Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos

En este artículo se presenta un modelo geoestadístico para caracterizar espacialmente el comportamiento del contaminante PM10 en la ciudad de Medellín Colombia. Los datos se han tomado de nueve sitios de monitoreo en valor promedio mensual (µg/m3) durante el periodo enero 2003 a diciembre 2007. Se e...

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Autores:: Londoño Ciro, Libardo Antonio
Cañón Barriga, Julio Eduardo
Villada Flórez, Rubén Darío
López Ceballos, Lina Yohana

Tipo de recurso:: Article of journal

Fecha de publicación:: 2015

Institución:: Universidad de San Buenaventura

Repositorio:: Repositorio USB

Idioma:: spa

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dc.title.spa.fl_str_mv	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
dc.title.translated.eng.fl_str_mv	Spatial characterization of pm10 in Medellín Colombia by geostatistical models
title	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
spellingShingle	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos Calidad del aire material particulado geoestadística
title_short	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
title_full	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
title_fullStr	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
title_full_unstemmed	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
title_sort	Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos
dc.creator.fl_str_mv	Londoño Ciro, Libardo Antonio Cañón Barriga, Julio Eduardo Villada Flórez, Rubén Darío López Ceballos, Lina Yohana
dc.contributor.author.spa.fl_str_mv	Londoño Ciro, Libardo Antonio Cañón Barriga, Julio Eduardo Villada Flórez, Rubén Darío López Ceballos, Lina Yohana
dc.subject.spa.fl_str_mv	Calidad del aire material particulado geoestadística
topic	Calidad del aire material particulado geoestadística
description	En este artículo se presenta un modelo geoestadístico para caracterizar espacialmente el comportamiento del contaminante PM10 en la ciudad de Medellín Colombia. Los datos se han tomado de nueve sitios de monitoreo en valor promedio mensual (µg/m3) durante el periodo enero 2003 a diciembre 2007. Se evaluaron diferentes modelos mediante pruebas de validación cruzada. El mejor modelo es el j-bessel. Se calculan los parámetros del modelo mediante pruebas ANOVA para agrupaciones trimestrales. Con Kriging ordinario y sistemas de información geográfica, se obtienen mapas de caracterización espacial del contaminante.
publishDate	2015
dc.date.accessioned.none.fl_str_mv	2015-12-30T00:00:00Z 2025-08-21T22:04:17Z
dc.date.available.none.fl_str_mv	2015-12-30T00:00:00Z 2025-08-21T22:04:17Z
dc.date.issued.none.fl_str_mv	2015-12-30
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dc.relation.references.spa.fl_str_mv	. N. Hamm, A. Finley, M. Schaap and A. Stein, “A spatially varying coefficient model for mapping PM10 air quality at the European scale,” Atmospheric Environment, Vol. 102, pp. 393-405, 2015. [2]. Z. Sun, X. An, Y. Tao and Q. Hou, “Assessment of population exposure to PM10 for respiratory disease in Lanzhou (China) and its health-related economic costs based on GIS,” BMC Public Health, Vol. 13, No 1, pp. 891-900, 2013. [3]. J. Li and D. Heap, “A. Spatial interpolation methods applied in the environmental sciences: A review,” Environmental Modelling & Software, Vol. 53, pp. 173-189, 2014. [4]. S. Young, J. Tullis and J. Cothren, “A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus,” Applied Geography, No 45, pp. 241-249, 2013. [5]. M. J. Bechle, D. B. Millet and J. D. Marshall, “Remote sensing of exposure to NO2: satellite versus ground based measurement in a large urban area,” Atmospheric Environment, Vol. 69, pp. 345-353, 2013. [6]. J. Lorenzo, G. Aviles, J. Mondejar and M. Vargas, “A spatio-temporal geostatistical approach to predicting pollution levels: The case of mono-nitrogen oxides in Madrid,” Computers, Environment and Urban Systems, No 37, pp. 95-106, 2013. [7]. M. Rooney, R. Arku, K. Dionisio, C. Paciorek, A. Friedman, H. Carmichael, Z. Zhou, A. Hughes, J. Vallarino, S. Agyei-Mensah, J. Spengler and M. Ezzati, “Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana,” Science of the Total Environment, Vol. 435-436, pp 107-114, 2012. [8]. D. Rojas-Avellaneda, “Spatial interpolation techniques for estimating levels of pollutant concentrations in the atmosphere,” Rev. mex. is, Vol.53, No.6, pp.447-454, 2007. [9]. H. Merbitz, S. Fritz and C. Schneider, “Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area,” Sci. Total Environ, Vol. 438, pp. 389-403, 2012. [10]. G. Hoek, K. Meliefste, J. Cyrys, M. Lewné, T. Bellander, M. Brauer and B. Brunekreef, “Spatial variability of fine particle concentrations in three European areas,” Atmospheric Environment, Vol. 36, No 25, pp. 4077-4088, 2002. [11]. G. Righini, A. Cappelletti, C. Cremona, A. Piersanti, L. Vitali and L. Ciancarella, “GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data,” Atmospheric Environment, No 97, pp. 121-129, 2014. [12]. D. Dominick, H. Juahir, M. Latif, S. Zain and A. Aris, “Spatial assessment of air quality patterns in Malaysia using multivariate analysis,” Atmospheric Environment, No 60, pp. 172-181, 2012. [13]. E. Gramsch, F. Cereceda-Balic, P Oyola and D. Von Baer, “Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and ozone data,” Atmospheric Environment, Vol. 40, No 28, pp. 5464-5475, 2006. [14]. C. Silva, L. Firinguetti, and A. Trier, “Contaminación ambiental por partículas en suspensión: Modelamiento estadístico,” Actas XXI Jornadas Nacionales de Estadística. Concepción, Chile, 1994. [15]. P. Pérez and J. Reyes, “Prediction of maximun of 24h average of PM10 concentrations 30h in advance in Santiago, Chile,” Atmospheric Environment, Vol. 36, pp. 4555-4561, 2002. [16]. C. Silva, P. Pérez and A. Trier, “Statistical modelling and prediction of atmospheric pollution by particulate materia: two nonparametric approaches,” Environmetrics, Vol. 12, pp. 147-159, 2001. [17]. J. Huertas, M. Huertas, S. Izquierdo and E. Gonzales, “Air quality impact assessment of multiple open pit coal mines in northern Colombia,” Journal of Environmental Management, Vol. 93, No 1, pp. 121-129, 2012. [18]. E. Dons, M. Van Poppe, L. Panis, S. De Prins, P. Berghmans, G. Koppen and C. Matheeussen, “Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution,” Science of the total Environment, Vol. 476–477, pp. 378-386, 2014. [19]. M. Beauchamp, L. Malherbe and C. De Fouquet, “A pragmatic approach to estimate the number of days in exceedance of PM10 limit value,” Atmospheric Environment, Vol. 111, pp. Pages 79-93, 2015. [20].R. Webster and M. Oliver, Geostatistics for Environmental Scientists. England: John Wiley & Sons Ltd, 2007, p. 19-20. [21]. R. Bilonik, “A. Risk qualified maps of hydrogen ion concentration for the New York state area for 1966 – 1978,” Atmospheric Environment, Vol. 17, pp. 2513-2524, 1983. [22]. L. Londoño and J. Cañón, “Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks,” Revista Facultad de Ingeniería Universidad de Antioquia, No. 76, to be published, 2015. [23]. A. Pollice, and G. Jona Lasinio, “Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network,” Journal of Data Science, Vol. 7, pp. 43-59, 2009. [24]. M. Quiroz, D. Martínez, H. Massone, L. Londoño and C. Dapeña, “Spatial distribution of electrical conductivity and stable isotopes in groundwater in large catchments: a geostatistical approach in the Quequén Grande River catchment, Argentina,” Isotopes in Environmental and Health Studies, Jul. 2015. [Online]. Available: http://dx.doi.org/10.1080/10256016.2015.1056740. [25]. I. Villada and L. Londoño, “Aplicación de métodos geoestadísticos para la caracterización de la calidad química de un depósito de material calcáreo,” Revista Boletín Ciencias de la Tierra, No. 35, pp. 15-23, 2014. [26]. K. Johnston, J. Verhoef, K. Krivoruchko and N. Lucas, ArcGIS Geostatistical Analyst Tutorial. USA: ESRI, 2003, pp. 256-258.
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spelling	Londoño Ciro, Libardo AntonioCañón Barriga, Julio EduardoVillada Flórez, Rubén DaríoLópez Ceballos, Lina Yohana2015-12-30T00:00:00Z2025-08-21T22:04:17Z2015-12-30T00:00:00Z2025-08-21T22:04:17Z2015-12-30En este artículo se presenta un modelo geoestadístico para caracterizar espacialmente el comportamiento del contaminante PM10 en la ciudad de Medellín Colombia. Los datos se han tomado de nueve sitios de monitoreo en valor promedio mensual (µg/m3) durante el periodo enero 2003 a diciembre 2007. Se evaluaron diferentes modelos mediante pruebas de validación cruzada. El mejor modelo es el j-bessel. Se calculan los parámetros del modelo mediante pruebas ANOVA para agrupaciones trimestrales. Con Kriging ordinario y sistemas de información geográfica, se obtienen mapas de caracterización espacial del contaminante.In this article a geostatistical model is presented in order to spatially characterize the PM10 pollutant behavior in the city of Medellin, Colombia. The data has been taken from nine monitoring locations in monthly average value (µg/m3) for the period from January 2003 to December 2007. Different models were evaluated by cross-validation tests. The best model is a j-bessel. The model parameters are calculated using ANOVA tests for quarterly groupings. Maps of the pollutant’s spatial characterization are obtained with ordinary Kriging and GIS.application/pdf10.21500/20275846.17282027-5846https://hdl.handle.net/10819/27343https://doi.org/10.21500/20275846.1728spaUniversidad San Buenaventura - USB (Colombia)https://revistas.usb.edu.co/index.php/IngUSBmed/article/download/1728/1501Núm. 2 , Año 2015 : Ingenierías USBMed352266Ingenierías USBMed. N. Hamm, A. Finley, M. Schaap and A. Stein, “A spatially varying coefficient model for mapping PM10 air quality at the European scale,” Atmospheric Environment, Vol. 102, pp. 393-405, 2015. [2]. Z. Sun, X. An, Y. Tao and Q. Hou, “Assessment of population exposure to PM10 for respiratory disease in Lanzhou (China) and its health-related economic costs based on GIS,” BMC Public Health, Vol. 13, No 1, pp. 891-900, 2013. [3]. J. Li and D. Heap, “A. Spatial interpolation methods applied in the environmental sciences: A review,” Environmental Modelling & Software, Vol. 53, pp. 173-189, 2014. [4]. S. Young, J. Tullis and J. Cothren, “A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus,” Applied Geography, No 45, pp. 241-249, 2013. [5]. M. J. Bechle, D. B. Millet and J. D. Marshall, “Remote sensing of exposure to NO2: satellite versus ground based measurement in a large urban area,” Atmospheric Environment, Vol. 69, pp. 345-353, 2013. [6]. J. Lorenzo, G. Aviles, J. Mondejar and M. Vargas, “A spatio-temporal geostatistical approach to predicting pollution levels: The case of mono-nitrogen oxides in Madrid,” Computers, Environment and Urban Systems, No 37, pp. 95-106, 2013. [7]. M. Rooney, R. Arku, K. Dionisio, C. Paciorek, A. Friedman, H. Carmichael, Z. Zhou, A. Hughes, J. Vallarino, S. Agyei-Mensah, J. Spengler and M. Ezzati, “Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana,” Science of the Total Environment, Vol. 435-436, pp 107-114, 2012. [8]. D. Rojas-Avellaneda, “Spatial interpolation techniques for estimating levels of pollutant concentrations in the atmosphere,” Rev. mex. is, Vol.53, No.6, pp.447-454, 2007. [9]. H. Merbitz, S. Fritz and C. Schneider, “Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area,” Sci. Total Environ, Vol. 438, pp. 389-403, 2012. [10]. G. Hoek, K. Meliefste, J. Cyrys, M. Lewné, T. Bellander, M. Brauer and B. Brunekreef, “Spatial variability of fine particle concentrations in three European areas,” Atmospheric Environment, Vol. 36, No 25, pp. 4077-4088, 2002. [11]. G. Righini, A. Cappelletti, C. Cremona, A. Piersanti, L. Vitali and L. Ciancarella, “GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data,” Atmospheric Environment, No 97, pp. 121-129, 2014. [12]. D. Dominick, H. Juahir, M. Latif, S. Zain and A. Aris, “Spatial assessment of air quality patterns in Malaysia using multivariate analysis,” Atmospheric Environment, No 60, pp. 172-181, 2012. [13]. E. Gramsch, F. Cereceda-Balic, P Oyola and D. Von Baer, “Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and ozone data,” Atmospheric Environment, Vol. 40, No 28, pp. 5464-5475, 2006. [14]. C. Silva, L. Firinguetti, and A. Trier, “Contaminación ambiental por partículas en suspensión: Modelamiento estadístico,” Actas XXI Jornadas Nacionales de Estadística. Concepción, Chile, 1994. [15]. P. Pérez and J. Reyes, “Prediction of maximun of 24h average of PM10 concentrations 30h in advance in Santiago, Chile,” Atmospheric Environment, Vol. 36, pp. 4555-4561, 2002. [16]. C. Silva, P. Pérez and A. Trier, “Statistical modelling and prediction of atmospheric pollution by particulate materia: two nonparametric approaches,” Environmetrics, Vol. 12, pp. 147-159, 2001. [17]. J. Huertas, M. Huertas, S. Izquierdo and E. Gonzales, “Air quality impact assessment of multiple open pit coal mines in northern Colombia,” Journal of Environmental Management, Vol. 93, No 1, pp. 121-129, 2012. [18]. E. Dons, M. Van Poppe, L. Panis, S. De Prins, P. Berghmans, G. Koppen and C. Matheeussen, “Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution,” Science of the total Environment, Vol. 476–477, pp. 378-386, 2014. [19]. M. Beauchamp, L. Malherbe and C. De Fouquet, “A pragmatic approach to estimate the number of days in exceedance of PM10 limit value,” Atmospheric Environment, Vol. 111, pp. Pages 79-93, 2015. [20].R. Webster and M. Oliver, Geostatistics for Environmental Scientists. England: John Wiley & Sons Ltd, 2007, p. 19-20. [21]. R. Bilonik, “A. Risk qualified maps of hydrogen ion concentration for the New York state area for 1966 – 1978,” Atmospheric Environment, Vol. 17, pp. 2513-2524, 1983. [22]. L. Londoño and J. Cañón, “Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks,” Revista Facultad de Ingeniería Universidad de Antioquia, No. 76, to be published, 2015. [23]. A. Pollice, and G. Jona Lasinio, “Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network,” Journal of Data Science, Vol. 7, pp. 43-59, 2009. [24]. M. Quiroz, D. Martínez, H. Massone, L. Londoño and C. Dapeña, “Spatial distribution of electrical conductivity and stable isotopes in groundwater in large catchments: a geostatistical approach in the Quequén Grande River catchment, Argentina,” Isotopes in Environmental and Health Studies, Jul. 2015. [Online]. Available: http://dx.doi.org/10.1080/10256016.2015.1056740. [25]. I. Villada and L. Londoño, “Aplicación de métodos geoestadísticos para la caracterización de la calidad química de un depósito de material calcáreo,” Revista Boletín Ciencias de la Tierra, No. 35, pp. 15-23, 2014. [26]. K. Johnston, J. Verhoef, K. Krivoruchko and N. Lucas, ArcGIS Geostatistical Analyst Tutorial. USA: ESRI, 2003, pp. 256-258.Ingenierías USBmed - 2015info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/https://revistas.usb.edu.co/index.php/IngUSBmed/article/view/1728Calidad del airematerial particuladogeoestadísticaCaracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticosSpatial characterization of pm10 in Medellín Colombia by geostatistical modelsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2753https://bibliotecadigital.usb.edu.co/bitstreams/2cd4e313-83ad-407e-9465-77c645bedd34/downloadee842fcd8864ca6214ac2651011d4353MD5110819/27343oai:bibliotecadigital.usb.edu.co:10819/273432025-08-21 17:04:17.772https://creativecommons.org/licenses/by-nc-sa/4.0/https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.com

Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos

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