SciELO - Scientific Electronic Library Online

 
vol.34 issue2Using user models in Matlab® within the Aspen Plus® interface with an Excel® linkActive pre-filters for dc/dc Boost regulators author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Ingeniería e Investigación

Print version ISSN 0120-5609

Abstract

DIEZ, S; BARRA, E; CRESPO, F  and  BRITCH, J. Uncertainty propagation of meteorological and emission data in modeling pollutant dispersion in the atmosphere. Ing. Investig. [online]. 2014, vol.34, n.2, pp.44-48. ISSN 0120-5609.  http://dx.doi.org/10.15446/ing.investig.v34n2.40596.

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.

Keywords : Uncertainty; Variability; Monte Carlo; PM10.

        · abstract in Spanish     · text in English     · English ( pdf )