Atmospheric dispersion modeling near a roadway under calm meteorological conditions

doi:10.1016/j.trd.2014.10.013

Transportation Research Part D: Transport and Environment

Volume 34, January 2015, Pages 137-154

https://doi.org/10.1016/j.trd.2014.10.013 Get rights and content

Highlights

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We investigate the dispersion of pollutant under calm meteorological situations.
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We estimate vehicle emissions with local traffic data.
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PM resuspension is included based on Positive Matrix Factorization analysis.
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The atmospheric dispersion model is modified and is evaluated using measurements.
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Model performance is satisfactory for PM and PM components.

Abstract

Atmospheric pollutant dispersion near sources is typically simulated by Gaussian models because of their efficient compromise between reasonable accuracy and manageable computational time. However, the standard Gaussian dispersion formula applies downwind of a source under advective conditions with a well-defined wind direction and cannot calculate air pollutant concentrations under calm conditions with fluctuating wind direction and/or upwind of the emission source. Attempts have been made to address atmospheric dispersion under such conditions. This work evaluates the performance of standard and modified Gaussian plume models using measurements of NO₂, PM₁₀, PM_2.5, five inorganic ions and seven metals conducted near a freeway in Grenoble, France, during 11–27 September 2011. The formulation for calm conditions significantly improves model performance. However, it appears that atmospheric dispersion due to vehicle-induced turbulence is still underestimated. Furthermore, model performance is poor for particulate species unless road dust resuspension by traffic is explicitly taken into account.

Introduction

Studies have shown that populations spending large amounts of time near major roadways have an increased incidence and severity of health problems that may be related to air pollution from roadway traffic (Baldauf et al., 2008). Health effects include reduced and impaired lung function, asthma and other respiratory symptoms, cardiovascular effects, low birth weight, cancer, and premature death (e.g., Garshick et al., 2003, Janssen et al., 2002, Gauderman et al., 2005, Heinrich et al., 2005, McConnell et al., 2006, Pirjola et al., 2006). Therefore, it is essential to estimate population exposure near roadways in support of exposure and epidemiological studies as well as for impact studies of future roadway projects. To that end, one needs to select traffic, emission, and air quality models relevant to the given case study. Traffic models can be classified as static or dynamic models according to spatio-temporal scales. However, in many studies, traffic data are available from measurements that can be used directly as inputs for emission models. Emission models use traffic data (fleet composition, vehicle speed, etc.) and other relevant data (e.g., road gradient, ambient temperature) to estimate traffic-related emissions of air pollutants, which are used as inputs to an atmospheric dispersion model. A variety of atmospheric dispersion models are available to simulate the concentrations of air pollutants as a function of time and space, with different levels of details (Holmes and Morawska, 2006, Zannetti, 1990, Sportisse, 2009). Eulerian and Lagrangian models are typically used for large domains, ranging from urban to global scales. At local scales (i.e., near emission sources), different models are used depending on topography. Gaussian dispersion models are typically used for cases without obstacles or with obstacles of simple geometry. Street-canyon models may be appropriate for cities with high buildings, although for cases with complex geometries computational fluid dynamics (CFD) models may be required.

In this work, we use a Gaussian dispersion model to simulate air pollutant concentrations near a roadway. Actual traffic data are used as input to estimate air pollutant emissions. Concentrations of pollutants were measured near the roadway for a two-week period. Local meteorological measurements were also available. During that period, wind speeds were mostly low and the prevailing wind direction was such that the measurement site was located mostly upwind of the roadway. Most Gaussian dispersion models are designed for receptors located downwind of the roadway and for conditions with a significant wind speed (Benson, 1989, Zhang and Batterman, 2010, Kenty et al., 2007). However, conditions with calm meteorological conditions and upwind locations are also relevant to population exposure. Therefore, this study examines the performance of a Gaussian model with and without modification for calm meteorological conditions using the measurements conducted near a roadway. First, the formulation of the atmospheric dispersion model is briefly presented. Then, the field campaign is described. Finally, the model simulation results are presented and discussed.

Section snippets

Model description

The emission and atmospheric dispersion models must be selected such that they are consistent in terms of level of detail, input requirements, and spatial and temporal resolution. An emission model based on average vehicle speed is appropriate here considering the available traffic data.

Two steady-state models are used here to simulate the atmospheric dispersion of pollutants: a Gaussian plume model for roadway sources (Briant et al., 2011, Briant et al., 2013) and this plume model augmented

Field study

The traffic, meteorological, and air pollution data used in this study were obtained in the MOCoPo (Measuring and mOdelling traffic COngestion and POllution) project, which covered 4 periods (one in each season) during 2011 near freeway N87 located south of Grenoble in eastern France. Another project (PM-Drive; Particulate Matter, Direct and Indirect On-Road Vehicular Emissions) was conducted in part jointly with MOCoPo for a two-week campaign in September 2011, to obtain measurements of

Results

The pollutants simulated in this study are NO₂, PM₁₀, PM_2.5, Na⁺, NO₃⁻, K⁺, Mg²⁺, Ca²⁺, Co, Cu, Fe, Mn, Pb, Sb, and Sn based on data at both the background and traffic sites and emission factors availability. For example, emission factors for SO₄²⁻ and NH₄⁺ were not available for road dust resuspension. The simulations were conducted with hourly input and output data and comparison with actual measurements was made for hourly and 4 h averages depending on data availability.

Chemical

Conclusion

In this study, we estimated the atmospheric dispersion of pollutant emitted by vehicles near a freeway under calm meteorological situations. To that end, an average-speed emission model was applied to estimate vehicle emissions with local traffic data. In addition, PM resuspension by traffic was included based on results of a site-specific PMF analysis. The concentrations of NO₂, PM₁₀, PM_2.5, Na⁺, NO₃⁻, K⁺, Mg²⁺, Ca²⁺, Co, Cu, Fe, Mn, Pb, Sb and Sn were simulated and compared with observations

Acknowledgments

The authors thank the “Ville numérique” program; IFSTTAR; CEREA, a joint laboratory of École des Ponts ParisTech and EDF R&D; the MOCoPo project funded by PREDIT; and the PM-DRIVE project funded by the CORTEA program of ADEME (program n° 1162C0002) for their financial support.

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Atmospheric dispersion modeling near a roadway under calm meteorological conditions

Highlights

Abstract

Introduction

Section snippets

Model description

Field study

Results

Conclusion

Acknowledgments

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Chemom. Intell. Lab. Syst.

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CALINE4 – A Dispersion Model for Predicting Air Pollution Concentration Near Roadways