No abstract

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NO x ) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDVactivity by time of day, day of week, and road type. More HDDVactivity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NO x and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NO x and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM 2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.Implications: Air quality model performance relies partly on on-road mobile source emission inventories accurately allocated, both spatially and temporally. This work demonstrates the importance of characterizing the mix of heavy-duty diesel versus lightduty gasoline vehicle activity on an hourly basis on weekdays and weekends by road type. Incorporating detailed activity data increases weekday average NO x and PM emissions 5-15%, with early morning hour emission increases approaching 100%, compared to using one average vehicle activity mix. Application of the methodologies described in this paper will improve the accuracy of on-road emission inventories in the understanding of ozone photochemistry and PM formation.

No abstract

An extensive set of emission tests has been conducted in the Auto/Oil Air Quality Improvement Research Program on different fuel/vehicle systems. These emission tests have been used to model the impact of fuel/ vehicle changes on ozone formation in Los Angeles, Dallas-Fort Worth, and New York in 1995 and 2005/2010. Light-duty vehicles are estimated to contribute 28-37% of the peak ozone in 1980/1985, decreasing to 7-18% in 1995, and further decreasing to 5-9% in 2005/2010. Gasoline changes that show promise in reducing the contribution of light-duty vehicles to ozone formation are reductions in olefin content, 90% distillation temperature, sulfur content, and vapor pressure. Results for a methanol/gasoline blend (M85) used in prototype flexible/variable fuel vehicles depend on the assumptions used to project future M85 emissions. A research test gasoline produced less ozone than the M85 cases in Los Angeles and New York and either more or less ozone than M85 in Dallas-Fort Worth, depending on the assumptions. Sensitivity tests for Los Angeles addressed uncertainties in the overall magnitude of emissions from light-duty vehicles, in the biogenic inventory, and in the representation of the atmospheric chemistry.

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