Abstract
The economic impact of air pollution due to motor vehicles by 2030 will be 100 billion dollars, and the public health impacts from traffic pollution will grow to 17 billion dollars. The transportation industry also faces the challenge of monitoring and controlling greenhouse gases (GHGs). CSU studied criteria air pollutants (CAPs)under different traffic congestion scenarios along selected freeways in Ohio. The study captured pollution intensities in different seasons, representing different atmospheric stabilities and concentrations of criteria air pollutants and greenhouse gases as a function of traffic densities. Our prior work determined typical hot spots in Ohio along freeways prone to high traffic densities and possible congestion. MOVES was used to generate these scenarios to assess vehicle emissions in a simulated traffic congestion scenario across interstate intersections. ODOT traffic data was used for these scenarios. The resulting air pollutants and greenhouse gases from emissions were determined using a dispersion model. Concentrations of the air pollutants were compared with NAAQS. An MS EXCEL-based model was developed to assess the severity of air pollution. Our methodology adequately provided a framework to estimate CAPs and GHGs across interstate interchanges where the traffic densities are high, and the congestion in these areas leads to elevated levels of these pollutants. We observed that long-haul trucks contribute to lower NOx levels and lower CO levels than their road and passenger cars counterparts. Morning traffic produced lower pollutant concentrations than those in the evenings. We built a model app to forecast air quality for congested areas (primarily interstate intersections) on freeways. CAV technology will be deployed to communicate information to travelers on freeways on radio channels approaching these congested areas. The mobile air quality app deployed on board CAV can update the models with traffic data to assess the lowering of emissions and GHGs due to replacing conventional vehicles with CAVs or those that run on renewables. CAV may facilitate vehicle-to-vehicle communications of air quality to alert vehicles approaching congested intersections.
Date
2-2024
Recommended Citation
Nedunuri, Krishna Kumar and Kandiah, Ramanitharan, "CAV Systems Incorporating Air Pollution Information from Traffic Congestion" (2024). Center for Connected and Automated Transportation. Paper 46.
http://dx.doi.org/10.5703/1288284317733
DOI
10.5703/1288284317733