Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

Irwin, John S.; Rao, S. Trivikrama; Petersen, William B.; Turner, D.B.

doi:10.1016/0004-6981(87)90153-3

Cited by 32 publications

(13 citation statements)

References 13 publications

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“…We find general agreement with the conclusions reached by Hanna et al (1985) and Wilczak and Phillips (1986), which is not overly encouraging given the impact of this amount of uncertainty on dispersion modeling results. Findings by Irwin et al (1987) suggest that a probable error of 20% in the input values of wind speed, plume rise and standard deviation of vertical and lateral wind velocity fluctuations results in a 40% probable error in the estimates of the location and magnitude of the surface maximum concentration from an elevated point source in a neutral-to-unstable boundary layer.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the dispersive state of convective boundary layers for applied dispersion modeling

Irwin

Paumier

1990

Boundary-Layer Meteorol

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Estimates from semiempirical models that characterize surface heat flux, mixing depth, and profiles of temperature, wind, and turbulence are compared with observations from atmospheric field studies conducted in Colorado, Illinois, Indiana, and Minnesota. Sodar observations are compared with tower measurements at the Colorado site, for wind and turbulence profiles. The median surface heat flux, as calculated using surface-layer flux-profile relationships and an energy budget model, was consistently overestimated by 20 to 80%. Several mixing-depth models were evaluated: (1) integration of the hourly surface heat flux and friction velocity, (2) solving for the time rate of change of profiles of virtual potential temperature, and (3) an interpolation scheme used by the U.S. Environmental Protection Agency in regulatory dispersion models. For the late afternoon, 80 to 90% of the estimates from the first and third models were within 40% of the observed values. For the morning hours after sunrise, all were less accurate. Temperature estimates from surface-layer flux-profile relationships compared well with observations within the mixed layer, but were too low for the inversion layer aloft. Wind profiles were derived using surface-layer flux-profile relationships, a windprofile power-law based on Pasquill stability category, and sodar measurements. The sodar measurements were superior to both types of model estimates. Turbulence profiles were derived from sodar measurements and from semiempirical similarity relationships based on mixing depth and Obukhov length. The scatter in the comparisons with the sodar observations is twice that seen in the comparisons with empirical profile relationships. Overall, it appears that uncertainty of as low as 20 to 30% in the characterization of the diffusion meteorology is the exception rather than the rule.

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Section: Introductionmentioning

confidence: 99%

Characterizing the dispersive state of convective boundary layers for applied dispersion modeling

Irwin

Paumier

1990

Boundary-Layer Meteorol

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“…MCMs have been applied to atmospheric dispersion models ranging from simple Gaussian plume models (KOCHER et al, 1987;IRWIN et al, 1987) to complex photochemical models (e.g., HANNA et al, 1998).…”

Section: Methodsmentioning

confidence: 99%

“…Uncertainties in emission rate and height, wind speed and direction, dispersion parameters, and mixing depth were considered for several stability classes and downwind distances. IRWIN et al (1987) used MCM to relate the error bounds of meteorological data input to a Gaussian plume dispersion model to the uncertainty in the estimates of the maximum concentration and its downwind distance from the source. RAO et al (1985) applied a probabilistic approach to air quality model performance evaluation by determining the model's ability to simulate the tails of the CDF of the observed concentrations.…”

Section: Uncertainty In Regulatory Air Quality Modelingmentioning

confidence: 99%

Uncertainty Analysis in Atmospheric Dispersion Modeling

Rao

2005

Pure appl. geophys.

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The concentration of a pollutant in the atmosphere is a random variable that cannot be predicted accurately, but can be described using quantities such as ensemble mean, variance, and probability distribution. There is growing recognition that the modeled concentrations of hazardous contaminants in the atmosphere should be described in a probabilistic framework. This paper discusses the various types of uncertainties in atmospheric dispersion models, and reviews sensitivity/uncertainty analysis methods to characterize and/or reduce them. Evaluation and quantification of the range of uncertainties in predictions yield a deeper insight into the capabilities and limitations of atmospheric dispersion models, and increase our confidence in decision-making based on models.

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“…On the other hand, only few studies have been conduced to evaluate the source of the errors [19,20] and adjust the regulatory models AERMOD. More stydies should be done to improve the predictability of this model in real cases under averaging times shorter than 10 minutes and in the presence of obstacles.…”

Section: Introductionmentioning

confidence: 99%

An analysis of error propagation in AERMOD lateral dispersion using Round Hill II and Uttenweiller experiments in reduced averaging times

Hoinaski

Franco

Lisboa

2016

Environmental Technology

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Dispersion modelling was proved by researchers that most part of the models, including the regulatory models recommended by the Environmental Protection Agency of the United States (AERMOD and CALPUFF), do not have the ability to predict under complex situations. This article presents a novel evaluation of the propagation of errors in lateral dispersion coefficient of AERMOD with emphasis on estimate of average times under 10 min. The sources of uncertainty evaluated were parameterizations of lateral dispersion ([Formula: see text]), standard deviation of lateral wind speed ([Formula: see text]) and processing of obstacle effect. The model's performance was tested in two field tracer experiments: Round Hill II and Uttenweiller. The results show that error propagation from the estimate of [Formula: see text] directly affects the determination of [Formula: see text], especially in Round Hill II experiment conditions. After average times are reduced, errors arise in the parameterization of [Formula: see text], even after observation assimilations of [Formula: see text], exposing errors on Lagrangian Time Scale parameterization. The assessment of the model in the presence of obstacles shows that the implementation of a plume rise model enhancement algorithm can improve the performance of the AERMOD model. However, these improvements are small when the obstacles have a complex geometry, such as Uttenweiller.

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Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

Cited by 32 publications

References 13 publications

Characterizing the dispersive state of convective boundary layers for applied dispersion modeling

Characterizing the dispersive state of convective boundary layers for applied dispersion modeling

Uncertainty Analysis in Atmospheric Dispersion Modeling

An analysis of error propagation in AERMOD lateral dispersion using Round Hill II and Uttenweiller experiments in reduced averaging times

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