Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway.
The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO2 emissions growing 40-110% over the same period. On-road vehicles are a major source of CO2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NO(x)), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new US. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NO(x), PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach.


CONCLUSIONS
Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.
Department of Civil, Environmental and Construction Engineering (CECE), University of Central Florida (UCF), 4000 Central Florida Blvd., Eng. Building II, Orlando, FL 32816-2450, USA. Hatem.Abou-senna@ucf.edu
Hatem
H
Abou-Senna
Essam
E
Radwan
Kurt
K
Westerlund
C David
CD
Cooper
D000393:Air Pollutants / Q000032:analysis*; D002245:Carbon Dioxide / Q000032:analysis; D002248:Carbon Monoxide / Q000032:analysis; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D005431:Florida; D008962:Models, Theoretical; D009589:Nitrogen Oxides / Q000032:analysis; D013997:Time Factors; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D009589:Nitrogen Oxides; D001335:Vehicle Emissions; D002245:Carbon Dioxide; D002248:Carbon Monoxide