Medición de perfiles verticales de material particulado en la Sabana de Bogotá

Vista sencilla de ítem
dc.contributor.advisorRojas Roa, Néstor Yezid
dc.contributor.authorJaimes Gonzalez, Daniel Alejandro
dc.contributor.researchgroupCalidad del Airespa
dc.date.accessioned2022-08-25T19:00:30Z
dc.date.available2022-08-25T19:00:30Z
dc.date.issued2022
dc.descriptionilustraciones, fotografías, graficas, mapasspa
dc.description.abstractLa calidad del aire es una de las mayores preocupaciones para la ciudadanía. En los últimos años se han emitido estados de prevención por la mala calidad del aire en la ciudad de Bogotá y los alrededores. Un componente clave en la declaración de alertas ha sido el pronóstico de la calidad del aire con modelos de transporte químico de la atmósfera. Este tipo de herramientas se alimenta de datos satelitales y de superficie para su ajuste y verificación. Sin embargo, para el caso de material particulado, solo existen mediciones a nivel del suelo o mediciones remotas. Contar con herramientas para la medición de material particulado en la vertical puede fortalecer el desempeño de estos modelos. En este trabajo, se evalúa el uso de sensores de bajo costo y su uso en equipos drone para la medición de material particulado a diferentes alturas, en los primeros 500 metros de la atmósfera. Esto permite la obtención de perfiles de concentración, los cuales muestran que la atmósfera presenta al menos dos capas bien diferenciadas desde la madrugada, antes de alcanzar una concentración uniforme por mezclado vertical. (Texto tomado de la fuente)spa
dc.description.abstractAir quality is a major concern for citizens. In the last few years, air pollution alerts have been declared in the city of Bogota and its surrounding areas. These alerts have been known in advance of their occurrence thanks to the city's air quality forecasting and modeling tools. This type of tool relies on satellite and surface data for adjustment and verification. However, for particulate matter, only ground level or remote measurements are available. Having tools for measuring particulate matter vertically can strengthen the performance of these models. In this work, we evaluate the use of low-cost sensors and their use in drone equipment for the measurement of particulate matter at different altitudes, in the first 500 meters of the atmosphere. This allows obtaining concentration profiles, which show that the atmosphere presents at least two well differentiated layers from early morning, before reaching a uniform concentration by vertical mixing.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería Ambientalspa
dc.description.methodsPara evaluar los sensores a usar, se usará la metodología recomendada por la EPA. La cual tiene como objeto comparar el rendimiento de los sensores de calidad del aire para aplicaciones que no requieren comprar con la normatividad. Como lo puede ser caracterizaciones o análisis de tendencias. Para ello la EPA recomienda dos procedimientos, uno que consiste en instalar los sensores en un ambiente donde se conoce completamente las condiciones, como la temperatura y la humedad el aire. La segunda metodología consiste en la comparación con monitoreos de referencia o FRM/FEM, los cuales cumplen estándares de la EPA para la emisión de información ambiental que puede ser usada para tomar decisiones de carácter legal. Esto implica el uso de estándares trazables, mantenimientos preventivos, verificaciones y demás herramientas de control establecidas por el fabricante. Es esta segunda metodología la usada para determinar la aptitud de los sensores de calidad del aire.spa
dc.description.researchareaContaminación del aire por material particulado: caracterizaciónspa
dc.format.extentxx, 94 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/82110
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería Química y Ambientalspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Ambientalspa
dc.relation.indexedRedColspa
dc.relation.indexedLaReferenciaspa
dc.relation.referencesOrganizacion Mundial de la Salud, “Actualización mundial 2005,” 2005. [Online]. Available: https://apps.who.int/iris/bitstream/handle/10665/69478/WHO_SDE_PHE_OEH_06.02_spa.pdf%0Ajsessionid=970454FA25DFB60943EBC3409FF7E87B?sequence=1.spa
dc.relation.referencesJ. Schwartz, D. W. Dockery, and L. M. Neas, “Is Daily Mortality Associated Specifically with Fine Particles?,” J. Air Waste Manage. Assoc., vol. 46, no. 10, pp. 927–939, 1996, doi: 10.1080/10473289.1996.10467528.spa
dc.relation.referencesWorld Health Organization, “Ambient air pollution attributable deaths,” 2016. https://www.who.int/data/gho/data/indicators/indicator-details/GHO/ambient-air-pollution-attributable-deathsspa
dc.relation.referencesINS, “INS: 17,549 muertes en Colombia están asociadas a mala calidad del agua, del aire y a la exposición a combustibles pesados,” pp. 1–3, 2019.spa
dc.relation.referencesC. A. Pope III, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, and G. D. Thurston, “Lung Cancer, Cadiopulmonary Mortality and Long-term Exposure to Fine Particulate Air Pollution,” J. Am. Med. Assoc., vol. 287, no. 9, pp. 1132–1141, 2002spa
dc.relation.referencesB. Mundial, “Environmental Health Costs in Colombia: Changes from 2002 to 2010.” Washington DC, 2012, Accessed: Aug. 25, 2021. [Online]. Available: www.worldbank.orgspa
dc.relation.referencesJ. O. Pinto et al., “The status and future of small uncrewed aircraft systems (UAS) in operational meteorology,” Bull. Am. Meteorol. Soc., vol. 102, no. 11, pp. E2121–E2136, Nov. 2021, doi: 10.1175/BAMS-D-20-0138.1spa
dc.relation.referencesWorld Meteorological Organization, “Statement of guidance for high-resolution numerical weather prediction (NWP).” WMO, p. 10, 2018spa
dc.relation.referencesA. L. Houston, L. M. PytlikZillig, and J. C. Walther, “National weather service data needs for short-term forecasts and the role of unmanned aircraft in filling the gap: Results from a nationwide survey,” Bull. Am. Meteorol. Soc., vol. 102, no. 11, pp. E2106–E2120, Nov. 2021, doi: 10.1175/BAMS-D-20-0183.1spa
dc.relation.referencesUniversity of Wyoming, “University of Wyoming - Radiosonde Data,” 2022. http://weather.uwyo.edu/cgi-bin/sounding?region=samer&TYPE=TEXT%3ALIST&YEAR=2021&MONTH=08&FROM=1212&TO=1012&STNM=80222spa
dc.relation.referencesInstituto de Hidrología Meteorología y Estudios Ambientales, “RADIOSONDEOS COLOMBIA - IDEAM,” 2022. http://www.meteoaeronautica.gov.co/radiosondeos-colombiaspa
dc.relation.referencesS. A. H. Mohsan, M. A. Khan, F. Noor, I. Ullah, and M. H. Alsharif, “Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review,” Drones, vol. 6, no. 6, p. 147, Jun. 2022, doi: 10.3390/drones6060147spa
dc.relation.referencesT. Watai, T. Machida, N. Ishizaki, and G. Inoue, “A lightweight observation system for atmospheric carbon dioxide concentration using a small unmanned aerial vehicle,” J. Atmos. Ocean. Technol., vol. 23, no. 5, pp. 700–710, 2006, doi: 10.1175/JTECH1866.1spa
dc.relation.referencesV. Lambey and A. D. Prasad, “A Review on Air Quality Measurement Using an Unmanned Aerial Vehicle,” Water, Air, and Soil Pollution, vol. 232, no. 3. 2021, doi: 10.1007/s11270-020-04973-5spa
dc.relation.referencesR. Cichowicz and M. Dobrzański, “Spatial analysis (Measurements at heights of 10 m and 20 m above ground level) of the concentrations of particulate matter (PM10, PM2.5, and PM1.0) and gaseous pollutants (H2s) on the university campus: A case study,” Atmosphere (Basel)., vol. 12, no. 1, p. 62, Jan. 2021, doi: 10.3390/atmos12010062spa
dc.relation.referencesL. Jin et al., “Unmanned aerial vehicle observations of the vertical distribution of particulate matter in the surface layer of the Taklimakan Desert in China,” Atmosphere (Basel)., vol. 11, no. 9, p. 980, Sep. 2020, doi: 10.3390/atmos11090980spa
dc.relation.referencesT. Wang et al., “Unmanned aerial vehicle-borne sensor system for atmosphere-particulate-matter measurements: Design and experiments,” Sensors (Switzerland), vol. 20, no. 1, 2020, doi: 10.3390/s20010057spa
dc.relation.referencesC. Liu et al., “Vertical distribution of PM2.5 and interactions with the atmospheric boundary layer during the development stage of a heavy haze pollution event,” Sci. Total Environ., vol. 704, 2020, doi: 10.1016/j.scitotenv.2019.135329.spa
dc.relation.referencesG. Rohi, O. Ejofodomi, and G. Ofualagba, “Autonomous monitoring, analysis, and countering of air pollution using environmental drones,” Heliyon, vol. 6, no. 1, Jan. 2020, doi: 10.1016/j.heliyon.2020.e03252spa
dc.relation.referencesB. Li et al., “Use of Multi-Rotor Unmanned Aerial Vehicles for Fine-Grained Roadside Air Pollution Monitoring,” Transp. Res. Rec., vol. 2673, no. 7, pp. 169–180, 2019, doi: 10.1177/0361198119847991spa
dc.relation.referencesQ. Gu and C. Jia, “A Consumer UAV-based Air Quality Monitoring System for Smart Cities,” Mar. 2019, doi: 10.1109/ICCE.2019.8662050spa
dc.relation.referencesD. Wang, Z. Wang, Z. R. Peng, and D. Wang, “Using unmanned aerial vehicle to investigate the vertical distribution of fine particulate matter,” Int. J. Environ. Sci. Technol., vol. 17, no. 1, pp. 219–230, Jan. 2020, doi: 10.1007/s13762-019-02449-6spa
dc.relation.referencesY. Zhu et al., “Measurements of atmospheric aerosol vertical distribution above North China Plain using hexacopter,” Sci. Total Environ., vol. 665, pp. 1095–1102, 2019, doi: 10.1016/j.scitotenv.2019.02.100spa
dc.relation.referencesG. P. Mayuga, C. Favila, C. Oppus, E. Macatulad, and L. H. Lim, “Airborne Particulate Matter Monitoring Using UAVs for Smart Cities and Urban Areas,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2018-Octob, no. October, pp. 1398–1402, 2019, doi: 10.1109/TENCON.2018.8650293spa
dc.relation.referencesL. Y. Chen, H. S. Huang, C. J. Wu, Y. T. Tsai, and Y. S. Chang, “A LoRa-Based Air Quality Monitor on Unmanned Aerial Vehicle for Smart City,” Nov. 2018, doi: 10.1109/ICSSE.2018.8519967.spa
dc.relation.referencesX. B. Li, D. S. Wang, Q. C. Lu, Z. R. Peng, and Z. Y. Wang, “Investigating vertical distribution patterns of lower tropospheric PM2.5 using unmanned aerial vehicle measurements,” Atmos. Environ., vol. 173, pp. 62–71, 2018, doi: 10.1016/j.atmosenv.2017.11.009spa
dc.relation.referencesJ. B. Babaan, J. P. Ballori, A. M. Tamondong, R. V. Ramos, and P. M. Ostrea, “Estimation of PM 2.5 vertical distribution using customized UAV and mobile sensors in Brgy. UP Campus, Diliman, Quezon City,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Oct. 2018, vol. 42, no. 4/W9, pp. 89–103, doi: 10.5194/isprs-archives-XLII-4-W9-89-2018.spa
dc.relation.referencesS. M. Krishna, M. Gangadhar, and C. V. K. Rao, “Ambient air quality monitoring PM2.5 with quadcopter in rajam town of srikakulam district of andhra pradesh,” Int. J. Mech. Eng. Technol., vol. 9, no. 4, pp. 780–785, 2018, Accessed: May 30, 2021spa
dc.relation.referencesT. F. Villa, E. R. Jayaratne, L. F. Gonzalez, and L. Morawska, “Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle,” Environ. Pollut., vol. 230, pp. 134–142, 2017, doi: 10.1016/j.envpol.2017.06.033spa
dc.relation.referencesS. Qiu, B. Chen, R. Wang, Z. Zhu, Y. Wang, and X. Qiu, “Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation,” RSC Adv., vol. 7, no. 63, pp. 39726–39738, Aug. 2017, doi: 10.1039/c7ra05637k.spa
dc.relation.referencesM. Alvarado, F. Gonzalez, P. Erskine, D. Cliff, and D. Heuff, “A methodology to monitor airborne PM10 dust particles using a small unmanned aerial vehicle,” Sensors (Switzerland), vol. 17, no. 2, p. 343, Feb. 2017, doi: 10.3390/s17020343spa
dc.relation.referencesJ. Aurell, W. Mitchell, V. Chirayath, J. Jonsson, D. Tabor, and B. Gullett, “Field determination of multipollutant, open area combustion source emission factors with a hexacopter unmanned aerial vehicle,” Atmos. Environ., vol. 166, pp. 433–440, 2017, doi: 10.1016/j.atmosenv.2017.07.046spa
dc.relation.referencesK. Weber, G. Heweling, C. Fischer, and M. Lange, “The use of an octocopter UAV for the determination of air pollutants--a case study of the traffic induced pollution plume around a river bridge in Duesseldorf, Germany,” Int. J. Environ. Sci., vol. 2, pp. 63–68, 2017spa
dc.relation.referencesY. Yang, Z. Zheng, K. Bian, L. Song, and Z. Han, “Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing,” IEEE Internet Things J., vol. 5, no. 1, pp. 186–198, Feb. 2018, doi: 10.1109/JIOT.2017.2777820spa
dc.relation.referencesJ. M. Brady, M. D. Stokes, J. Bonnardel, and T. H. Bertram, “Characterization of a Quadrotor Unmanned Aircraft System for Aerosol-Particle-Concentration Measurements,” Environ. Sci. Technol., vol. 50, no. 3, pp. 1376–1383, Feb. 2016, doi: 10.1021/acs.est.5b05320spa
dc.relation.referencesM. Alvarado, F. Gonzalez, A. Fletcher, and A. Doshi, “Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open-pit mine sites,” Sensors (Switzerland), vol. 15, no. 8, pp. 19667–19687, 2015, doi: 10.3390/s150819667spa
dc.relation.referencesB. Altstädter et al., “ALADINA - An unmanned research aircraft for observing vertical and horizontal distributions of ultrafine particles within the atmospheric boundary layer,” Atmos. Meas. Tech., vol. 8, no. 4, pp. 1627–1639, Apr. 2015, doi: 10.5194/amt-8-1627-2015spa
dc.relation.referencesW. A. Harrison, D. J. Lary, B. J. Nathan, and A. G. Moore, “Using remote control aerial vehicles to study variability of airborne particulates,” Air, Soil Water Res., vol. 8, no. 8, pp. 43–51, Aug. 2015, doi: 10.4137/ASWR.S30774spa
dc.relation.referencesC. C. Peng and C. Y. Hsu, “Integration of an unmanned vehicle and its application to real-time gas detection and monitoring,” in 2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015, Aug. 2015, pp. 320–321, doi: 10.1109/ICCE-TW.2015.7216921.spa
dc.relation.referencesP. Haas, C. Balistreri, P. Pontelandolfo, and G. Triscone, “Development of an unmanned aerial vehicle UAV for air quality measurements in urban areas,” Am. Inst. Aeronaut. Astronaut., no. June, pp. 1–9, 2014, doi: https://doi.org/10.2514/6.2014-2272spa
dc.relation.referencesT. S. Bates et al., “Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway, using unmanned aerial systems (UAS),” Atmos. Meas. Tech., vol. 6, no. 8, pp. 2115–2120, 2013, doi: 10.5194/amt-6-2115-2013spa
dc.relation.referencesT. F. Villa, F. Salimi, K. Morton, L. Morawska, and F. Gonzalez, “Development and validation of a UAV based system for air pollution measurements,” Sensors (Switzerland), vol. 16, no. 12, p. 2202, Dec. 2016, doi: 10.3390/s16122202spa
dc.relation.referencesL. Barbieri et al., “Intercomparison of small unmanned aircraft system (sUAS) measurements for atmospheric science during the LAPSE-RATE campaign,” Sensors (Switzerland), vol. 19, no. 9, p. 2179, May 2019, doi: 10.3390/s19092179spa
dc.relation.referencesB. R. Greene, A. R. Segales, T. M. Bell, E. A. Pillar-Little, and P. B. Chilson, “Environmental and sensor integration influences on temperature measurements by rotary-wing unmanned aircraft systems,” Sensors (Switzerland), vol. 19, no. 6, p. 1470, Mar. 2019, doi: 10.3390/s19061470spa
dc.relation.referencesA. L. Houston and J. M. Keeler, “The impact of sensor response and airspeed on the representation of the convective boundary layer and airmass boundaries by small unmanned aircraft systems,” J. Atmos. Ocean. Technol., vol. 35, no. 8, pp. 1687–1699, Aug. 2018, doi: 10.1175/JTECH-D-18-0019.1spa
dc.relation.referencesDNP, “Calidad del Aire. Una prioridad de Politica Publica en Colombia,” in Calidad del aire una prioridad de politica publica en Colombia, 2018, p. 69, [Online]. Available: https://www.dnp.gov.co/Portals/0/archivos/documentos/Subdireccion/Conpes/3582.pdf.spa
dc.relation.referencesSecretaria Distrital de Ambiente, “INFORME MENSUAL DE LA RED DE MONITOREO DE CALIDAD DEL AIRE DE BOGOTÁ-RMCAB ENERO 2022,” 2022spa
dc.relation.referencesCorporación Autónoma Regional de Cundinamarca, “BOLETÍN MENSUAL DE CALIDAD DEL AIRE CAR,” Bogotá, 2022. Accessed: Jul. 10, 2022spa
dc.relation.referencesR. Duvall et al., Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Sites, Non-Regulatory Supplemental and Informational Monitoring Applications. EPA, 2021spa
dc.relation.referencesR. Williams et al., “Peer Review and Supporting Literature Review of Air Sensor Technology Performance Targets,” no. September, p. 33, 2018, [Online]. Available: https://www.epa.gov/sites/production/files/2018-10/documents/peer_review_and_supporting_literature_review_of_air_sensor_technology_performance_targets.pdfspa
dc.relation.referencesCorporación Autónoma Regional de Cundinamarca, “Histórico de series hidrometeorológicas | CAR,” 2018spa
dc.relation.referencesDJI, “Matrice 600 Pro - User Manual,” 2022.spa
dc.relation.referencesMatrice 600 Pro, “Matrice 600 Pro - Product Information - DJI,” 2021. https://www.dji.com/matrice600/info#downloadsspa
dc.relation.referencesW. S. Cleveland, E. Grosse, and W. M. Shyu, “Local Regression Models,” in Statistical Models in S, 1st Editio., T. J. H. John M. Chambers, Ed. New York: Routledge, 1992spa
dc.relation.references“Numerical Differentiation Introduction in R.” https://rstudio-pubs-static.s3.amazonaws.com/295650_406b32cdd2d34ca3a150bbe95010d665.htmlspa
dc.relation.referencesUS EPA, “List of designated reference and equivalent methods. United State Environmental Protection Agency, available at: http://www.epa.gov/ttn/amtic/files/ambient/criteria/reference-equivalent-methods-list.pdf,” 2014spa
dc.relation.referencesL. Wang, M. Xu, Q. Hou, Z. Wang, Y. Lan, and S. Wang, “Numerical verification on influence of multi-feature parameters to the downwash airflow field and operation effect of a six-rotor agricultural UAV in flight,” Comput. Electron. Agric., vol. 190, p. 106425, Nov. 2021, doi: 10.1016/j.compag.2021.106425spa
dc.relation.referencesQ. Guo et al., “CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover,” Comput. Electron. Agric., vol. 172, May 2020, doi: 10.1016/j.compag.2020.105343spa
dc.relation.referencesP. P. Neumann and M. Bartholmai, “Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit,” Sensors Actuators, A Phys., vol. 235, pp. 300–310, Nov. 2015, doi: 10.1016/j.sna.2015.09.036spa
dc.relation.referencesL. N. C. Sikkel, G. C. H. E. De Croon, C. De Wagter, and Q. P. Chu, “A novel online model-based wind estimation approach for quadrotor micro air vehicles using low cost MEMS IMUs,” in IEEE International Conference on Intelligent Robots and Systems, Nov. 2016, vol. 2016-Novem, pp. 2141–2146, doi: 10.1109/IROS.2016.7759336spa
dc.relation.referencesY. Qu, Z. Xing, Y. Zhang, and Z. Yu, “Real-time wind vector estimation for a micro UAV,” in 2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017, Jul. 2017, pp. 1716–1721, doi: 10.1109/ICUAS.2017.7991356spa
dc.relation.referencesM. Simma, H. Mjøen, and T. Boström, “Measuring wind speed using the internal stabilization system of a quadrotor drone,” Drones, vol. 4, no. 2. Multidisciplinary Digital Publishing Institute, pp. 1–10, Jun. 16, 2020, doi: 10.3390/drones4020023spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/spa
dc.subject.ddc550 - Ciencias de la tierraspa
dc.subject.lembCALIDAD DEL AIREspa
dc.subject.lembAir qualityenge
dc.subject.lembCONTAMINACION DEL AIRE-CONTROL INDUSTRIALspa
dc.subject.lembCONTROL DE CALIDAD DEL AIREspa
dc.subject.lembAir quality managementeng
dc.subject.proposalSensores de bajo costospa
dc.subject.proposalPM2.5
dc.subject.proposalDroneeng
dc.subject.proposalPerfiles verticalesspa
dc.subject.proposalLow cost sensorseng
dc.subject.proposalVertical Profileseng
dc.titleMedición de perfiles verticales de material particulado en la Sabana de Bogotáspa
dc.title.translatedMeasurement of vertical profiles of particulate matter in the Bogotá Savannaeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
1030598762.2022.pdf
Tamaño:
4.44 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería - Ingeniería Ambiental
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Maestría en Ingeniería - Ambiental