A computational modular approach to evaluate $ {\mathrm{NO_{x}}} $ emissions and ozone production due to vehicular traffic

Abstract

<p style='text-indent:20px;'>The societal impact of traffic is a long-standing and complex problem. We focus on the estimation of ground-level ozone production due to vehicular traffic. We propose a comprehensive computational approach combining four consecutive modules: a traffic simulation module, an emission module, a module for the main chemical reactions leading to ozone production, and a module for the diffusion of gases in the atmosphere. The traffic module is based on a second-order traffic flow model, obtained by choosing a special velocity function for the Collapsed Generalized Aw-Rascle-Zhang model. A general emission module is taken from literature, and tuned on NGSIM data together with the traffic module. Last two modules are based on reaction-diffusion partial differential equations. The system of partial differential equations describing the main chemical reactions of nitrogen oxides presents a source term given by the general emission module applied to the output of the traffic module. We use the proposed approach to analyze the ozone impact of various traffic scenarios and describe the effect of traffic light timing. The numerical tests show the negative effect of vehicles restarts on emissions, and the consequent increase in pollutants in the air, suggesting to increase the length of the green phase of traffic lights.</p>

Estimate of traffic emissions through multiscale second order models with heterogeneous data

“…Remark 5.1. The results of the tests described above are not in contrast with those proposed in [4,Section 3.1], where the CGARZ model without GPS data is used with the NGSIM dataset [34]. Indeed, the latter contains data for more than 5000 vehicles in 500 meters of road during 45 minutes of data recording.…”

“…In [30] the authors suggest a new methodology to estimate in real-time the emission rates of pollutants and describe their diffusion in air. Furthermore, in [4] the authors propose a computational tool to estimate pollutant emissions due to vehicular traffic using second order traffic models. This approach approximates emissions well when a large amount of data is available to feed the traffic model.…”

“…Here we focus on the estimation of pollutants production at ground level due to vehicular traffic, whose impact on air quality is a long-standing and complex problem. Following [4], we propose a computational approach that combines the traffic simulation model with an emission one. We focus our attention on NO x emissions, which are dangerous to human health and are precursors of the ozone, that also has negative effects on health [31,35].…”

“…Once approximated the density, velocity and acceleration of vehicles, we use them to estimate the emissions produced by vehicular traffic. Here we consider two microscopic emission models and we follow [4] to extend them to macroscopic variables.…”

Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City

“…Many studies have demonstrated that low cloudiness [43,44], intense solar radiation [45], high temperature [46,47], and low RH [48] can accelerate the O 3 production rate [49,50]. High road network density [51], frequent motor vehicle braking, rapid acceleration, and high traffic flow [52] lead to high NO X emissions [53]. Wind speed and direction can affect the horizontal distribution of O 3 in local areas, and a low wind speed facilitates O 3 accumulation [54,55].…”