Development and evaluation of a ground-level area source analytical dispersion model to predict particulate matter concentration for different particle sizes

Nimmatoori, Praneeth; Kumar, Ashok

doi:10.1016/j.jaerosci.2013.08.014

Cited by 11 publications

(11 citation statements)

References 22 publications

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“…where q is the emission rate of the mobile source per unit length, and γ(s) is the gamma function of s. The value of u 1 is based on the field measurement, and K 1 is computed using Equation (6) used by Rao et al [35] and Nimmatoori and Kumar [36]:…”

Section: Dispersion Modelmentioning

confidence: 99%

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Madiraju¹,

Kumar²

2021

Atmosphere

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Transportation sources are a major contributor to air pollution in urban areas, and the role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model (SLINE 1.0) based on the solution of the convective–diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The dispersion coefficients for stable and unstable atmospheric conditions are based on the near-field parameterization. Initial vertical dispersion coefficient due to the wake effect of mobile sources is incorporated based on a literature review. The model inputs include emission factor, wind speed, wind direction, turbulence parameters, and terrain features. The model is evaluated based on the Idaho Falls field study (2008). The performance of the model is evaluated using several statistical parameters. Results indicate that the model performs well against this dataset in predicting concentrations under both the stable and unstable atmospheric conditions. The sensitivity of the model to compute ground-level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables using the ASTM (American Society for Testing and Materials) method. However, some recalibration of the model constants is needed using several field datasets to make sure that the model is acceptable for computing ground-level concentrations in engineering applications.

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Section: Dispersion Modelmentioning

confidence: 99%

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Madiraju¹,

Kumar²

2021

Atmosphere

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“…The value of is based on the measurement and is computed using the Equation (4) used by Rao et al [37]; Nimmatoori and Kumar et al [38]…”

Section: Dispersion Modelmentioning

confidence: 99%

Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

Madiraju

Kumar

2020

The 3rd International Electronic Conference on Atmospheric Sciences

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Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modelling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e. the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be reexamined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

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“…None of the above studies did not apply the discrete phase method for modeling particulate matter concentrations. The primary focus of previous studies involving particulate matter emissions from biosolid applied locations included developing analytical dispersion models and applying them for predicting particulate matter concentrations at downwind distances [17] [18] [19]. However, none of them involved numerical solutions for modeling the particulate concentrations of different size ranges.…”

Section: Introductionmentioning

confidence: 99%

“…had similar performance for particle sizes 7.5 and 10 which indicate the need for more data points for the statistical analyses. The performances of the turbulence models were compared with the particulate matter deposition (PMD) model [19] which is an analytical area-source dispersion model. The PMD incorporates dry deposition while predicting concentrations for different particle sizes.…”

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confidence: 99%

Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics

Nimmatoori¹,

Kumar²

2021

ACES

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This paper proposes a methodology using computational fluid dynamics (CFD)-FLUENT to simulate the dispersion of particulate matter releasing from a biosolid applied agricultural field and predict the particulate concentrations for different ranges of particle sizes. The discrete phase model (Lagrangian-Eulerian approach) was used in combination with each of the four turbulence models:Standard kε (kε), Realizable kε (Rkε), Standard kω (kω), and Shear-stress transport k-ω (SST) to predict particulate matter size concentrations for distances downwind of the agricultural field. In this modeling approach, particulates were simulated as discrete phase and air as continuous phase. The predicted particulate matter concentrations were compared statistically with their corresponding field study observations to evaluate the performance of turbulence models. The statistical analysis concluded that among four turbulence models, the discrete phase model when used with Rkε performed the best in predicting particulate matter concentrations for low (u < 2 m/s) and medium (2 < u < 5 m/s) wind speeds. For high (u > 5 m/s) wind speeds, Rkε, kω, and SST showed similar performances. The discrete phase model using Rkε performed very well and modeled the best concentrations for the particle sizes (μm): 0.

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Development and evaluation of a ground-level area source analytical dispersion model to predict particulate matter concentration for different particle sizes

Cited by 11 publications

References 22 publications

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics

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