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

Abstract

Propagación de la incertidumbre de los datos meteorológicos y de emisión en el modelado de la dispersión de contaminantes en la atmósfera S. Diez 1 , E. Barra 2 , F. Crespo 3 and J. Britch 4 ABSTRACTVariability 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. RESUMENLa variabilidad es la heterogeneidad real dentro de una población, que no puede ser reducida ni eliminada por más o mejores determinaciones. La incertidumbre representa la ignorancia acerca de un fenómeno pobremente caracterizado, pero que puede reducirse mediante la recopilación de más datos. El objetivo de este trabajo es estimar la concentración de PM10 provocada por las emisiones de una fuente puntual ubicada en Malagueño (Córdoba, Argentina), considerando la variabilidad y la incertidumbre de la meteorología y las emisiones. Para abordar este análisis fue desarrollado un método que utiliza los algoritmos del modelo Industrial Source Complex de la USEPA junto a la metodología Monte Carlo. Con cien mil iteraciones se obtuvo la distribución de concentraciones, encontrándose que la incertidumbre en la dirección del viento es la de mayor incidencia sobre las estimaciones.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

“…Apart from the complexity of environmental processes and interface interactions (Holnicki and Nahorski, 2015;Irwin, 2014), variance in measured and estimated pollutant concentrations where GI is present may be due to: (i) pollutant measurement error; (ii) model input uncertainties (Table 2); (iii) simplification of deposition processes (Section 3); and (iv) difficulties in treating GI in numerical solutions (Sections 4 and 5). Past studies have reported the effects of input data uncertainties on pollutant concentrations at different spatial scales, including: meteorology data uncertainty at mesoscale (Gilliam et al, 2015;Godowitch et al, 2015), emissions data uncertainty at macroscale (Diez et al, 2014;Holnicki and Nahorski, 2015) and at mesoscale (Saikawa et al, 2017), topography and land use data uncertainty at macroscale (Zou et al, 2016), and surface roughness uncertainty at macroscale (Barnes et al, 2014). The challenges to model inputs and processes, especially regarding the consideration of GI in air pollutant concentration simulations, primarily relate to: (i) spatio-temporal variation of GI characteristics such as shape and size, porosity, pollution tolerance and pollutant sink; (ii) pollutant transformation due to GI; and (iii) influences of meteorological and topographical data (such as temperature, humidity, terrain slope, wind speed and direction) on the deposition process.…”