Spatial analysis of exposure to PM 2.5 in the urban area of Bogota between 2010 and 2016

Air pollution in urban areas of the world has become one of the major areas of statistical and spatial analysis in the past decade. Ever since air quality monitoring networks were put in place all over the world, the amount of data available to perform analysis has grown exponentially. In Bogota, th...

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Autores:
Rojas Niño, Nicolás
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2017
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/39635
Acceso en línea:
http://hdl.handle.net/1992/39635
Palabra clave:
Calidad del aire
Análisis espacial (Estadística)
Material particulado
Kriging
Bogotá (Colombia)
Ingeniería
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
Description
Summary:Air pollution in urban areas of the world has become one of the major areas of statistical and spatial analysis in the past decade. Ever since air quality monitoring networks were put in place all over the world, the amount of data available to perform analysis has grown exponentially. In Bogota, the Air Quality Monitoring Network was put in place in 1997, and has recorded data related to pollutant concentrations every year since 1998. With this growing amount of information, there is a surging need to analyze effectively the data, to determine key factors in air quality deterioration and to protect the population from exposure to a bad air quality. There has been a strong effort to analyze Bogota?s data from a strictly statistical approach, however, given the nature of the data, a spatial statistics analysis is also pertinent. This paper proposes a methodology for performing spatial analysis on air quality data for Bogota, using an automated tool that is easy to use for local authorities or the public. The spatial analysis is performed with a Kriging methodology, taking available information from monitoring stations and performing a spatial interpolation of unknown locations of the variable throughout the city