Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area

Kumar, Ashok; Dixit, Shobhit; Varadarajan, Charanya; Vijayan, Abhilash; Masuraha, Anand

doi:10.1002/ep.10129

Cited by 63 publications

(51 citation statements)

References 7 publications

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“…The factor of 2 statistic describes the percentage of data where model-predicted concentrations are within a factor of 2 of observed data. This value ranges from 14 to 43% across the six sites, lower than the 50-80% requirement for a good model (Chang and Hanna, 2004;Kumar et al, 2006).…”

Section: Scientific Model Validationmentioning

confidence: 72%

“…The unfilled circle corresponds to the population mean with error bars representing the 95% confidence interval around the mean. The shaded area represents the range of values acceptable for a "good model," as defined by Chang and Hanna (2004) and Kumar et al (2006). performance, as even small instrumentation errors or shifts in wind direction can cause a spatial displacement between model predictions and observations.…”

Section: Discussionmentioning

confidence: 99%

“…Data were examined in subsets by wind speed and atmospheric stability (according to Monin-Obukhov length) to examine model performance under various meteorological conditions. Hourly performance measures for these data sets are then compared to what previous investigators consider to be "acceptable" model performance measures (Chang & Hanna, 2004;Kumar et al, 2006). AERMOD v12060 with regulatory default assumptions was initially used to model the two sites, but since this preliminary study, the EPA released an updated version of the model.…”

Section: Introductionmentioning

confidence: 99%

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AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

Frost

2013

Journal of the Air & Waste Management Association

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An evaluation of the steady-state dispersion model AERMOD was conducted to determine its accuracy at predicting hourly ground-level concentrations of sulfur dioxide (SO 2 ) by comparing model-predicted concentrations to a full year of monitored SO 2 data. The two study sites are comprised of three coal-fired electrical generating units (EGUs) located in southwest Indiana. The sites are characterized by tall, buoyant stacks, flat terrain, multiple SO 2 monitors, and relatively isolated locations. AERMOD v12060 and AERMOD v12345 with BETA options were evaluated at each study site. For the six monitor-receptor pairs evaluated, AERMOD showed generally good agreement with monitor values for the hourly 99th percentile SO 2 design value, with design value ratios that ranged from 0.92 to 1.99. AERMOD was within acceptable performance limits for the Robust Highest Concentration (RHC) statistic (RHC ratios ranged from 0.54 to 1.71) at all six monitors. Analysis of the top 5% of hourly concentrations at the six monitor-receptor sites, paired in time and space, indicated poor model performance in the upper concentration range. The amount of hourly model predicted data that was within a factor of 2 of observations at these higher concentrations ranged from 14 to 43% over the six sites. Analysis of subsets of data showed consistent overprediction during low wind speed and unstable meteorological conditions, and underprediction during stable, low wind conditions. Hourly paired comparisons represent a stringent measure of model performance; however, given the potential for application of hourly model predictions to the SO 2 NAAQS design value, this may be appropriate. At these two sites, AERMOD v12345 BETA options do not improve model performance.Implications: A regulatory evaluation of AERMOD utilizing quantile-quantile (Q-Q) plots, the RHC statistic, and 99th percentile design value concentrations indicates that model performance is acceptable according to widely accepted regulatory performance limits. However, a scientific evaluation examining hourly paired monitor and model values at concentrations of interest indicates overprediction and underprediction bias that is outside of acceptable model performance measures. Overprediction of 1-hr SO 2 concentrations by AERMOD presents major ramifications for state and local permitting authorities when establishing emission limits.

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Section: Scientific Model Validationmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

Frost

2013

Journal of the Air & Waste Management Association

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“…For air quality modelling for various perspectives with measured values of concentration, AERMOD has been applied. AERMOD was used for different time scales which could help chronical exposure assessment in epidemiological studies (Kumar et al, 2006;Zou et al, 2010). Kumar et al (2006) used AERMOD to predict concentration for the 1-h, 3-h and 24-h averaging period for Lucas county, Ohio, USA.…”

Section: Application Of Aermod For Impact Assessmentmentioning

confidence: 99%

Application of WRF Model for Air Quality Modelling and AERMOD - A Survey

Kumar

Patil

Dikshit

et al. 2017

Aerosol Air Qual. Res.

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Meteorology plays a crucial role in air quality. The presence of uncertainties of a significant nature in the meteorological profile used during air quality model simulation has the potential to affect negatively the results of the simulations. This paper describes a most recent version of the meteorological model called Weather Research and Forecasting (WRF) model and its importance in air quality. The performance of WRF depends upon the intended application and parameterization scheme of physics options. WRF model is also applied to investigate the simulation results with various land surface models (LSMs) and Planetary Boundary Layer (PBL) parameterizations and various set of microphysics options. It predicts various meteorological spatial parameters like mixing layer height, temperature, humidity, rain fall, cloud cover and wind. The WRF results are integrated with air quality model (AQM) and the AQM depends upon the performance of WRF. It has been applied for evaluation of national pollution control policy, behaviour of plume rise, property of aerosols, prediction of Ozone, SO 2 , NO x , PM 10 , PM 2.5 etc. using AQM for various sources. The effect of topography and different seasons on the concentration of pollutants in the atmosphere has also been studied using AQM. AQM AERMOD has also been reviewed with various other AQM models such as ADMS-Urban and CALPUFF. AERMOD has been used for different time scales, health risk assessment, evaluation of various control strategies, Environmental Impact Assessment (EIA) studies and emission factor estimation. This paper presents the importance of meteorological model to AQM as well as many applications of AQM to demonstrate various scientific questions and policies.

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“…It was found that the shear layer model over predicts the concentration for all windy conditions except for few data points. The statistical evaluation based on the work of Hanna et al (1993), Gudivaka and Kumar (1990), Riswadkar and Kumar (1994) and Kumar et al (2006), was used in this study. In order to determine the significance of the evaluation of the model, four statistical parameters; normalized mean square error (NMSE), fractional bias (FB), correlation coefficient (R), and geometric mean bias (MG) were calculated.…”

Section: Model Evaluation Using Measured Datamentioning

confidence: 99%

Development and Evaluation of a Dispersion Model to Predict Downwind Concentrations of Particulate Emissions from Land Application of Class B Biosolids in Unstable Conditions

Bhat¹,

Kumar²,

Czajkowski³

2011

Indoor and Outdoor Air Pollution

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Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area

Abstract: The AERMOD dispersion model was used to compute ambient air concentrations of SO 2 for 1-, 3-, and 24-h

Cited by 63 publications

References 7 publications

AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

Application of WRF Model for Air Quality Modelling and AERMOD - A Survey

Development and Evaluation of a Dispersion Model to Predict Downwind Concentrations of Particulate Emissions from Land Application of Class B Biosolids in Unstable Conditions

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