Mostrar el registro sencillo del ítem

dc.contributor.advisorSchneider, Ismael Luisspa
dc.contributor.advisorCano Cuadro, Heidis Patriciaspa
dc.contributor.authorBolaño Truyol, Jehison Rafaelspa
dc.date.accessioned2020-09-08T23:30:23Z
dc.date.available2020-09-08T23:30:23Z
dc.date.issued2020
dc.identifier.citationBolaño, J. (2020). Determinación del aporte de quemas de biomasa en la concentración de pm2.5 en dos municipios del área metropolitana de barranquilla a través del uso de herramientas de sensoramiento remoto. Trabajo de Maestría. Recuperado de https://hdl.handle.net/11323/7078spa
dc.identifier.urihttps://hdl.handle.net/11323/7078spa
dc.description.abstractThe disruption of air quality due to the increase in atmospheric emissions, especially due to the burning of biomass, constitutes one of the greatest environmental concerns worldwide. In this study, through the use of remote sensing tools and dispersion models, the contributions of the burns in the alterations of PM2.5 in two municipalities of the Barranquilla Metropolitan Area were estimated. Initially, the variations of PM2.5 between January 2017 and June 2018 were analyzed and validated for the municipalities of Soledad (Hipódromo and EDUMAS stations) and Malambo (Tránsito y Transporte station). Subsequently, using the parameters AOD and AAE, the aircraft are classified according to their origin. The biomass burning report is estimated for the period between February 24 and March 30, 2018, when the main burning periods are observed. The burn points and their intensity were obtained from satellite images and the Hysplit model used to estimate emissions. From the dispersion model, which used forward trajectories, it obtained the burns that contribute, on average, with 26.93% for EDUMAS and 22.82% at Hipódromo (Soledad), while for Transit and Transportation with 28.78% (Malambo) of PM2.5 proteins. These results indicate a significant contribution of regional burns, with the contributions coming from La Guajira being recorded. This information is essential so that they can implement more effective mitigation measures and lessen the impact on the population's health.spa
dc.description.abstractEl deterioro de la calidad de aire por el aumento de emisiones atmosféricas, en especial por las quemas de biomasa, constituye una de las mayores preocupaciones ambientales a nivel mundial. En este estudio, mediante el uso de herramientas de sensoramiento remoto y modelos de dispersión, se estimaron los aportes de las quemas en las concentraciones de PM2.5 en dos muncipios del Área Metropolitana de Barranquilla. Inicialmente se analizó y validó las concentraciones de PM2.5 entre enero 2017 a junio 2018 para los municipios de Soledad (estaciones Hipódromo y EDUMAS) y Malambo (estación Tránsito y Transporte). Posteriormente, empleando los parámetros AOD y AAE, los aerosoles se clasificaron según su origen. El aporte de las quemas de biomasa se estimó para el período entre 24 de febrero y 30 de marzo de 2018, cuando se presentaron los principales períodos de quema. Los puntos de quema y su intensidad se obtuvieron a partir de imágenes satelitales y el modelo Hysplit utilizado para estimar las emisiones. A partir del modelo de dispersión, que empleó trayectórias forward, se obtuvo que las quemas aportan, en promedio, con 26,93% para EDUMAS y 22,82% en Hipódromo (Soledad), mientras que para Tránsito y Transporte con 28,78% (Malambo) de las concentraciones de PM2.5. Esos resultados indican un aporte significativo de quemas regionales, siendo registradas contribuciones que vienen desde La Guajira. Esas informaciones son fundamentales para que se puedan implementar medidas de mitigación más efectivas y disminuir el impacto sobre la salud de la población.spa
dc.language.isospa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subjectBiomass burningspa
dc.subjectParticulate matterspa
dc.subjectRemote sensingspa
dc.subjectDispersion model,spa
dc.subjectHysplitspa
dc.subjectQuemas de biomasaspa
dc.subjectMaterial particuladospa
dc.subjectSensoramiento remotospa
dc.subjectModelo de dispersiónspa
dc.titleDeterminación del aporte de quemas de biomasa en la concentración de pm2.5 en dos municipios del área metropolitana de Barranquilla a través del uso de herramientas de sensoramiento remotospa
dc.typeTrabajo de grado - Maestríaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.programMaestría de Investigación en Desarrollo Sostenible Midesspa
dc.relation.referencesAbdelhady, S., Borello, D., Shaban, A., & Rispoli, F. (2014). Viability study of biomass power plant fired with rice straw in Egypt. Energy Procedia, 61, 211–215. https://doi.org/10.1016/j.egypro.2014.11.1072spa
dc.relation.referencesAdamiec, E., Dajda, J., Gruszecka-Kosowska, A., Helios-Rybicka, E., Kisiel-Dorohinicki, M., Klimek, R., Pałka, D., & Wąs, J. (2019). Using Medium-Cost Sensors to Estimate Air Quality in Remote Locations. Case Study of Niedzica, Southern Poland. Atmosphere, 10(7), 393. https://doi.org/10.3390/atmos10070393spa
dc.relation.referencesAkagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., & Wennberg, P. O. (2011). Emission factors for open and domestic biomass burning for use in atmospheric models. Atmospheric Chemistry and Physics, 11(9), 4039–4072. https://doi.org/10.5194/acp-11-4039-2011spa
dc.relation.referencesAlbar, I., Amissah, L., Bowman, D., Charlton, V., Cochrane, M., Groot, B. De, Ellison, D., Field, R., Flannigan, M., Goldammer, J., Kant, P., Khan, M. N., Kornexl, W. L., Krasovskii, A., McCaffrey, S., Mitchell, A. M., Mohanty, A. K., Moore, P., Murdiyarso, D., … Vikram, E. (2018). Global Fire Challenges in a warming world: Summary Note of a Global Expert Workshop on Fire and Climate Change. Occasional Paper No. 32, 32(January), 61.spa
dc.relation.referencesAlcaldía de Barranquilla. (2013). Gaceta distrital N° 390-6. Gaceta Distrial, 429, 100.spa
dc.relation.referencesAldeghi, Carn, Escobar-Wolf, & Groppelli. (2019). Volcano Monitoring from Space Using High-Cadence Planet CubeSat Images Applied to Fuego Volcano, Guatemala. Remote Sensing, 11(18), 2151. https://doi.org/10.3390/rs11182151spa
dc.relation.referencesAMB. (2005). Area Metropolitana De Barranquilla Plan De Gestion Integral De Diagnóstico Capitulo V . Diagnostico Institucional: Vol. I.spa
dc.relation.referencesArmenteras-Pascual, D., Retana-Alumbreros, J., Molowny-Horas, R., Roman-Cuesta, R. M., Gonzalez-Alonso, F., & Morales-Rivas, M. (2011). Characterising fire spatial pattern interactions with climate and vegetation in Colombia. Agricultural and Forest Meteorology, 151(3), 279–289. https://doi.org/10.1016/j.agrformet.2010.11.002spa
dc.relation.referencesAsif, Z., Chen, Z., & Han, Y. (2018). Air quality modeling for effective environmental management in the mining region. Journal of the Air and Waste Management Association, 68(9), 1001–1014. https://doi.org/10.1080/10962247.2018.1463301spa
dc.relation.referencesBae, M., Kim, B. U., Kim, H. C., & Kim, S. (2020). A multiscale tiered approach to quantify contributions: A case study of PM2.5 in South Korea during 2010-2017. Atmosphere, 11(2), 5–15. https://doi.org/10.3390/atmos11020141spa
dc.relation.referencesBallesteros-González, K., Sullivan, A. P., & Morales-Betancourt, R. (2020). Estimating the air quality and health impacts of biomass burning in northern South America using a chemical transport model. Science of The Total Environment, 139755. https://doi.org/10.1016/j.scitotenv.2020.139755spa
dc.relation.referencesBarranquilla, A. de. (2016). Plan de Ordenamiento Territorial.spa
dc.relation.referencesBibi, H., Alam, K., & Bibi, S. (2016). In-depth discrimination of aerosol types using multiple clustering techniques over four locations in Indo-Gangetic plains. Atmospheric Research, 181, 106–114. https://doi.org/10.1016/j.atmosres.2016.06.017spa
dc.relation.referencesBitta, J., Pavlíková, I., Svozilík, V., & Jančík, P. (2018). Air pollution dispersion modelling using spatial analyses. ISPRS International Journal of Geo-Information, 7(12), 7–9. https://doi.org/10.3390/ijgi7120489spa
dc.relation.referencesChang, C. H., Hsiao, Y. L., & Hwang, C. (2015). Evaluating spatial and temporal variations of aerosol optical depth and biomass burning over southeast asia based on satellite data products. Aerosol and Air Quality Research, 15(7), 2625–2640. https://doi.org/10.4209/aaqr.2015.10.0589spa
dc.relation.referencesChuesaard, T., Chetiyanukornkul, T., Kameda, T., Hayakawa, K., & Toriba, A. (2014). Influence of biomass burning on the levels of atmospheric polycyclic aromatic hydrocarbons and their nitro derivatives in Chiang Mai, Thailand. Aerosol and Air Quality Research, 14(4), 1247– 1257. https://doi.org/10.4209/aaqr.2013.05.0161spa
dc.relation.referencesCIOH. (2020). Centro de Investigaciones Oceanográficas e Hidrográficas. Epocas Climaticas En El Litoral Caribe Colombiano. https://www.cioh.org.co/meteorologia/Climatologia/ClimatologiaCaribe7.phpspa
dc.relation.referencesCONAMA. (2016). Teledetección y sensores Medioambientales. Respuesta Verde CONAMA 2016, 17. http://www.conama.org/conama/download/files/conama2016//GTs 2016/14_final.pdfspa
dc.relation.referencesContraloría Distrital. (2018). Informe del estado de los recursos naturales y del Ambiente de Barranquilla 2018.spa
dc.relation.referencesDawson, J. P., Adams, P. J., & Pandis, S. N. (2007). Sensitivity of PM2.5 to climate in the Eastern US: A modeling case study. Atmospheric Chemistry and Physics, 7(16), 4295– 4309. https://doi.org/10.5194/acp-7-4295-2007spa
dc.relation.referencesDepartamento Nacional de Planeación de Colombia. (2018). Valoración económica de la degradación ambiental en Colombia 2015. https://colaboracion.dnp.gov.co/CDT/Prensa/Valoración económica de la degradación ambiental.pdfspa
dc.relation.referencesDevara, P. C. S., & Manoj, M. G. (2013). Aerosol-cloud-precipitation interactions: A challenging problem in regional environment and climate research. Particuology, 11(1), 25– 33. https://doi.org/10.1016/j.partic.2012.07.006spa
dc.relation.referencesDing, A., Yang, Y., Zhao, Z., Hüls, A., Vierkötter, A., Yuan, Z., Cai, J., Zhang, J., Gao, W., Li, J., Zhang, M., Matsui, M., Krutmann, J., Kan, H., Schikowski, T., Jin, L., & Wang, S. (2017). Indoor PM2.5 exposure affects skin aging manifestation in a Chinese population. Scientific Reports. https://doi.org/10.1038/s41598-017-15295-8spa
dc.relation.referencesEPA. (2017). Quality Assurance Handbook for Air Pollution Measurement Systems: Vol. II.spa
dc.relation.referencesFan, Q., Lan, J., Liu, Y., Wang, X., Chan, P., Hong, Y., Feng, Y., Liu, Y., Zeng, Y., & Liang, G. (2015). Process analysis of regional aerosol pollution during spring in the Pearl River Delta region , China. Atmospheric Environment, 122(January 2013), 829–838. https://doi.org/10.1016/j.atmosenv.2015.09.013spa
dc.relation.referencesFilchev, L., Pashova, L., Kolev, V., & Frye, S. (2020). Chapter 6 - Surveys, Catalogues, Databases/Archives, and State-of-the-Art Methods for Geoscience Data Processing (P. Škoda & F. B. T.-K. D. in B. D. from A. and E. O. Adam (eds.); pp. 103–136). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-819154-5.00016-3spa
dc.relation.referencesGallego, A., Ignacio, G., Bejamin, S., Fernandez, P., Garcinuño, R., Bravo, J., Pradana, J., Garcia, M., & Durand, J. (2012). Contaminación atmosférica. In Universidad Naciaonal a Distancia (Vol. 91, Issue 5). https://doi.org/10.1017/CBO9781107415324.004spa
dc.relation.referencesGEOS-Chem. (2019). GEOS-Chem. Overview. http://acmg.seas.harvard.edu/geos/geos_overview.htmlspa
dc.relation.referencesGFWF. (2019). Global Watch Fires. Global Watch Fires. https://fires.globalforestwatch.org/about/spa
dc.relation.referencesGiglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217(March), 72–85. https://doi.org/10.1016/j.rse.2018.08.005spa
dc.relation.referencesGodish, T. (2003). Air Quality, Fourth Edition. In Air Quality, Fourth Edition. https://doi.org/10.1201/b13168spa
dc.relation.referencesGonzález-álvarez, Á., Viloria-Marimón, O. M., Coronado-Hernández, Ó. E., Vélez-Pereira, A. M., Tesfagiorgis, K., & Coronado-Hernández, J. R. (2019). Isohyetal maps of daily maximum rainfall for different return periods for the Colombian Caribbean Region. Water (Switzerland), 11(2). https://doi.org/10.3390/w11020358spa
dc.relation.referencesGuillermo, Ronquillo-Lomeli, Gilberto, Herrera-Ruiz, Alfredo, I., José Gabriel Alejandro, R.-M., Ramirez-Maya, & Trejo-Perea, M. (2018). Total suspended particle emissions modelling in an industrial boiler. Energies, 11(11). https://doi.org/10.3390/en11113097spa
dc.relation.referencesGuo, L., Chen, B., Zhang, H., Xu, G., Lu, L., Lin, X., Kong, Y., Wang, F., & Li, Y. (2018). Improving PM2.5 forecasting and emission estimation based on the bayesian optimization method and the coupled FLEXPART-WRF model. Atmosphere, 9(11), 1–17. https://doi.org/10.3390/atmos9110428spa
dc.relation.referencesHernandez, A. J., Morales-rincon, L. A., Wu, D., Mallia, D., Lin, J. C., & Jimenez, R. (2019). Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Basin. Atmospheric Environment, 205(45), 1–8. https://doi.org/10.1016/j.atmosenv.2019.01.051spa
dc.relation.referencesHoyos, N., Correa-Metrio, A., Sisa, A., Ramos-Fabiel, M. A., Espinosa, J. M., Restrepo, J. C., & Escobar, J. (2017). The environmental envelope of fires in the Colombian Caribbean. Applied Geography, 84, 42–54. https://doi.org/10.1016/j.apgeog.2017.05.001spa
dc.relation.referencesHuang, H. C., Lin, F. C. F., Wu, M. F., Nfor, O. N., Hsu, S. Y., Lung, C. C., Ho, C. C., Chen, C. Y., & Liaw, Y. P. (2019). Association between chronic obstructive pulmonary disease and PM2.5 in Taiwanese nonsmokers. International Journal of Hygiene and Environmental Health, 222(5), 884–888. https://doi.org/10.1016/j.ijheh.2019.03.009spa
dc.relation.referencesHuang, X. H. H., Bian, Q., Ng, W. M., Louie, P. K. K., & Yu, J. Z. (2014). Characterization of PM2.5 major components and source investigation in suburban Hong Kong: A one year monitoring study. Aerosol and Air Quality Research, 14(1), 237–250. https://doi.org/10.4209/aaqr.2013.01.0020spa
dc.relation.referencesIDEAM. (2019). IDEAM - Instituto de Hidrología, Meteorología y Estudios Ambientales. Servicios de Pronósticos y Alertas. http://www.ideam.gov.co/web/atencion-y-participacionciudadana/servicios-pronosticos-alertasspa
dc.relation.referencesINPE. (2020). Banco de datos de quemadas. Portal De Datos Abiertos Del Programa Quemadas. http://queimadas.dgi.inpe.br/queimadas/portal/informacoes/apresentacaospa
dc.relation.referencesIslam, Robiul;Jayarathne, T., Simpson, I., Werden, B., Maben, J., Gilbert, A., Praveen, P., Adhikari, S., Panday, A., Rupakheti, M., Blake, D., Yokelson, R., DeCarlo, P., Keene, W., & Stone, E. (2019). Ambient air quality in the Kathmandu Valley, Nepal during the premonsoon: Concentrations and sources of particulate matter and trace gases. In Atmospheric Chemistry and Physics Discussions (pp. 1–53). https://doi.org/10.5194/acp-2019-333spa
dc.relation.referencesJiménez, J. F. (2016). Altura de la Capa de Mezcla en un área urbana montañosa y tropical. Universidad de Antioquia Facultad de Ingeniería. Medellín. Colombia. 235.spa
dc.relation.referencesJuda-Rezler, K., Reizer, M., Maciejewska, K., Błaszczak, B., & Klejnowski, K. (2020). Characterization of atmospheric PM2.5 sources at a Central European urban background site. Science of the Total Environment, 713, 136729. https://doi.org/10.1016/j.scitotenv.2020.136729spa
dc.relation.referencesKabisch, N., Selsam, P., Kirsten, T., Lausch, A., & Bumberger, J. (2019). A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. 99(July 2018), 273–282.spa
dc.relation.referencesKaneyasu, N., Ishidoya, S., Terao, Y., Mizuno, Y., & Sugawara, H. (2020). Estimation of PM2.5 Emission Sources in the Tokyo Metropolitan Area by Simultaneous Measurements of Particle Elements and Oxidative Ratio in Air. ACS Earth and Space Chemistry, 4(2), 297– 304. https://doi.org/10.1021/acsearthspacechem.9b00314spa
dc.relation.referencesKnorr, W., Dentener, F., Lamarque, J. F., Jiang, L., & Arneth, A. (2017). Wildfire air pollution hazard during the 21st century. Atmospheric Chemistry and Physics, 17(14), 9223–9236. https://doi.org/10.5194/acp-17-9223-2017spa
dc.relation.referencesKong, H., Lin, J., Zhang, R., Liu, M., Weng, H., Ni, R., Chen, L., & Wang, J. (2019). Highresolution (0.05°x 0.05°) NOx emissions in the Yangtze River Delta inferred from OMI. Atmospheric Chemistry and Physics, x, 12835–12856.spa
dc.relation.referencesKota, S. H., Guo, H., Myllyvirta, L., Hu, J., Sahu, S. K., Garaga, R., Ying, Q., Gao, A., Dahiya, S., Wang, Y., & Zhang, H. (2018). Year-long simulation of gaseous and particulate air pollutants in India. Atmospheric Environment, 180, 244–255. https://doi.org/10.1016/j.atmosenv.2018.03.003spa
dc.relation.referencesKraaij, T., Baard, J. A., Arndt, J., Vhengani, L., & van Wilgen, B. W. (2018). An assessment of climate, weather, and fuel factors influencing a large, destructive wildfire in the Knysna region, South Africa. Fire Ecology, 14(2), 1–12. https://doi.org/10.1186/s42408-018-0001-0spa
dc.relation.referencesKumar, S., Lin, N., Chantara, S., Wang, S., Khamkaew, C., Prapamontol, T., & Janjai, S. (2018). Science of the Total Environment Radiative response of biomass-burning aerosols over an urban atmosphere in northern peninsular Southeast Asia. Science of the Total Environment, 633, 892–911. https://doi.org/10.1016/j.scitotenv.2018.03.204spa
dc.relation.referencesLai, H. C., Ma, H. W., Chen, C. R., Hsiao, M. C., & Pan, B. H. (2019). Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5. Atmospheric Environment, 212(May), 116–127. https://doi.org/10.1016/j.atmosenv.2019.05.038spa
dc.relation.referencesLangley Dewitt, H., Gasore, J., Rupakheti, M., Potter, K. E., Prinn, R. G., De Dieu Ndikubwimana, J., Nkusi, J., & Safari, B. (2019). Seasonal and diurnal variability in O3, black carbon, and CO measured at the Rwanda Climate Observatory. Atmospheric Chemistry and Physics, 19(3), 2063–2078. https://doi.org/10.5194/acp-19-2063-2019spa
dc.relation.referencesLi, Fang, Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., … Wiedinmyer, C. (2019). Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP). Atmospheric Chemistry and Physics Discussions, January, 1–57. https://doi.org/10.5194/acp-2019-37spa
dc.relation.referencesLi, Fangjun, Zhang, X., Kondragunta, S., & Roy, D. P. (2018). Investigation of the Fire Radiative Energy Biomass Combustion Coefficient: A Comparison of Polar and Geostationary Satellite Retrievals Over the Conterminous United States. Journal of Geophysical Research: Biogeosciences, 123(2), 722–739. https://doi.org/10.1002/2017JG004279spa
dc.relation.referencesLi, X., Chen, W. Y., Sanesi, G., & Lafortezza, R. (2019). Remote sensing in urban forestry: Recent applications and future directions. Remote Sensing, 11(10), 1–20. https://doi.org/10.3390/rs11101144spa
dc.relation.referencesLi, Z., Wang, Y., Li, Z., Guo, S., & Hu, Y. (2020). Levels and Source of PM2.5 Associated PAHs during and after Wheat Harvest in a Central Rural Area of Beijing-Tianjin-Hebei (BTH) Region. Aerosol and Air Quality Research, 1070–1082. https://doi.org/10.4209/aaqr.2020.03.0083spa
dc.relation.referencesLiu, T., Marlier, M. E., DeFries, R. S., Westervelt, D. M., Xia, K. R., Fiore, A. M., Mickley, L. J., Cusworth, D. H., & Milly, G. (2018). Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities: Delhi, Bengaluru, and Pune. Atmospheric Environment, 172(September 2017), 83–92. https://doi.org/10.1016/j.atmosenv.2017.10.024spa
dc.relation.referencesLiu, Y., Wang, J., Zhao, X., Wang, J., Wang, X., Hou, L., Yang, W., Han, B., & Bai, Z. (2020). Characteristics, secondary formation and regional contributions of pm2.5 pollution in jinan during winter. Atmosphere, 11(3), 9–11. https://doi.org/10.3390/atmos11030273spa
dc.relation.referencesLlanos, E. (2011). Metropolización de Barranquilla y problemática espacial de Soledad - Atlántico. Perspectiva Geográfica, 1(15), 261–276. https://doi.org/10.19053/01233769.1742spa
dc.relation.referencesLuo, Lei, Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., Wang, G., Shi, P., Khatteli, H., Chen, F., Tariq, S., Shao, J., Bachagha, N., Yang, R., & Yao, Y. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment, 232(June), 111280. https://doi.org/10.1016/j.rse.2019.111280spa
dc.relation.referencesLuo, Li, Zhang, Y., Xiao, H., Xiao, H., & Zheng, N. (2019). Spatial Distributions and Sources of Inorganic chlorine in PM2.5 across China in winter. Atmosphere, x.spa
dc.relation.referencesMADS. (2011). Estrategia de corresponsabilidad social en la lucha contra incendios forestales. 8, 21. www.minambiente.gov.coContenidospa
dc.relation.referencesMalamakal, T., Chen, L. A., Wang, X., Green, M. C., Gronstal, S., Chow, J. C., & Watson, J. G. (2013). Prescribed burn smoke impact in the Lake Tahoe Basin : model simulation and field verification. 52, 225–243.spa
dc.relation.referencesMarkou, M. T., & Kassomenos, P. (2010). Cluster analysis of five years of back trajectories arriving in Athens, Greece. Atmospheric Research, 98(2–4), 438–457. https://doi.org/10.1016/j.atmosres.2010.08.006spa
dc.relation.referencesMartins, L. D., Hallak, R., Alves, R. C., de Almeida, D. S., Squizzato, R., Moreira, C. A. B., Beal, A., da Silva, I., Rudke, A., & Martins, J. A. (2018). Long-range transport of aerosols from biomass burning over Southeastern South America and their implications on air quality. Aerosol and Air Quality Research, 18(7), 1734–1745. https://doi.org/10.4209/aaqr.2017.11.0545spa
dc.relation.referencesMasiol, M., Squizzato, S., Rich, D. Q., & Hopke, P. K. (2019). Long-term trends (2005–2016) of source apportioned PM2.5 across New York State. Atmospheric Environment, 201(June 2018), 110–120. https://doi.org/10.1016/j.atmosenv.2018.12.038spa
dc.relation.referencesMataveli, G. A. V., Silva, M. E. S., França, D. de A., Brunsell, N. A., de Oliveira, G., Cardozo, F. da S., Bertani, G., & Pereira, G. (2019). Characterization and Trends of Fine Particulate Matter (PM2.5) Fire Emissions in the Brazilian Cerrado during 2002–2017. Remote Sensing, 11(19), 2254. https://doi.org/10.3390/rs11192254spa
dc.relation.referencesMendez-Espinosa, J. F., Belalcazar, L. C., Morales Betancourt, R., Ballesteros-González, K., Sullivan, A. P., Morales-Betancourt, R., Hoyos, N., Correa-Metrio, A., Sisa, A., Ramos- Fabiel, M. A., Espinosa, J. M., Restrepo, J. C., Escobar, J., Chen, S. P., Wang, C. C. H., Lin, W. D., Tong, Y. H., Chen, Y. C., Chiu, C. J., … Wu, W. (2017). Ultrafine particles from residential biomass combustion: A review on experimental data and toxicological response. Atmospheric Chemistry and Physics, 9(February), 1–15. https://doi.org/10.1080/02726351003758444spa
dc.relation.referencesMielonen, T., Aaltonen, V., Lihavainen, H., Hyvärinen, A. P., Arola, A., Komppula, M., & Kivi, R. (2013). Biomass burning aerosols observed in Northern Finland during the 2010 wildfires in Russia. Atmosphere, 4(1), 17–34. https://doi.org/10.3390/atmos4010017 Ministerio de Ambiente y Desarrollo Sostenible. (2017). 2017 COLOMBIA Resolución 2254 de 2017 - Niveles Calidad del Aire.spa
dc.relation.referencesMoghim, S., & Ramezanpoor, R. (2019). Characterization of aerosol types over Lake Urmia Basin. E3S Web of Conferences, 99, 3–6. https://doi.org/10.1051/e3sconf/20199901006spa
dc.relation.referencesMoreno Horacio. (2019). Semana Sostenible. Muertes Por Contaminación Del Aire Le Costaron a Medellín $5 Billones En Solo Un Año. https://sostenibilidad.semana.com/medioambiente/articulo/muertes-por-contaminacion-del-aire-le-costaron-a-medellin-5-billonesde-pesos-en-solo-un-ano/43332spa
dc.relation.referencesMoya Álvarez, A. S., Arredondo, R. E., & Yuli Posadas, R. Á. (2017). Determination of the presence of particles (PM 10 ) in Peru produced by biomass burning using numerical models. Revista Internacional de Contaminacion Ambiental, 33(1), 99–108. https://doi.org/10.20937/RICA.2017.33.01.09spa
dc.relation.referencesMukai, S., Yasumoto, M., & Nakata, M. (2014). Estimation of Biomass Burning Influence on Air Pollution around Beijing from an Aerosol Retrieval Model. Scientific World Journal, 2014(June 2010). https://doi.org/10.1155/2014/649648spa
dc.relation.referencesMukherjee, T., Vinoj, V., Midya, S. K., Puppala, S. P., & Adhikary, B. (2020). Numerical simulations of different sectoral contributions to post monsoon pollution over Delhi. Heliyon, 6(3), e03548. https://doi.org/10.1016/j.heliyon.2020.e03548spa
dc.relation.referencesNación. (2018, December 21). El fenómeno del Niño ya está en el país y nos está impactando. EL Tiempo. https://www.eltiempo.com/colombia/otras-ciudades/fenomeno-del-nino-2018- 2019-en-colombia-y-probables-afectaciones-307218spa
dc.relation.referencesNASA. (2019a). MODIS Moderate Resolution Imaging Spectroradiometer. https://modis.gsfc.nasa.gov/about/index.phpspa
dc.relation.referencesNASA. (2019b). NASA Worldview. https://worldview.earthdata.nasa.gov/spa
dc.relation.referencesNgan, F., Stein, A., Finn, D., & Eckman, R. (2018). Dispersion simulations using HYSPLIT for the Sagebrush Tracer Experiment. Atmospheric Environment, 186(March), 18–31. https://doi.org/10.1016/j.atmosenv.2018.05.012spa
dc.relation.referencesNOAA. (2019a). Hysplit. National Oceanic and Atmospheric Administration. https://www.arl.noaa.gov/hysplit/hysplit/spa
dc.relation.referencesNOAA. (2019b). Información de archivo del Sistema global de asimilación de datos. Air Resources Laboratory. Información de archivo del Sistema global de asimilación de datosspa
dc.relation.referencesNoda, J., Bergström, R., Kong, X., Gustafsson, T. L., Kovacevik, B., Svane, M., & Pettersson, J. B. C. (2019). Aerosol from Biomass Combustion in Northern Europe: Influence of Meteorological Conditions and Air Mass History. Atmosphere, 10(12), 789. https://doi.org/10.3390/atmos10120789spa
dc.relation.referencesNoyes, K. J., Kahn, R., Sedlacek, A., Kleinman, L., Limbacher, J., & Li, Z. (2020). Wildfire smoke particle properties and evolution, from space-based multi-angle imaging. Remote Sensing, 12(5). https://doi.org/10.3390/rs12050769spa
dc.relation.referencesOcko, I. B., & Ginoux, P. A. (2017). Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements. Atmospheric Chemistry and Physics, 17(7), 4451–4475. https://doi.org/10.5194/acp-17-4451-2017spa
dc.relation.referencesOrioli, R., Cremona, G., Ciancarella, L., & Solimini, A. G. (2018). Association between PM10, PM2.5, NO2, O3 and self-reported diabetes in Italy: A cross-sectional, ecological study. PLoS ONE, 13(1), 1–17. https://doi.org/10.1371/journal.pone.0191112spa
dc.relation.referencesParques Nacionales Naturales de Colombia. (2014). Tres Hectáreas fueron consumidas en quema en el Vía Parque Isla de Salamanca. Parques Nacionales. http://www.parquesnacionales.gov.co/portal/es/tres-hectareas-fueron-consumidas-enquema-en-el-via-parque-isla-de-salamanca/spa
dc.relation.referencesPathak, B., Bhuyan, P. K., Gogoi, M., & Bhuyan, K. (2012). Seasonal heterogeneity in aerosol types over Dibrugarh-North-Eastern India. Atmospheric Environment, 47, 307–315. https://doi.org/10.1016/j.atmosenv.2011.10.061spa
dc.relation.referencesPereira, Allan A., Pereira, J. M. C., Libonati, R., Oom, D., Setzer, A. W., Morelli, F., MachadoSilva, F., & de Carvalho, L. M. T. (2017a). Burned area mapping in the Brazilian Savanna using a one-class support vector machine trained by active fires. Remote Sensing, 9(11). https://doi.org/10.3390/rs9111161spa
dc.relation.referencesPereira, Allan A., Pereira, J. M. C., Libonati, R., Oom, D., Setzer, A. W., Morelli, F., MachadoSilva, F., & de Carvalho, L. M. T. (2017b). Burned area mapping in the Brazilian Savanna using a one-class support vector machine trained by active fires. Remote Sensing, 9(11). https://doi.org/10.3390/rs9111161spa
dc.relation.referencesPereira, Allan Arantes, De Barros, D. A., Acerbi, F. W., Alves Pereira, J. A., & Dos Reis, A. A. (2013). Análise da distribuição espacial de áreas queimadas através da função K de Ripley. Scientia Forestalis/Forest Sciences, 41(100), 445–455.spa
dc.relation.referencesPisso, I., Sollum, E., Grythe, H., Kristiansen, N., Cassiani, M., Eckhardt, S., Arnold, D., Morton, D., Thompson, R. L., Groot Zwaaftink, C. D., Evangeliou, N., Sodemann, H., Haimberger, L., Henne, S., Brunner, D., Burkhart, J. F., Fouilloux, A., Brioude, J., Philipp, A., … Stohl, A. (2019). The Lagrangian particle dispersion model FLEXPART version 10.3. Geoscientific Model Development Discussions, January, 1–67. https://doi.org/10.5194/gmd- 2018-333spa
dc.relation.referencesPrato, D. F., & Huertas, J. I. (2019). Determination of the area affected by agricultural burning. Atmosphere, 10(6), 1–14. https://doi.org/10.3390/atmos10060312spa
dc.relation.referencesPribadi, A., & Kurats, G. (2016). Greenhouse gas and air pollutant emissions from land and forest fire in Indonesia during 2015 based on satellite data. Journal of Physics: Conference Series, 755(1), 0–8. https://doi.org/10.1088/1742-6596/755/1/011001spa
dc.relation.referencesQuerol, X., Pérez, N., Reche, C., Ealo, M., Ripoll, A., Tur, J., Pandolfi, M., Pey, J., Salvador, P., Moreno, T., & Alastuey, A. (2019). African dust and air quality over Spain: Is it only dust that matters? Science of the Total Environment, 686, 737–752. https://doi.org/10.1016/j.scitotenv.2019.05.349spa
dc.relation.referencesReddington, C. L., Morgan, W. T., Darbyshire, E., Brito, J., Coe, H., Artaxo, P., Marsham, J., & Spracklen, D. V. (2018). Biomass burning aerosol over the Amazon: analysis of aircraft, surface and satellite observations using a global aerosol model. Atmospheric Chemistry and Physics Discussions, October 2012, 1–32. https://doi.org/10.5194/acp-2018-849spa
dc.relation.referencesRoberts, G., Wooster, M. J., Xu, W., & He, J. (2018). Fire activity and fuel consumption dynamics in sub-Saharan Africa. Remote Sensing, 10(10), 1–22. https://doi.org/10.3390/rs10101591spa
dc.relation.referencesRodrigues, J. A., Libonati, R., Peres, L. D. F., & Setzer, A. (2018). Mapeamento de Áreas Queimadas em Unidades de Conservação da Região Serrana do Rio de Janeiro Utilizando o Satélite Landsat-8 Durante a Seca de 2014. Anuário Do Instituto de Geociências - UFRJ, 41(1), 318–327. https://doi.org/0101-9759spa
dc.relation.referencesRolph, G., Stein, A., & Stunder, B. (2017). Real-time Environmental Applications and Display sYstem: READY. Environmental Modelling and Software, 95, 210–228. https://doi.org/10.1016/j.envsoft.2017.06.025spa
dc.relation.referencesRönkkö, T. J., Hirvonen, M. R., Happo, M. S., Leskinen, A., Koponen, H., Mikkonen, S., Bauer, S., Ihantola, T., Hakkarainen, H., Miettinen, M., Orasche, J., Gu, C., Wang, Q., Jokiniemi, J., Sippula, O., Komppula, M., & Jalava, P. I. (2020). Air quality intervention during the Nanjing youth olympic games altered PM sources, chemical composition, and toxicological responses. Environmental Research, 185(September 2019), 109360. https://doi.org/10.1016/j.envres.2020.109360spa
dc.relation.referencesRupakheti, D., & Kang, S. (2018). Observation of optical properties and sources of aerosols at Buddha ’ s birthplace , Lumbini , Nepal : environmental implications. 14868–14881.spa
dc.relation.referencesRybarczyk, Y., & Zalakeviciute, R. (2018). Machine learning approaches for outdoor air quality modelling: A systematic review. Applied Sciences (Switzerland), 8(12). https://doi.org/10.3390/app8122570spa
dc.relation.referencesSamek, L., Stegowski, Z., Furman, L., Styszko, K., Szramowiat, K., & Fiedor, J. (2017). Quantitative Assessment of PM2.5 Sources and Their Seasonal Variation in Krakow. Water, Air, and Soil Pollution, 228(8). https://doi.org/10.1007/s11270-017-3483-5spa
dc.relation.referencesSchwartz, J. D., Wang, Y., Kloog, I., Yitshak-Sade, M., Dominici, F., & Zanobetti, A. (2018). Estimating the Effects of PM 2.5 on Life Expectancy Using Causal Modeling Methods. Environmental Health Perspectives, 126(12), 127002. https://doi.org/10.1289/EHP3130spa
dc.relation.referencesShelestov, A., Kolotii, A., Borisova, T., Turos, O., Milinevsky, G., Gomilko, I., Bulanay, T., Fedorov, O., Shumilo, L., Pidgorodetska, L., Kolos, L., Borysov, A., Pozdnyakova, N., Chunikhin, A., Dudarenko, M., Petrosian, A., Danylevsky, V., Miatselskaya, N., & Choliy, V. (2019). Essential variables for air quality estimation. International Journal of Digital Earth, 0(0), 1–21. https://doi.org/10.1080/17538947.2019.1620881spa
dc.relation.referencesShen, H., Cheng, P. H., Yuan, C. S., Yang, Z. M., Hung, C. M., & Ie, I. R. (2020). Chemical characteristics, spatiotemporal distribution, and source apportionment of PM2.5 surrounding industrial complexes in Southern Kaohsiung. Aerosol and Air Quality Research, 20(3), 557–575. https://doi.org/10.4209/aaqr.2020.01.0007spa
dc.relation.referencesShi, S., Cheng, T., Gu, X., Guo, H., Wu, Y., & Wang, Y. (2019). Biomass burning aerosol characteristics for different vegetation types in different aging periods. Environment International, 126(February), 504–511. https://doi.org/10.1016/j.envint.2019.02.073spa
dc.relation.referencesSilva, P. S., Bastos, A., Libonati, R., Rodrigues, J. A., & DaCamara, C. C. (2019). Impacts of the 1.5 °C global warming target on future burned area in the Brazilian Cerrado. Forest Ecology and Management, 446(May), 193–203. https://doi.org/10.1016/j.foreco.2019.05.047spa
dc.relation.referencesSobhani, N., Kulkarni, S., & Carmichael, G. R. (2018). Source sector and region contributions to black carbon and PM2.5 in the Arctic. Atmospheric Chemistry and Physics, 18(24), 18123– 18148. https://doi.org/10.5194/acp-18-18123-2018spa
dc.relation.referencesSoleimanifar, N., Bidad, K., Nicknam, M., Nikbin, B., & Mahmoudi, M. (2018). Effect of food intake and ambient particulate air pollution on ankylosing spondylitis disease activity. Clinical and Experimental Rheumatology, 36, S43. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L6222665 23spa
dc.relation.referencesThermo Fisher Scientific. (2014). Instruction Manual Model 5014 i Beta. 106428. www.thermofisher.comspa
dc.relation.referencesTurap, Y., Rekefu, S., Wang, G., Talifu, D., Gao, B., Aierken, T., Hao, S., Wang, X., Tursun, Y., Maihemuti, M., & Nuerla, A. (2019). Chemical characteristics and source apportionment of pm2.5 during winter in the southern part of Urumqi, China. Aerosol and Air Quality Research, 19(6), 1325–1337. https://doi.org/10.4209/aaqr.2018.12.0454spa
dc.relation.referencesUccelli, R., Mastrantonio, M., Altavista, P., Pacchierotti, F., Piersanti, A., & Ciancarella, L. (2019). Impact of modelled PM2.5, NO2 and O3 annual air concentrations on some causes of mortality in Tuscany municipalities. European Journal of Public Health, 29(5), 871–876. https://doi.org/10.1093/eurpub/cky210spa
dc.relation.referencesUN-HABITAT. (2011). Book Review: Global Report on Human Settlements 2011, Cities and Climate Change. In Environmental Law Review (Vol. 14, Issue 3). https://doi.org/10.1350/enlr.2012.14.3.162spa
dc.relation.referencesVenkatappa, M., Sasaki, N., Shrestha, R. P., Tripathi, N. K., & Ma, H. O. (2019). Determination of vegetation thresholds for assessing land use and land use changes in Cambodia using the Google Earth Engine cloud-computing platform. Remote Sensing, 11(13). https://doi.org/10.3390/rs11131514spa
dc.relation.referencesVictor, N., Alexandru, D., Luminita, M., & Camelia, T. (2018). Biomass Burning Aerosol Over Romania Using Dispersion Model And Calipso Data. 04012, 1–4.spa
dc.relation.referencesViloria-Marimón, O. M., González-Álvarez, Á., & Mouthón-Bello, J. A. (2019). Analysis of the behavior of daily maximum rainfall within the department of Atlántico, Colombia. Water (Switzerland), 11(12), 1–24. https://doi.org/10.3390/w11122453spa
dc.relation.referencesVivanco Moreno, S. F., & Ramírez Lara, E. (2007). Aplicación del Modelo Hysplit (Hybrid Single Particle Lagrangian Integrated Trayectories) para Evaluar las Trayectorias del Aire y su Impacto en la Dispersión de Contaminantes Atmosféricos.spa
dc.relation.referencesVongruang, P., Wongwises, P., & Pimonsree, S. (2017). Assessment of fire emission inventories for simulating particulate matter in Upper Southeast Asia using WRF-CMAQ. Atmospheric Pollution Research, 8(5), 921–929. https://doi.org/10.1016/j.apr.2017.03.004spa
dc.relation.referencesWang, L. K., Pereira, N. C., & Hung, Y. (2004). Air Pollution Control Engineering (H. Press (ed.); Vol. 1).spa
dc.relation.referencesWang, X. D., Zheng, M., Lou, H. F., Wang, C. S., Zhang, Y., Bo, M. Y., Ge, S. Q., Zhang, N., Zhang, L., & Bachert, C. (2016). An increased prevalence of self-reported allergic rhinitis in major Chinese cities from 2005 to 2011. Allergy: European Journal of Allergy and Clinical Immunology. https://doi.org/10.1111/all.12874spa
dc.relation.referencesWerf, G. R. Van Der, Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., & Morton, D. C. (2010). and Physics Global fire emissions and the contribution of deforestation , savanna , forest , agricultural , and peat fires ( 1997 – 2009 ). 2001, 11707– 11735. https://doi.org/10.5194/acp-10-11707-2010spa
dc.relation.referencesWHO. (2014). Burden of disease from Ambient Air Pollution for 2012. Lmi, 2012–2014. https://doi.org/10.1016/S0140-6736(12)61766-8.Smithspa
dc.relation.referencesWikistart. (2019). FLEXPART. Modelo FLEXPART. https://www.flexpart.eu/ 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 Atmospheres, 110(24), 1–24. https://doi.org/10.1029/2005JD006318spa
dc.relation.referencesWooster, M. J., Xu, W., & Nightingale, T. (2012). Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets. Remote Sensing of Environment, 114(2), 236–254. https://doi.org/10.1016/j.rse.2017.12.016spa
dc.relation.referencesWorld Health Organization. (2005). Guías de calidad del aire de la OMS relativas al material particulado, el ozono, el dióxido de nitrógeno y el dióxido de azufre. Actualización mundial 2005. In World Health Organization. http://www.who.int/phe/health_topics/outdoorair/outdoorair_aqg/es/spa
dc.relation.referencesWorld Health Organization. (2013). Health risks of air pollution in Europe – HRAPIE project. World Heath Organization (WHO), 60. https://doi.org/10.1021/acs.est.5b05833spa
dc.relation.referencesWorld Health Organization. (2018). Nueve de cada diez personas de todo el mundo respiran aire contaminado. World Health Organization. https://www.who.int/es/news-room/detail/02-052018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-takingactionspa
dc.relation.referencesWu, Y., Arapi, A., Huang, J., Gross, B., & Moshary, F. (2018). Intra-continental wild fi re smoke transport and impact on local air quality observed by ground-based and satellite remote sensing in New York City. 187(December 2017), 266–281.spa
dc.relation.referencesWWF Colombia. (2013). Nodos Regionales De Cambio Climatico Reporte Consolidado De Linea Base Analisis De Riesgos Climaticos Y Necesidades De Adaptación Climatica. Ministerio Medio Ambiente. Colombia. http://www.minambiente.gov.co/index.php/component/content/article?id=1695:plantillacambio-climatico-45#1-1-enlacesspa
dc.relation.referencesYang, Q., Huang, X., & Tang, Q. (2019). Science of the Total Environment The footprint of urban heat island effect in 302 Chinese cities : Temporal trends and associated factors. Science of the Total Environment, 655, 652–662. https://doi.org/10.1016/j.scitotenv.2018.11.171 Yeo, S. young, Lee, H. kyeong, Choi, S. woo, Seol, S. hee, Jin, H. ah, Yoo, C., Lim, J. yun, & Kim, J. soo. (2019). Analysis of the National Air Pollutant Emission Inventory (CAPSS 2015) and the major cause of change in Republic of Korea. Asian Journal of Atmospheric Environment, 13(3), 212–231. https://doi.org/10.5572/ajae.2019.13.3.212spa
dc.relation.referencesYin, L., Du, P., Zhang, M., Liu, M., Xu, T., & Song, Y. (2019). Estimation of emissions from biomass burning in China (2003-2017) based on MODIS fire radiative energy data. Biogeosciences, 16(7), 1629–1640. https://doi.org/10.5194/bg-16-1629-2019spa
dc.relation.referencesZhang, T., Wooster, M. J., de Jong, M. C., & Xu, W. (2018). How well does the “small fire boost” methodology used within the GFED4.1s fire emissions database represent the timing, location and magnitude of agricultural burning? Remote Sensing, 10(6). https://doi.org/10.3390/rs10060823spa
dc.relation.referencesZhang, X., Kondragunta, S., & Quayle, B. (2011). Estimation of biomass burned areas using multiple-satellite-observed active fires. IEEE Transactions on Geoscience and Remote Sensing, 49(11 PART 2), 4469–4482. https://doi.org/10.1109/TGRS.2011.2149535spa
dc.relation.referencesZhang, X., Kondragunta, S., Ram, J., Schmidt, C., & Huang, H.-C. (2012). Near-real-time global biomass burning emissions product from geostationary satellite constellation. Journal of Geophysical Research: Atmospheres, 117(D14), n/a-n/a. https://doi.org/10.1029/2012jd017459spa
dc.relation.referencesZhou, Y., Han, Z., Liu, R., Zhu, B., Li, J., & Zhang, R. (2018). A modeling study of the impact of crop residue burning on pm2.5 concentration in beijing and tianjin during a severe autumn haze event. Aerosol and Air Quality Research, 18(7), 1558–1572. https://doi.org/10.4209/aaqr.2017.09.0334spa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


Ficheros en el ítem

Thumbnail
Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-ShareAlike 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-ShareAlike 4.0 International