Uncertainty propagation of meteorological and emission data in modeling pollutant dispersion in the atmosphere

Variability is true heterogeneity existing within a population that cannot be reduced or eliminated by more or better determinations. Uncertainty represents ignorance about poorly characterized phenomena, but it can be reduced by collecting more data. The aim of this paper was to study the impact of...

Full description

Autores:
Diez, Sebastian
Barra, Enrique
Crespo, Flavia
Britch, Javier
Tipo de recurso:
Article of journal
Fecha de publicación:
2014
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/48970
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/48970
http://bdigital.unal.edu.co/42427/
Palabra clave:
Uncertainty
Variability
Monte Carlo
PM10
Incertidumbre
Variabilidad
Monte Carlo.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Variability is true heterogeneity existing within a population that cannot be reduced or eliminated by more or better determinations. Uncertainty represents ignorance about poorly characterized phenomena, but it can be reduced by collecting more data. The aim of this paper was to study the impact of the variability and uncertainty of the main variables, i.e., emissions and meteorology, of the PM10 concentration caused by a point source located at Malagueño (Córdoba, Argentina). To perform this analysis, a scheme was developed using the USEPA Industrial Source Complex model algorithms with a Monte Carlo methodology. Using a simulation with one hundred thousand iterations, the concentration distribution was obtained and showed that the uncertainty in wind direction had the greatest impact on the estimates.