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

Abstract

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.

Development of an Analytical Line Source Dispersion Model to Predict Ground Level Concentrations for Particulate Matter (PM) of Different Particle Size Ranges

“…The surprising result is that the variables related to dispersion coefficients show very little sensitivity. This result is different than the one reported by Harsha and Kumar [16,[25][26][27] using the ASTM method and the Sensitivity-Index method. The analysis performed using Crystal Ball should be re-examined.…”

“…The expressions for the horizontal and vertical dispersion coefficients for stable conditions are given as Equations ( 4) and ( 5), and the expressions for unstable conditions are given as Equations ( 6) and (7). These equations are based on the work of Snyder et al [15] and include an additional term m t to account for the additional vertical spread caused by the vehicular turbulence, as suggested by Madiraju and Kumar [16].…”

“…where u * is the surface friction velocity, U e is the effective wind speed, L is the Monin-Obukhov length, m t is vertical spread due to the turbulence created by the vehicles. m t is computed using the formulation given by Madiraju and Kumar [16]:…”

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

“…Considering pollution source information and meteorological data to simulate air quality is widely used in most studies [54,55]. This study utilized air pollutant concentration and meteorological data from 2017 to 2020 in six Chinese urban agglomerations to simulate air quality conditions [56].…”