1). (5-6 can be rewritten as the sum of two integral terms, the first one represents the source contribution before t0, and the second one describes the effects of the sources during the period t0

′ ′ 퐶(푋, 푡) = ∫ 푝(푿, 푡|푿 , 푡0) 퐶(푋 , 푡0) 푑푥′ 푑푦′ 푑푧′ 푎푡푚 (5-7) 푡 + ∫ ∫ 푝(푿, 푡|푿′, 푡′) 푆( 푿′, 푡′) 푑푥′푑푦′푑푧′푑푡′ 푡0 푎푡푚

For the case of backward modeling, in which the interest is the concentration at the receptor

(Xr) rather than the whole concentration field over X, equation (5-7 can be expressed as:

퐶(푿풓, 푡푟) = ∫ 퐼(푿풓, 푡푟|푿, 푡0) 퐶(푋, 푡0) 푑푥 푑푦 푑푧 푎푡푚 (5-8) 푡 + ∫ ∫ 퐼(푿풓, 푡푟|푿, 푡) 푆( 푿, 푡) 푑푥 푑푦 푑푧 푑푡 푡0 푎푡푚

Where 퐼(푿풓, 푡푟|푋, 푡)is the influence function constructed from backward time particles location, which is formally calculated as (J. C. Lin et al., 2003):

푁푡표푡 𝜌(푿풓, 푡푟|푿, 푡) 1 퐼(푿풓, 푡푟|푿, 푡) = = ∑ 훿(푿푝(푡) − 푿) (5-9) 푁푡표푡 푁푡표푡 푝=1

Where Ntot is the total number of particles and 훿 represents the presence or absence of particle p at location X at t. As shown by (J. C. Lin et al., 2003) the time and volume- Chapter 5. Understanding atmospheric transport of biomass burning emissions 143 integrated function is represented by the time each particle p spends in a volume element (i,j,k) over time step m, divided by the total number of particles.

The source term is represented by diluting the surface emission F(x,y,t) in units of (µmoles m-2 s-1) in an atmospheric column of height h, and average density 𝜌̅(푥, 푦, 푡):

퐹(푥, 푦, 푡)푚 푆(푋, 푡) = 푎푖푟 (5-10) ℎ 𝜌̅(푥, 푦, 푡)

When that source term is replaced in (5-8 and integrated over discrete volume and time element the change in concentration in units of (ppm) at the receptor in response to the emission F can be calculated as:

푁푡표푡 푚푎푖푟 1 ∆퐶푚,푖,푗(푿푟, 푡푟) = [ ∑ ∆푡푝,푖,푗,푘 ] 퐹(푥푖, 푦푗, 푡푚) ℎ 𝜌̅(푥푖, 푦푖, 푡푚) 푁푡표푡 푝=1 (5-11)

= 푓(푿풓, 푡푟|푥푖, 푦푖, 푡푚) 퐹(푥푖, 푦푗, 푡푚)

2 -1 Where 푓(푿풓, 푡푟|푥푖, 푦푖, 푡푚) is defined as the footprint in units of (m s molair ) and represents the sensitivity of an observation to upstream surface fluxes. (5-11 was originally developed for trace gases and express the change in concentration in units of ppm. However, when simulating particulate matter and emission fluxes are in units of (g PMx m-2 s-1), the footprint

2 -1 2 -3 term can be converted from (m s molair ) to (m s m air) by multiplying by the density

𝜌̅(푥푖, 푦푖, 푡푚) and dividing by the molar weight of air 푚푎푖푟, obtaining concentrations of particulate matter in terms of mass per volume.

5.2 Manuscript: Understanding the impact of biomass burning on air quality pollution in Northern South America savannas through Lagrangian simulations and a tailor-made emission inventory

A.J. Hernandez V., L.A. Morales R., Lin, J., Jimenez R.

144 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

5.2.1 Abstract

Northern South America´s air quality has been shown to be impacted by biomass burning emissions from the Llanos savannas. However, quantifying the contribution of this regional pollution source has been difficult due to the absence of monitoring sites and also because biomass burning is a complex, non-steady state problem, of high spatiotemporal variability. Here we developed a tailor-made emission inventory that combines multiple satellite products to ensure the detection of small fires that are predominant in the Llanos savannas and the temporal disaggregation of the emissions. Then, we used the Stochastic Time- Inverted Lagrangian model (STILT) to understand the atmospheric transport of biomass burning emission and estimate the fraction of the enhancements of particulate matter observed in a measurement campaign conducted in a remote area in the Llanos region. Burned area in Colombian llanos were 1.8 times higher than Global Fire Emission Database (GFED) burned area, and emissions were 2 times higher than GFED estimates. The high variability in the cell to cell ratio of burned area between emission inventories and the fact that small fires are not properly detected in global product methodologies makes global emissions products difficult to scale. Although simulations using our emission inventory better reproduced the observed PM10 enhancements than when using GFED emissions, a high fraction of the enhancement could not be explained by the transport of primary emissions. Discrepancies between simulated and observed enhancements were not caused by wind fields as demonstrated by the comparison between simulations driven by WRF simulations and GDAS. An analysis of the dependency of the magnitude of the discrepancy and the origin of the contribution and the good correlation found between the indicator of the contribution at the receptor of particles from fire emissions (CFER) and PM10, suggest that secondary aerosols make a significant contribution to PM10 enhancements.

5.2.2 Introduction

Biomass burning (BB) is a significant global source of smoke particles and trace gases, including greenhouse gases such as carbon dioxide, methane and nitrous oxide and chemically active gases such as nitric oxide, carbon monoxide and volatile organic compounds (Crutzen & Andreae, 1990; Koppmann, Czapiewski, & Reid, 2005). Therefore, BB emissions contribute to climate change, perturb global atmospheric chemistry and degrade air quality, affecting human health, and perturb global and regional meteorology via Chapter 5. Understanding atmospheric transport of biomass burning emissions 145 aerosol radiative forcing (Crutzen & Andreae, 1990; Langmann et al., 2009). BB emitted particles are of special concern for human health due to its ultrafine size and composition. Most of biomass burning emitted particles are within the accumulation mode (particle diameter < 1μm) (Reid & Koppmann, 2005) and its organic components have shown to cause DNA damage and cell death in human lung cells (De Oliveira Alves et al., 2017).

According to data from the Global Fire Emission Database (van der Werf et al., 2010), during 2016, 31.7 Tg of PM2.5 were emitted globally, 16% in South America. Most of BB research in South America has focused on Brazil Amazon and Cerrado regions, which makes around 80% of South America emissions. The impact of fires occurring in savannas of northern South America (“Llanos“) have been almost absent from literature. The “Llanos“, located in Colombia and Venezuela Orinoco River Basin (ORIB), constitutes the second largest savanna ecosystem of South America and undergoes periodic and extensive BB (Romero- Ruiz, Etter, Sarmiento, & Tansey, 2010). Although on a smaller scale than fires in Cerrado savannas, they may threaten the health of comunnities living downwind Llanos savannas.

Growing evidence indicates that biomass burning plays an important role on baseline and episode air quality in Northern South America. Recently, we reported transboundary transport of BB aerosols and photochemical pollution caused by fires that ocurred in Venezuelan Llanos when fire season in Colombia had already ended (Hernandez et al., 2019). Nevertheless, the absence of monitoring sites, even of weather stations, makes difficult to attribute impact on local and regional air quality to BB.

This source attribution problem is usually tackled using simulation and/or analytic tools (chemical composition for receptor modeling). One of the fundamental problems when simulating BB impacts is building emission inventories, because BB is a complex, non- steady state problem, of high spatiotemporal variability. Emission estimation is even more difficult in the Llanos savannas where available information shows the predominance of small fires with more than 75% of patches smaller than 115 ha (Armenteras, Romero, & Galindo, 2005), which is smaller than the minimum detectable burned size of available global burned area products (120 ha for MCD64 product)(Giglio, Loboda, Roy, Quayle, & Justice, 2009).

146 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

In this manuscript, we address the following research questions: 1) To what extent satellites can detect fires that occur in the Llanos region? 2) Which fraction of enhancements of particulate matter observed in the Llanos region can be attributed to biomass burning? For that, we developed an emission inventory that integrates multiple satellite products, we simulated the driving meteorology, implemented them into Lagrangian simulations and compared the resulting BB emission contribution with observations from a measurement campaign conducted in a remote area of Colombian Llanos region during the period February 1 – 14, 2015.

5.2.3 Methodology

▪ Observations

PM10 measurement were conducted at Taluma (4.375250 N, 72.231306 W), a rural location in an experimental research center of CORPOICA in Puerto Lopez, Meta (Figure 5-1). This measurement site was selected because it does not have urban sources, which permits to measure the regional background concentrations and the regional enhancements caused by biomass burning events.

Measurement campaigns were divided in three parts. The first part of the campaign was done at the beginning of the dry season when there was little fire activity (December 5-18, 2014). The second part was done during the middle of the dry season when higher fire activity was expected (from January 17 – February 15, 2015). The third part was at the beginning of the dry season 2015-2016 (December 14-21, 2015).

We used one Harvard impactor (using Teflon filters) and a Beta attenuation sampler (MP101M, Environnement SA). The latter was only available during the period February 1- 14. Here, we made special focus on measurements from the MP101M sampler because its temporal resolution allows better comparison with the 3-hourly simulations.

Chapter 5. Understanding atmospheric transport of biomass burning emissions 147

Figure 5-1: Study area and measurement sites. Source (this thesis)

Meteorological sites in the region are very scarce. For validating our meteorological simulations we used information from two stations (Libertad and Yopal) operated by the National Meteorological, Hydrological and Environmental Institute (IDEAM) and 1 site (Fazenda) that we run at 5 km from Taluma site.

▪ Emission inventory

Emissions were calculated following the bottom-up approach of multiplying the emission factor of particulate matter, biomass loading, combustion efficiency and burned area, BA. Among these factors, burned area dominates the uncertainty in emission estimation at scales relevant to regional air quality modeling (Urbanski, Hao, & Nordgren, 2011). Thus, we used emission factors, biomass loading and combustion efficiency from available information in the literature and focused on developing a hybrid approach to estimate BA and its temporal disaggregation. Methodological steps of the biomass burning emission inventory are summarized in Figure 5-2.

148 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

The approach is based on the combination of Landsat images and MODIS products. This hybrid method takes advantage of the Landsat capabilities to detect small fires, MODIS reflectance product to detect burned areas in areas covered by clouds in Landsat images and MODIS active fire product to temporal disaggregate the emissions.

We identify BA on Landsat images using the Burned Area Mapping Software - BAMS (Bastarrika et al., 2014) (Figure 5-2, step i). This tool uses several spectral indexes and thresholds that are established based on visual delimitation of training polygons defined by the user, allowing the production of accurate burned area maps at local scale.

One of the problems when using Landsat for BA identification is that some areas are not observed due to cloud cover. In those areas, we estimated BA through the application of spectral indexes over MOD13Q1 product (Figure 5-2, step ii). Thresholds were defined based on calculated spectral indexes over burned polygons previously identified as burned in Landsat scenes. We used the MOD13Q1 product (MODIS/Terra Vegetation Indices 16- Day L3 Global) rather than other available MODIS reflectance products because as a composite of 16 days minimizes the effects of cloud cover. Moreover, the product has a resolution of 250 m, which permits the detection of smaller fires compared to the 500m products.

However, this implies that only bands 1 (Red) and 2 (NIR) are available, which limits the number of spectral indexes for burned area detection that can be applied.

The NDVI is an index itself and comes within the product at a resolution of 250 m. Blue band is also available in the product at 250 m resolution as the disaggregation of the originally measured 500m blue band reflectance. We used the NDVI and applied two more burned area identification indexes that can be calculated using the red and NIR bands: The Global Environment Monitoring Index (GEMI) (Pinty & Verstraete, 1992) and the Burned Area Index (Martin & Chuvieco, 1998).

Chapter 5. Understanding atmospheric transport of biomass burning emissions 149

Figure 5-2: Flowchart of biomass burning emission estimation. (i) Landsat burned area identification using BAMS tool (ii) Burned area identification based on the application of spectral indexes (iii) Landsat and MODIS burned area integration (iv) Active fires-based burned area (v) emission estimation and (vi) date attribution. (Source: this thesis).

Landsat and MODIS burned polygons required two steps for their integration (Figure 5-2, step ii). First, Landsat polygons were taken to MODIS time intervals. Then, polygons were selected based on Landsat image quality. The methodology applied kept only Landsat polygons were Landsat images were of good quality and kept MODIS polygons where no Landsat identification was possible due to the quality of the image. 150 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

We also used MODIS active fire detections from the MCD14ML product (Figure 5-2, step iv) as a mean of incorporating fires that could have been omitted by the BA algorithm applied over MOD13Q1 in areas with low Landsat quality. To avoid false detections, we used active fires with confidence level higher than 70. For each fire detection we made a buffer of 500 m (pixel size) which was assumed as the BA. Detections in multiple days over the same area were discarded since they may correspond to gas flares rather than biomass burning. We also excluded gas flare pixels identified by the NOOA global gas flaring database (Elvidge, Zhizhin, Baugh, Hsu, & Ghosh, 2016) (https://ngdc.noaa.gov/eog/viirs/download_global_flare.html.

Emission factors, biomass density and combustion efficiency depend on the type of vegetation that was burned. Due to the availability of information, we had to use different information sources for Colombian and Venezuelan Llanos. For Colombia, we implemented a more detailed approach using the most recent available land cover information Corine Land Cover 2005-2009 (Instituto de Hidrología y Estudios Ambientales, 2012). Land cover categories were aggregated into more general classes that better match the available information on biomass density, combustion efficiency and emission factors (Akagi et al., 2011; Etter, Sarmiento, & Romero, 2010). For Venezuela, we used MOD12 land cover product. Biomass density and combustion efficiency for the different savanna ecosystems in Colombia were taken from Etter et al., [2010]. For categories not included in that reference we used biomass density and combustion efficiency from other sources (Akagi et al., 2011; Brown, Gillespie, & Lugo, 1997; Hoelzemann, Schultz, Brasseur, Granier, & Simon, 2004). Emission factors were taken from Andreae and Merlet [2001] and Akagi et al. [2011]. Table S1 and S2 show the emission factors, biomass loading, and combustion efficiency used for each land cover category for Colombia and Venezuela, respectively.

Then, to assign a date to the burned polygons we used active fire detections a two-step process (Figure 5-2, step vi). First, we identified overlapping active fire detection and burned areas. Then, for the remaining areas, where no active fires were detected, we distributed the emissions based on the daily fraction of emissions as calculated from MCD14ML active fire detections (Giglio, Csiszar, & Justice, 2006) during the emission inventory period following the approach used by Mu et al., (2011) for disaggregating monthly GFED3 emissions into daily time step. Chapter 5. Understanding atmospheric transport of biomass burning emissions 151

▪ Meteorological simulations

Driving meteorology for Lagrangian simulations was obtained from the Advanced Research version of the WRF model (ARW, version 3.4.1.), a numerical weather prediction and atmospheric simulation system (Skamarock et al., 2008). Initial and boundary conditions were taken from the Global Forecast System (GFS) Final Analysis (FNL), produced by the National Centers for Environmental Prediction (NCEP), which has a horizontal grid spacing of 0.5° with 27 vertical levels every 6 h.

Additional outputs of WRF were configured to improve mass conservation and temporal representation of wind variation in Lagrangian simulations, including time-averaged, mass coupled horizontal and vertical velocities (Hegarty et al., 2013b; D V Mallia, Lin, Urbanski, Ehleringer, & Nehrkorn, 2015; Nehrkorn et al., 2010).

Simulations using 6 different physical parametrizations and domain resolutions were compared against surface observations of three meteorological stations located in Colombian Llanos (Figure 5-1) to determine the optimal settings. Results from 1-week simulation (February 2-9/2015 with 1 day of spin-up) were evaluated by calculating bias error (BE), gross error (GE), root mean square error (RMSE) and index of agreement (IOA) following Borge et al., (2008). WRF configurations tested and the range of mean bias (MB) for temperature, wind speed, wind direction and specific humidity are shown in Table 5-2. All simulations used the Unified Noah Land Surface Model and Kain-Fritsch cumulus parametrization (only applied to D1). Parametrizations from run 1 were chosen for this study due to the better performance observed in wind direction. Comparison of all statistical parameters are shown in Supplementary material.

152 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Table 5-2: WRF configurations tested and model evaluation for the period February 3 - 9, 2015.

Run Wrf_1 Wrf_2 Wrf_3 Wrf_4 Wrf_5 Wrf_6 8- 2 – Lin et al 2 – Lin et al 2 – Lin et al 2 – Lin et al 2 – Lin et al Microphysics Thompson 5 New Long wave 1- RRTM 1- RRTM 1- RRTM 1- RRTM 1- RRTM radiation Goddard 5 New Short wave 1 – Dudhia 1 – Dudhia 1 – Dudhia 1 – Dudhia 1 – Dudhia Goddard WRF radiation 11 - configurations 11 - Revised 11 - Revised 11 - Revised 11 - Revised Revised 5 - MYNN tested MM5 MM5 MM5 MM5 Surface layer MM5 Planetary Boundary 1 -YSU 1 -YSU 1 -YSU 1 -YSU 1 -YSU 5 - (MYNN) Layer Domain 2-domain 2-domain 3-domain 2-domain 3-domain 2-domain configuration (20,4 km) (20,4 km) (27,9,3 km) (20,4 km) (25,5,1 km) (20,4 km) MB Temperature (°C) [-0.86,0.44] [-0.15,1.14] [-0.66,0.52] [-0.81,0.95] [0.02,1.04] [-1.34,-0.52] MB Wind Model speed (m/s) [-0.69,1.74] [-0.75,1.66] [-0.26,2.08] [-0.26,1.82] [-0.35,2.58] [-0.38,1.71] evaluation MB Wind direction (°) [-6.18,4.82] [-1.02,32.55] [-34.12,0.53] [-45.6,-1.91] [-19.39,-14.21] [-23.99,6.84] MB Specific humidity (g/kg) [-3.1,-1.4] [-3,-0.96] [-3,-1.05] [-3.1,-1.48] [-2.8,-0.84] [-2.9,-1.42]

WRF was configured with three nested domains at 27, 9 and 3 km resolution (Figure 5-1). Coarse domain was centered at 5.564°N and 68.9°W, covering Colombia, Ecuador, Venezuela, parts of the Pacific Ocean and the Caribbean Sea. The innermost domain (D3) covered the whole Llanos region. Simulations covered the period January 31 – February 14, with 1 day of spin-up.

Comparison of the observed and simulated temperature, wind speed and wind directions for 1 week of the simulation period (February 5-11, 2015) are shown in Figure 5-3. The best performance of the model was observed at Fazenda station, which can be explained because of the flat terrain characteristic of the Llanos region. Yopal and Libertad are closer to Andes Mountains which makes the simulation more challenging. Wind speeds at foothills stations were overpredicted, especially at Yopal.

Chapter 5. Understanding atmospheric transport of biomass burning emissions 153

Figure 5-3: Simulated vs. Observed temperature, wind speed and wind directions. (Source: this thesis)

▪ Lagrangian simulations

We estimated the impact of biomass burning emissions on Llanos air quality using the Stochastic Time-Inverted Lagrangian transport (STILT) model (J. C. Lin et al., 2003) driven by WRF wind fields. STILT simulates the transport of air parcels backward in time influenced by mean (advective) and turbulent wind components. From these air parcels’ backward trajectories STILT calculates “footprints”, which represent the upstream influence of an upwind source located at xi,yi, prior time tm, in concentrations at the receptor site, located at xr, at the time tr: 푁푡표푡 푀푎푖푟 1 푓(푥푟, 푡푟|푥푖, 푦푗, 푡푚) = ∑ ∆푡푝,푖,푗,푘 (5-12) ℎ ⋅ 𝜌̅(푥푖, 푦푗, 푡푚) 푁푡표푡 푝=1

154 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin where Mair is the molecular weight of air, h is the atmospheric height in which surface fluxes are diluted (assumed as 50% of the boundary layer height), 𝜌̅ is the average air density below h, Ntot is the total number of particles, and ∆푡푝,푖,푗,푘 is the period of time a particle p spends at a volume element (∆푥, ∆푦, ℎ) and time tm.

We run STILT releasing 1000 air parcels every 3 hours for the period February 1-14, 2015. Air parcels were tracked backwards in time for 72 hours, with positions calculated every 2 minutes. For each simulation time (every 3-hours) hourly footprints were generated for each of the 72 hours of backward simulation.

We calculated the contributions of biomass burning emissions to PM levels using both, daily emissions (emissions occurring in a given day are uniformly distributed in each hour) and 3- hourly emissions. The diurnal cycle was taken from GOES fire detections in Colombian Llanos during the dry season 2014-2015 (December 2014 – March 2015).

Hourly footprints were multiplied by corresponding daily biomass burning emissions, previously gridded to the same footprint resolution. We used two footprint resolutions. First, we aggregated our emission inventory in cells of 0.25°  0.25°, calculated the contributions and compared it with the contributions resulting from using GFED inventory. Then, we aggregated our emission inventory in cells of 0.05° X 0.05° to observe changes in contributions when using higher spatial resolution.

We also applied an indicator of the contribution at the receptor of particles from fire emissions (CFER indicator), which has proven to be useful to explain PM10 enhancements of particulate matter caused by biomass burning in the Llanos region (Hernandez et al., 2019).CFER is based on footprints obtained from Lagrangian modelling and a proxy of fire emissions. Since the footprint value at a cell represents the sensitivity of the receptor to an emission occurring in that cell, then the footprint value multiplied by a proxy of emissions is an indicator of the contribution of those emissions to the concentration enhancement at the receptor. CFER uses the Fire Radiative Power as the proxy of emissions and is calculated as: Chapter 5. Understanding atmospheric transport of biomass burning emissions 155

푛 퐹 (5-13) 퐶퐹퐸푅 = ∑(푓푎 ∙ ∑ 퐹푅푃푏) 푎=1 푏=1 where fa is the footprint value at the cell a, FRPb is the Fire Radiative Power of the fire b within the cell a, F is the total number of fires within the cell a. The indicator was calculated for each PM10 sampling day and included the sum of FRP of fires detected with a confidence higher than 70% (as reported by MODIS MCD14ML product), occurring during the sampling day and two days prior (as trajectories go back in time by 72 h). The footprint and sum of FRP were calculated in cells of 0.1° X 0.1°.

5.2.4 Results

▪ Biomass burning emissions

Burned area in the Llanos region during the period January 17- February 15, 2015 was 628,828 ha, corresponding to a total TPM emissions of 39,204 ton. Colombian Llanos accounted for 54% of total burned area. Most of the fires were small (Figure 5-4). In Colombia, 3585 fires were under 100 ha, corresponding to 83% of burned polygons and 32% of the total burned area. Venezuela also had high number of fires under 100 ha (3151 fires corresponding to 86% of burned polygons and 39% of total burned area). For Venezuela most of the detections of fires of sizes between 50-100 ha were based on active fire detections.

In Colombia, 60.2% of TPM emissions corresponded to burned areas that did not have an overlapping active fire. In Venezuela, date attribution based on daily fraction of active fires was necessary for 47.2% of TPM emissions. Daily distribution of emissions indicates that temporal dynamics of emissions are different in both countries (Figure 5-5). Colombia’s highest emissions occurred during January 28, February 6 and February 17-18, days in which daily emissions were above 1000 ton. Venezuela’s higher emissions were in the period January 27-30, with a peak emission of 2281 ton on January 27.

156 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 5-4: Number of fires by size interval in Colombia (A) and Venezuela (C) and accumulated fraction of burned area and number of fires by size interval in Colombia (B) and Venezuela (D). (Source: this thesis)

Chapter 5. Understanding atmospheric transport of biomass burning emissions 157

Figure 5-5: Daily TPM emissions per country. True date corresponds to burned areas that were overlapped by active fires and assigned dates correspond to burned areas with no active fire detection that were attributed a date based on a daily fraction of active fires calculated using 3-day centered mean in 0.5° cells. (Source: this thesis).

Grasslands and savannas were the land cover where most of the fires and burned area occurred, representing 91% of total burned area. Forest in Colombia and woody savannas in Venezuela, although represented only 0.3% and 5.2% of total burned area, accounted for 7% and 23% of TPM emissions respectively, due to the biomass loading of these land cover categories. Fires in cropland areas were higher in Venezuela than in Colombia, but due to its low biomass loading (agricultural residues) did not represent a significant amount of emissions.

Total burned area and emissions estimated with the methodology presented in this work are higher than reported in the global inventory, GFED. Table 5-3 shows the burned area and emissions of the two inventories by country. In Colombia, HDEZ burned area and emissions are higher than GFED estimates, but for Venezuela HDEZ inventory detected less burned area. Figure 5-6 (a) shows the difference in burned area. Green cells show cells in which HDEZ burned area was lower than GFED area. Most of this occurred in the northern part of the Venezuelan Llanos region.

Total burned area in Colombia was 1.8 times higher than GFED burned area, and emissions were 2 times higher than GFED estimates. The ratio between burned areas is highly 158 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin variable. Some areas in Colombian high plains HDEZ burned area was more than 5 times GFED area.

Table 5-3: Burned area and emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED) emission inventories for the period January 17 – February 18, 2015

Inventory Colombia Venezuela Total

Burned area HDEZ (ha) 328,634 299,966 628,600

GFED (ha) 177,891 336,588 514,479

Emissions HDEZ (kg) 16,953 22,251 39,204

GFED (kg) 8,571 14,839 23,410

Figure 5-6 shows differences in TPM emissions between HDEZ and GFED inventories. In general, in Colombia HDEZ emissions were greater than GFED estimates. In Venezuelan Llanos, the southern part HDEZ emissions were higher than GFED, and some areas of northern Llanos had lower emissions due to differences in burned area. In northern part, there were some cells having higher emissions in areas having lower burned area, which can be explained by differences in land-cover associated factors.

Figure 5-6: Differences in (a) burned area and (b) emissions from the emission inventory developed in this work (HDEZ) and the Global Fire Emission Database (GFED). (Source: this thesis). (a) (b)

Chapter 5. Understanding atmospheric transport of biomass burning emissions 159

▪ PM10 simulations

Particulate matter measurements covered the period from February 1 – February 14, 2015. During the first four days, there were little fire emissions and PM10 concentrations were relatively low, with even some 3-hour periods having concentrations below the detection limit (4 µg/m³) of the equipment for that measurement interval. Then, between February 5 – 10 higher fire emissions occurred. PM10 concentrations showed an increase and range from around 20 µg/m³ to 40 µg/m³. Fire activity decreased from February 11-14, but the decreases of PM10 concentrations was only observed after noon of February 12.

During the period with higher fire activity, high wind speeds from the north east were observed, which resulted in long-range footprints with a narrower extent than footprints of February 1-4 and February 11-14. These periods had lower wind speeds and more variable wind direction.

Observed PM10 enhancement and fire emissions contribution to PM10 for the period of high fire emissions are shown in Figure 5-7. Observed PM10 enhancements refers to the difference between the observed PM10 concentration and the baseline concentration. The latter was defined as the average of concentrations measured at the beginning of the dry season when there was low fire activity and air masses had similar origin as during the biomass burning period. Fire emission contribution refers to the sum of the enhancements caused by biomass burning emissions.

When using daily emissions (emissions are equally distributed in the 24 hours) the model observed only small enhancements on February 7 (4.1 µg/m³), February 9 (8.5 µg/m³) and a higher enhancement on February 10 (17.6 µg/m³). However, when using 3-hourly emissions the model better resolved PM10 dynamics and there was 1 over predicted concentration on February 10. Model statistics confirmed the slightly better performance when using 3-hourly emissions (MB=-10.3 µg/m³ and RMSE=12.2 µg/m³ for daily emissions, MB=-9.9 µg/m³ and RMSE=12.0 µg/m³ for 3-hourly emissions). There was almost no difference between enhancements observed when using emissions aggregated in 0.25° X 0.25° cells compared to emissions aggregated in 0.05° X 0.05° cells. (MB=-10.3 µg/m³ and RSME=12.2 µg/m³ for the case with resolution of 0.25° and MB=-10.4 µg/m³ and RMSE=12.2 µg/m³ for 0.05° resolution). 160 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Figure 5-7: Observed PM10 enhancement and fire emissions contribution to PM10 (a) using daily and 3-hourly HDEZ emissions and (b) using emission aggregated at cells of 0.25° X 0.25° and 0.05° X 0.05°. Dotted line and gray rectangle represent the average concentration measured on February 2 a day with little fire activity in Colombia and which concentrations were similar to concentrations measured at the beginning of the dry season when there was little fire activity. (Source: this thesis)

Contributions simulated using WRF derived wind fields do not substantially differ from contributions simulated using GDAS (Figure 5-8). Significant differences occurred only in some specific days (February 9-11) and correspond to the influence of close fires that were not captured due to differences in the initial wind direction that lead to substantially different footprints near the receptor (see supplementary material). Simulations using WRF had lower mean bias (MB (WRF)= -8.6 µg/m³; MB (GDAS) = -9.6 µg/m³) but RMSE was higher when using WRF than when using GDAS (RMSE(WRF)= 11.0 µg/m³ and RMSE(GDAS)=10.7 µg/m³).

Chapter 5. Understanding atmospheric transport of biomass burning emissions 161

Figure 5-8: Comparison of contributions using WRF and GDAS. (Source: this thesis)

When implementing GFED emission inventory in the Lagrangian simulations very small enhancements were observed (MB=12.0 µg/m³ and RMSE=13.2 µg/m³). Small peaks were observed in Feburary 7, 9, 10 and 12. Considering that the performance of the emission inventory here developed was better for Colombian llanos and also that some of the footprint was outside Llanos region where no emissions were calculated we run a simulation using HDEZ emissions only for Colombian llanos and GFED emissions scaled by a factor 2 outside Llanos area. Results showed that this hybrid emission approach better resolved dynamics of some days, specially February 8 and 12. Model performance with this hybrid emission approach had lower RMSE and mean bias (MB=8.6 µg/m³ and RMSE=11.0 µg/m³).

Figure 5-9: Observed PM10 enhancement and fire emissions contribution to PM10 using (a) GFED vs HDEZ inventory and (b) HDEZ and GFED hybrid inventory (HDEZ emissions for Colombian Llanos and GFED multiplied by 2 outside Colombian Llanos)

162 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

As will be mentioned in the discussion section, the discrepancies observed in the simulated contributions could be caused by secondary aerosol formation not quantified in the modeling approach.

Figure 5-10 shows the average contribution to the observed enhancements against distance using both daily emissions and 3-hourly emissions. During February 5 – 8, 50% of the contribution came from Colombian Llanos. From February 9-11 there was a higher percentage of contribution from Colombian Llanos (~75%), and specifically for February 10 (the day with the highest enhancements), around 70% of the contribution within 200 km from the measurement site.

Figure 5-10: Average contribution to the observed enhancements against distance. Gray dotted line corresponds to the distance from Taluma to Venezuela boundary. (Source: this thesis).

5.2.5 Discussion

There are many factors that can contribute to the significant discrepancies observed between the simulated and measured PM10 enhancement. We explored the following factors: advection, height of the atmospheric column used for calculating the footprints, Chapter 5. Understanding atmospheric transport of biomass burning emissions 163 dispersion (turbulent parametrization), biomass burning emission inventory, temporal disaggregation of emissions, emissions from other sources and secondary aerosol formation. Each of those factors are discussed below.

In order to evaluate potential advection errors, we compared simulations driven by WRF and GDAS wind fields. Although the discrepancies could not be explained by differences in the driving meteorology, GDAS simulations better reproduced the dynamic of PM10 enhancements. This could be related with the fact that GDAS is a product that includes global data assimilation and conserves mass at a global scale and our WRF simulations did not include data assimilation. The over prediction of wind speeds found in the foothills do not have much impact on simulated concentrations. Most of the winds that transported air masses to the measurement site came from north east, where flat terrain is predominant and modeled wind speed and direction show good agreement with observations.

Although previous research had reported that footprints are insensitive to the value assumed as the column height “h” if h is within the interval 10%-100% of the PBL (Gerbig et al., 2003; J. C. Lin et al., 2003), we run simulations changing that parameter from 0.5 to 0.8 and compared the percentage of particles within the mixing height for each case and found similar results for both h. We also compared the contributions calculated for both h values and found an average difference of 0.5 µg/m³ for February 7, 2015.

Other potential source of the discrepancies could be the performance of model parametrizations for describing the turbulence in the real atmosphere. However, STILT has been evaluated against tracers and compared to other Lagrangian models confirming its good performance and comparability with HYSPLIT and FLEXPART (Hegarty et al., 2013a). Given that the rate of turbulent diffusion depends on the intensity of turbulence (Rakesh, Venkatesan, & Srinivas, 2013) and that the measured turbulent intensity in the area is close to values measured in mid-latitudes where the model has proven to have good performance, it can be concluded that it is unlikely that the discrepancies are caused by turbulence parametrization.

Underestimation of emission could be another source of discrepancies. Although our tailor- made inventory better reproduced the observed enhancements, the inventory has its own limitations. For example, there was underestimation for the northern part of Venezuelan 164 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin caused the fact that Landsat scenes over that area had bad quality and could not be used. Also, MODIS had quality problems, resulting in burned area estimations relying only on active fire detections. In our inventory Venezuelan Llanos burned area accounted for 48% of total burned area while in GFED represents 65% of total burned area. The spatial variability in the differences between emission inventories caused by the no detection of small fires makes global inventories difficult to scale.

The high percentage of emissions without overlapping active fires demonstrated that active fire detections under-represent fire emissions in the Llanos area and adds high uncertainty when using the emission inventory for atmospheric modelling. The higher percentage of burned polygons with a true date in Venezuela (overlapping burned area and active fire detections) is because Venezuela had a higher number of burned polygons that were based on active fire detections, especially in northeast llanos.

The second most important PM10 source after biomass burning is oil production activities. This is a year-round source that is expected to have a similar impact our baseline concentration than concentrations during the period of biomass burning. Our previous estimations of PM10 emissions from energy generation in oil production activities in Colombian Llanos gives 147 ton/month (Hernandez et al., 2019). According to data from a global gas flaring emission database (Huang & Fu, 2016), the emission of Black Carbon from gas flaring in the whole Llanos region is 85 ton/month, which corresponds to PM10 emissions of around 461 ton/month. Considering those values, during the biomass burning period, emissions from oil extraction only represent around 14% of biomass burning emissions and do not explain the discrepancies between observed and simulated enhancements.

Also, dust reentrainment is not considered. Recent research has shown that fire can induce wind erosion by two mechanisms. First, by the fire-driven turbulence generated during the fires (Wagner, Jähn, & Schepanski, 2018) and second, by wind erosion of soils burned by fires (Wagenbrenner, 2017). Wind erosion occurs when wind speeds exceeds a threshold of around 6-7 m/s, values that are not typically registered in the area. However, when a fire occurs the heated air in the near surface layer begins to raise and as a result, surrounding air flows to replace the rising air. The accelerated horizontal winds and atmospheric Chapter 5. Understanding atmospheric transport of biomass burning emissions 165 turbulence induced by the fire over a terrain whose vegetation have been partly removed may exceed the threshold for dust emission (Wagner et al., 2018).

Another source of the observed discrepancies is that secondary particle formation was not considered in the modeling approach and may represent a significant fraction of measured PM. A recent study based on 5.5 year of continuous measurements over savanna and grasslands fire plumes showed that after only 3 hours of daytime plume ageing the mass of PM1 more than doubles primary PM1 load (average PM1 enhancement factor of 2.1) (Vakkari et al., 2018). They also discussed that a fraction of the underestimation typically observed when implementing biomass burning inventories in air quality simulations, may be caused by the lack of subgrid scale secondary aerosol formation, and that the scaling methods that have been applied may overestimate components such as black carbon.

For each simulated contribution we calculated the distance to which 50% of the contribution (r50) is produced and plot the ratio between observed PM10 enhancement and simulated contribution (Figure 5-11). It can be observed that the greater the distance r50 (contributions coming from longer distances, implying longer ageing processes) the largest the difference between the observed and predicted enhancements which reinforces the hypothesis of high influence of secondary aerosols.

Figure 5-11: Difference between observed PM10 enhancement and simulated contribution divided by the simulated contribution versus d50 (distance to which 50% of the contribution is produced). (Source: this thesis).

166 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

Furthermore, the influence of BB emissions to PM10 levels was confirmed by the good correlation observed between PM10 daily data with the CFER indicator (r2=0.43). Since this indicator uses a proxy of fire emission rather than a detailed emission inventory (Figure 5-12), the contribution of secondary particles is embedded within the proxy.

Figure 5-12: Correlation of PM10 with the indicator of the contribution at the receptor of particles from fire emissions. (Source: this thesis)

There are two potential sources of overestimation in the simulations: (1) wet and dry deposition were not considered and (2) plume rise was not considered.

Biomass burning particles are mainly within the accumulation mode, with very low sedimentation velocities (3.5 E-5 m/s for particles of 1 µm with density=1), thus, not considering dry deposition is not considered to be a major cause of overestimation. The simulation period was a dry week, so not considering wet deposition is also ruled out as a source of overestimation.

Recently, Derek V. Mallia, Kochanski, Urbanski, & Lin, (2018) implemented plume rise in STILT. Derek Mallia, (2018) demonstrated that for long-range transport (plumes that have been in the atmosphere for more than 18 hours; fires at distances greater than ~200 km) was largely insensitive to the incorporation of a plume rise model because convective mixing evenly distributes the emission throughout the lower troposphere. For our case, most of the simulated days have most of the contribution from distances greater than 200 km, except Chapter 5. Understanding atmospheric transport of biomass burning emissions 167 for February 10, where ~75% of the contributions came from the first 200km. Only for that case, the lack of plume rise parametrization may have caused overestimation. That day was the only case were simulated concentration was higher than observed. For the rest of the simulation period, the lack of plume rise parametrization is not expected to significantly overestimate the simulated enhancements.

5.2.6 Conclusions

We developed a highly detailed biomass burning inventory for the Llanos region combining Landsat burned area detection, burned area identification by spectral indexes applied over MODIS composites and active fire data. Our inventory demonstrated that, for the period here analyzed, fires smaller than 100 ha accounted for 83% and 86% of total burned polygons in Colombia and Venezuela, respectively. Our methodology performed better for Colombian llanos due to cloud cover in Venezuela for the period of analysis. Our estimates of burned area in Colombia are 1.8 times the estimates of GFED, one of the most widely used global fire emissions database. There was high variability in the cell to cell ratio of burned area between emission inventories showed that global emissions products are difficult to scale.

Most of the burned area did not have an overlapping active fire detection. This adds high uncertainty to daily emissions when used for atmospheric modelling and demonstrates that thermal anomalies products underestimate fire activity in the Llanos region.

Simulations using our emission inventory better reproduced the observed PM10 enhancements than GFED emissions. However, a high fraction of the enhancement could not be explained by the transport of primary emissions. Discrepancies between simulated and observed enhancements were not caused by wind fields as demonstrated by the comparison between simulations driven by WRF simulations and GDAS. Rather, the observed differences may be attributed to secondary particle formation not considered by the modeling approach. Further work should account for secondary aerosol formation.

The indicator of the contribution at the receptor of particles from fire emissions (CFER) showed good correlation with the average PM10 daily concentration confirming the impact of biomass burning emission on the enhancements observed during the period of high 168 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin biomass burning with respect to concentrations measured at the beginning of the dry season.

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6. Conclusions and perspectives

6.1 Conclusions

This thesis contributes to the understanding of the impact of biomass burning on air quality of Colombian Orinoco River basin, addressing a pollution problem not previously identified in Colombia. To establish the contribution of savanna burning emissions to air quality levels, three components were developed and integrated: (1) air quality measurements; (2) an emission inventory, and (3) simulations of atmospheric transport and dispersion.

In the first component, measurements were conducted in 2 different settings: (1) in sites not impacted by urban sources (a suburban location and a rather remote rural location) and (2) simultaneously in 2 urban locations. The second component included the development of a satellite information-based scheme for detecting, validating and incorporating small fires as sources of biomass burning. The third component used Lagrangian dispersion modeling to study the transport of the estimated emissions and their impact on observed PM10 concentrations.

Upon this methodology, this research allows concluding the following:

• Aerosol Optical Depth (AOD), as measured by the AERONET sun photometer installed at Puerto Gaitan, Meta, revealed that fine mode aerosol content in the total atmospheric column are significantly increased during the biomass burning season. The second largest source of fine particles in the region is oil extraction activities but considering that it is a year-round activity whose emissions are 1/10 of biomass burning emissions during the dry season, the increase in fine mode AOD is likely caused by biomass burning. Furthermore, cluster analysis of the aerosol size distributions showed a large increase of the fine mode during BB periods. 174 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

• PM10 also increases during the biomass burning period as compared to the beginning of the dry season, and the magnitude of the observed enhancements are covariant with fire activity intensity. During the intense biomass burning period in Colombia, PM10 concentrations were increased by 26 µg/m³ and 21 µg/m at Taluma (Puerto Lopez, remote site) and Libertad (Villavicencio’s outskirts, suburban site) sites, respectively.

• The levels of PM10 and BC reached during the biomass burning period in the Llanos region are similar of even higher to the levels observed in an urban area like Bogota, where multiple industrial and mobile sources exist. PM10 enhancements were closely related enhancements of biomass burning tracers (BC, K+) and dust tracers. Exploratory source apportionment techniques demonstrated that around 80% of the observed PM10 enhancements are due to biomass burning emissions and 20% are caused by soil dust.

• Biomass burning emissions from Venezuela significantly deteriorated air quality in Arauca and Yopal, and potentially other cities downwind, during periods where the rainy season has already started in Colombia but there is still high fire activity in Venezuela. It was demonstrated that Venezuelan fires caused an air pollution episode with PM10 concentrations in some sites above daily standards and regionally high AOD. Also, biomass burning increased ozone concentration by a factor 2 respect to ozone measurements conducted during a period not affected by biomass burning.

• Yopal and Arauca have a very similar average daily baseline PM10 concentration of ~35 µg/m3. This baseline is not a background concentration but the result of local and regional emissions other than BB. It was found that BB can episodically add up to ~100 µg/m3 to this baseline level.

• Even for the cases of moderate biomass burning, the relatively low enhancements may have caused significant health effects due to the toxicity inherent of biomass burning emitted particles. The literature indicates that ambient air organic PM in ambient air impacted by biomass burning have genotoxic effects even for PM10 Conclusions 175

levels under the WHO guidelines, with enhancements of particulate matter similar to the ones measured at Taluma during the moderate biomass burning period.

• I developed an indicator of the contribution at the receptor of particles from fire emissions (CFER), which is a useful tool that uses FRP from MODIS active fire detection as a proxy of fire emissions and the footprint generated from backward Lagrangian modeling to estimate the influence of fire emissions on the daily average PM10 concentration at receptors. This indicator performed well for PM10 observations at Yopal, Arauca and Taluma and has potential to be an operational tool if combined with real-time MODIS fire detection and Lagrangian simulations.

• The emission inventory showed that there is a predominance of small fires that are not well captured by global emission products. Even when small fires are identified using higher resolution satellite products like Landsat (30 m resolution), attributing date to burned polygons remains a challenge since around 60% of the polygons did not have an overlapping active fire. This uncertainty in the temporal distribution of emissions adds uncertainty to air quality simulations.

• Simulated contributions of biomass burning emissions to the PM10 levels were one third smaller than enhancements observed during the biomass burning period. However, simulations results suggest that those differences are related to secondary aerosol formations not included within the modeling framework. The use of CFER confirmed the impact of biomass burning and the footprints from backward modeling showed the area contributing to the enhancements at the receptor.

6.2 Perspectives

• Bearing in mind the very difficult problems associated with the control of transboundary pollution (an environmental “diplomacy” problem), and with the management of frequent fires in Colombian Llanos, it is required that local environmental authorities implement a regional pollution forecast system able to detect pollution sources, foresee episodes, alert the population and implement measures to reduce their exposure. 176 Assessment of the impact of biomass burning on air quality in the Colombian Orinoco River Basin

• There is a growing concern about the impact of the Llanos biomass burning emissions on the air quality in the Andean region, particularly in cities like Bogota, Medellin and Bucaramanga. Mendez-Espinosa, Belalcazar, & Betancourt, (2019) found that PM10 concentrations at those Andean cities were correlated with the time series of number of fires and that fires explained ~11% of seasonal variations of CO concentrations. PM emitted by biomass burning should be observable as AOD from satellite or surface measurements. If convection (during the emission or during transport) put those emissions within the mixing layer, it is reasonably to propose as working hypothesis that those emissions will impact Orinoco cities and will not likely reach Andes cities. Otherwise, if those emissions are injected at a higher altitude, they would not likely impact the ORIB surface and will probably impact Andes cities. This suggest that a monitoring system using AOD observations (e.g. sun photometry) combined with particulate matter monitoring in the Llanos could be used to measure impacts in the Llanos region and forecast potential impacts in the Andes region.

• Chemical composition of sources (biomass burning emissions, soil dust) should be measured to establish with a high level of certainty the contribution of each emission source to particulate matter levels.

• Organic compounds of ambient air particulate matter should be characterized to obtain information on toxic compounds (p.e. Polycyclic Aromatic Hydrocarbons (PAHs) and to include them in source apportionment modeling using source-specific tracers such us Levoglucosan.

• Epidemiological studies are required to evaluate the health impacts of biomass burning as a regional pollution source.

A. Appendix: Climatological means of temperature, precipitation rate and wind speed in the Llanos region

Figure A 1: Climatological mean (1981-2010) of surface air temperature (°C) for 3-month periods (a) Dec – Feb (b) Mar – May (c) Jun -Aug and (d) Sep-Nov. Source: NCEP/NCAR Reanalysis (A) (B)

(C) (D)

178 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

Figure A 2: Climatological mean (1981-2010) of precipitation rate (mm/day) for 3-month periods (a) Dec – Feb (b) Mar – May (c) Jun -Aug and (d) Sep-Nov. Source: NCEP/NCAR Reanalysis (a) (b)

(c) (d)

Appendix A. Climatological means of temperature, wind speed and rain in the 179 Llanos region

Figure A 3: Climatological mean (1981-2010) of wind speed (m/s) for 3-month periods (a) Dec – Feb (b) Mar – May (c) Jun -Aug and (d) Sep-Nov. Source: NCEP/NCAR Reanalysis (a) (b)

(c) (d)

B. Appendix: Supplementary material: Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Basin

1. Location of measurement sites Figure B 1: Detailed location of measurement sites.

182 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

2. First order estimation of oil production and agriculture emissions

Most of the particulate matter emissions from oil production in the Llanos region come from energy generation, mostly with diesel engines. According to the Colombian national inventory of energy production (UPME, 2014), energy auto-generation installed capacity in the Llanos region was 477.5 MW on 2014. We used an emission factor of 4.2510-4 kg PM10/kWh (large stationary diesel engines from AP42, section 3.4.). To calculate monthly emissions, we assumed energy production using the installed capacity, for 24 hours/day and 30 days/month, which gives an energy production of 3.44108 kWh. Estimated monthly PM10 emissions are thus, 푘푔 3.438 ⋅ 108 푘푊ℎ ∙ 4.256 ⋅ 10−4 = 146,321 푘푔 = 146.3 푡표푛푠 푘푊ℎ

A detailed estimation of the agriculture PM10 emissions requires enumeration of agricultural activities, estimation and addition of their emission factors for each crop, and then multiplication by their activity factors, i.e. crop areas. Here, we make a first order estimation from a detailed estimation of the effective (year-long) emission factor (EF) for dry grain production (basically rice, corn and soybean) in the Colombian ORIB. This EF, 8.74 kg PM10 ha-1 calendar year-1 (Tatis Bautista et al., 2015), is expected to overestimate the overall EF for agriculture in the Colombian ORIB, as permanent crops (basically oil palm, cacao and rubber) EFs (averaged over the permanent crop life cycle) are much smaller than the temporary (grain) EFs. Agricultural PM10 emissions are thus reckoned at 푘푔 푃푀10 759,444 ℎ푎 ∙ 8.74 = 6,637,540 푘푔/푦푒푎푟 ℎ푎 ⋅ 푦푒푎푟

3. Mean backward trajectories

Time series disengage from 9th May 2015 on. In order to better understand this, we calculated mean trajectories with HYSPLIT (Stein et al., 2015). These show a significantly different origin for air masses arriving at Yopal and Arauca during some of the decoupled period days. For instance, on May 9 and May 15 (see Case 1 below), Yopal was influenced by Southeast (Amazonian) air masses, while Arauca kept receiving air masses from the Llanos. Other days, air masses arriving at Arauca came from the North (Maracaibo Lake Appendix B. Supplementary material Transboundary Pollution 183 region) and within the mixing layer, while Yopal received air masses from the Llanos but above the mixing layer (see Cases 2-4 below).

Figure B 2: Mean backward trajectories during the decoupled period Case 1: Different air Case 2: Trajectories from Case 3 – Example 1: masses origin for Arauca outside the mixing layer Trajectories for Arauca and Yopal from low levels and Trajectories for Arauca from > 1500 m Arauca

Yopal

184 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

Case 3 – Example 2: Trajectories for Case 4: Coupled period Arauca from low levels and Trajectories for Arauca from > 1500 m Arauca

Yopal

Appendix B. Supplementary material Transboundary Pollution 185

4. Precipitation during the measurement campaign Figure B 3: Precipitation during the measurement campaign.

5. Data from TC4 campaign over the Llanos foothills

The NASA’s Tropical Composition, Clouds and Climate Coupling (TC4) campaign (https://espo.nasa.gov/tc4) covered the Llanos foothills, specifically during the 21st July 2007 mission. The O3 average concentration used as a reference corresponds to the fraction of the flight from Villavicencio almost to Yopal with an altitude between 353 and 1000 m (Avery et al., 2010). Figure B 4: Data from 21st July TC4 NASA campaign over the Llanos foothills. A) Path and altitude of the flight used for obtaining the average concentration. B) Profile of O3 concentration in Llanos foothills

(a)

(b)

186 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

6. Synoptic scale transport of aerosols from biomass burning (BB) emissions in the Brazilian Amazonia and Cerrado 7. Figure B 5: Biomass burning emissions transported from Brazil to Colombia on September 16, 2015. A) Carbon monoxide from Aqua/AIRS; B) Aerosol Optical Depth.

8. MODIS AOT and Angstrom Exponent averaging area.

Figure B 6: MODIS AOT and Angstrom Exponent averaging area.

C. Appendix: Supplementary material: Understanding the atmospheric composition impact of biomass burning in northern South American savannas through Lagrangian simulations and a tailor- made emission inventory

1. Biomass loading, combustion efficiency and emission factors Table C 1: Corine land cover categories and their associated biomass loading, combustion efficiency and emission factors

Emission Biomass Source Source factor Source Corine land cover Combustion Category loading biomass combustion (g/kg emission category efficiency (ton/ha) loading efficiency biomass factor burned) Andreae y Etter et al., Overflow Merlet, 2001 Natural dense overflow Etter et al., 2010 - Alluvial plain 7 0.7 8.3 TPM - grassland 2010 overflow plain savannas Savanna and savannas Grassland Rice fields Andreae y Cropland Akagi et al., Akagi et al., Merlet, 2001 Cropland/natural Crop 1.4 2011 - 0.8 2011 - Cereal 13 TPM- vegetation mosaic residue Cereal crop crop Agricultural Cropland/natural residues vegetation mosaic Natural dense Andreae y grassland Flat Merlet, 2001 Etter et al., Etter et al., Sparsely vegetated highplain 6 0.85 8.3 TPM - 2010 2010 areas savannas Savanna and Burned areas Grassland Gallery Hoelzemann et Akagi et al., Gallery and riparian and Brown et. al al., 2004 2011 TPM- 217 0.5 13 forests riparian (1997) (Tropical Tropical forest forest) forest Pastures/natural Andreae y vegetation mosaic Hoelzemann et Merletl, 2001 Pastures/crops mosaic Etter et al., al., 2004 - Pastures 8 0.85 8.3 TPM - Pastures with trees 2010 Savanna and Savanna and and shrubs grasslands Grassland Pastures 188 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

Emission Biomass Source Source factor Source Corine land cover Combustion Category loading biomass combustion (g/kg emission category efficiency (ton/ha) loading efficiency biomass factor burned) Sparsely vegetated áreas Andreae y Merlet, 2001 Open grassland on Sandy Etter et al., Etter et al., 3.5 0.95 8.3 TPM - sands savanna 2010 2010 Savanna and Sparse vegetation on Grassland sands Hoelzemann et al., 2004 - Hoelzemann et Akagi et al., Tropical Tropical al., 2004 2011 TPM- Dense forest 255.1 0.5 13 forest forest of (Tropical Tropical South forest) forest America Open shrublands Akagi et al., Woody Hoelzemann Hoelzemann et Fragmented forest 30.74 0.6 15.4 2011 TPM - savanna et al., 2004 al., 2004 Wetlands Chaparral

Table C 2 Modis land cover categories and their associated biomass loading, combustion efficiency and emission factors

Emission Biomass Reference Reference factor Reference Combustion Modis category Category loading (biomass (Combustion (g/kg (Emission efficiency (ton/ha) loading) efficiency) biomass factor) burned) Evergreen Hoelzemann Hoelzemann et Broadleaf forest et al., 2004 - FINN and al., 2004 - Tropical Tropical Akagi et. al Deciduous 255.1 0.5 Tropical forest 13 forest forest of (2011) Broadleaf forest of South South Tropical forest America Mixed forest America Closed shrublands Open shrublands Akagi et al., Woody Hoelzemann Hoelzemann et Woody savannas 30.74 0.6 15.4 2011 TPM - savanna et al., 2004 al., 2004 Permanent Chaparral wetlands Etter et al., Etter et. al 2010 - (2011) - FINN and Alluvial Savannas Savannas 7 0.7 Alluvial 8.3 Andreae and overflow overflow plain Merlet (2001) plain savannas savannas Akagi et. al Etter et al., Etter et. al (2011) Grasslands Pastures 8 0.85 8.3 2010 (2011) Pasture mainteinance Croplands FINN (Cropland) Akagi et al., Crop Akagi et. al and Andreae Cropland/Natural 1.4 2011 - 0.8 13 residue (2011) and Merlet vegetation mosaic Cereal crop (2001) Crop residue

Appendix C. Supplementary material Understanding the atmospheric 189 composition impact of biomass burning in northern South American savannas

2. Evaluation of WRF simulations Table C 3: Evaluation of WRF simulations

Variable Site id_file WRF1 WRF2 WRF3 WRF4 WRF5 WRF6 Fazenda MB (°C) 0.44 1.14 0.52 0.95 1.04 -0.64 MGE (°C) 1.02 1.37 1.31 1.38 1.35 1.17 IOA 0.86 0.81 0.82 0.81 0.81 0.84 Yopal MB (°C) -0.86 -0.15 -0.66 -0.81 0.02 -1.34 Temperature MGE (°C) 1.09 0.96 1.03 1.01 1.16 1.50 IOA 0.80 0.82 0.81 0.81 0.79 0.72 Libertad MB (°C) -0.10 0.16 0.36 0.49 0.52 -0.52 MGE (°C) 1.21 1.42 1.31 1.43 1.35 1.26 IOA 0.81 0.77 0.79 0.77 0.78 0.80 Fazenda MB (m/s) -0.69 -0.75 -0.26 -0.26 -0.35 -0.38 RMSE (m/s) 1.36 1.61 1.11 1.29 1.33 1.23 IOA 0.70 0.67 0.76 0.73 0.72 0.73 Yopal MB (m/s) 1.74 1.66 2.08 1.82 2.58 1.71 Wind speed RMSE (m/s) 2.27 2.22 2.49 2.36 2.92 2.31 IOA 0.31 0.32 0.21 0.28 0.03 0.31 Libertad MB (m/s) 1.14 1.10 1.08 1.38 1.08 1.09 RMSE (m/s) 1.46 1.33 1.36 1.62 1.40 1.43 IOA -0.32 -0.26 -0.26 -0.40 -0.26 -0.28 Fazenda MB (g/kg) -1.40 -0.96 -1.05 -1.48 -0.84 -1.42 MGE (g/kg) 1.42 1.10 1.20 1.49 1.03 1.45 IOA 0.37 0.51 0.47 0.34 0.54 0.36 Yopal MB (g/kg) -1.70 -2.00 -1.60 -1.60 -2.00 -1.80 Specific MGE (g/kg) 1.70 2.00 1.70 1.60 2.00 1.90 Humidity IOA -0.21 -0.32 -0.18 -0.15 -0.31 -0.27 Libertad MB (g/kg) -3.10 -3.00 -3.00 -3.10 -2.80 -2.90 MGE (g/kg) 3.10 3.00 3.00 3.10 2.80 3.00 IOA -0.59 -0.58 -0.58 -0.59 -0.55 -0.57 Fazenda MB (°) -6.18 -1.02 0.53 -19.35 -19.39 -1.80 MGE (°) 63.71 69.81 55.83 64.21 60.06 65.11 Wind Yopal MB (°) 4.82 32.55 -34.12 -45.60 -14.21 -23.99 Direction MGE (°) 80.37 74.61 95.57 111.96 80.21 86.75 Libertad MB (°) 3.02 3.61 -3.75 -1.91 -15.03 6.84 MGE (°) 93.32 89.38 91.49 90.20 99.98 86.12

(MB) Mean bias (IOA) Index of agreement (RMSE) Root mean square error (MGE) Mean gross error

190 Assessment of the impact on air quality of biomass burning in the Colombian Orinoco river Basin region

3. Base concentration Figure C 1: Back trajectories for the period of the measurements used as base concentration

Appendix C. Supplementary material Understanding the atmospheric 191 composition impact of biomass burning in northern South American savannas

4. Analysis of contribution differences when using WRF vs GDAS

D. Picture gallery