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US Carbon Dioxide Motor Vehicle Emissions Resolved Hourly at a 1 km Resolution Daniel Mendoza 1 , Kevin Gurney 1 , Chris Miller 1 1 Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, IN 47907 Future work: After the emissions are input into a transport model, such as the Regional Atmospheric Modeling System (RAMS), the result will be a key factor in better understanding the North American carbon cycle. The results of the transport emissions will be combined with all other Sectors (commercial, industrial, residential, and electric power generation) and, along with CO emissions, will be used to drive simulations of atmospheric CO and CO2 distribution. The spatial gradients of carbon monoxide (CO) will then be compared to observations from the Measurements Of Pollution In The Troposphere (MOPPITT) satellite. The total CO 2 emissions from Vulcan will then form a key dataset in carbon inventories and be a crucial portion of atmospheric CO 2 inversions along with the CO 2 column observed by the Orbiting Carbon Observatory satellite (OCO). INTRODUCTION Acknowledgements: Special thanks to Broc Seib at Purdue University and NASA for funding this project. NMIM is a consolidated emissions modeling system for EPA’s MOBILE6 and NONROAD models. MOBILE6 is an emission factor model for predicting gram per mile emissions of Hydrocarbons (HC), Carbon Monoxide (CO), Nitrogen Oxides (NOx), Carbon Dioxide (CO 2 ), Particulate Matter (PM), and toxics from cars, trucks, and motorcycles under various conditions. NMIM uses the NMIM County Database (NCD) information to perform MOBILE6 runs. NCD is comprised of information provided by individual states as part of the National Emissions Inventory (NEI) process. NMIM assumes that monthly time resolution is accurate for both meteorology and source activity and produces monthly runs as output. Therefore NMIM runs MOBILE6 only once for each county and the emission factors obtained from MOBILE6 are then multiplied by Vehicle Miles Traveled (VMT). NMIM simplifies runs by modeling only weekdays since the difference with weekends is only temporal. Therefore MOVES was used to create a surrogate diurnal cycle based on hourly total fuel emission results which are the output of MOVES. The data input for MOVES comes from several sources including: Highway Population and Activity data from US Census Bureau, FHWA Highway Statistics, Federal Transit Administration National Transit Database, and Oak Ridge National Laboratories Light-duty Vehicle Database. The monthly output from NMIM is distributed over different days of the week, also a process that MOVES performs as it can differentiate between each day of a week. Once a diurnal cycle is established for a particular day of the week, this factor table is applied to the monthly emissions from NMIM and thus an individual day of a particular month can be reasonably resolved. The results for emissions were compared against two established national databases; the Energy Information Administration (EIA) database for 2001 and the Emission Database for Global Atmospheric Research (EDGAR) emission data EDGAR 32FT2000 (Marland, 1999). The emissions obtained from the model correspond very well at the state level which is provided by the EIA data and also at the total country level which is provided by both databases. This is very encouraging as it confirms the accuracy of the model in predicting emissions. Seasonal Variations in CO 2 Emissions in Ohio Roads (focus on Cleveland) The figures below show the variation in CO 2 emissions at four different periods in the year. The dates are picked to fall in the middle of each season: the entire output for the months of January, April, July, and October. It can be seen that the July data sees a larger amount of emissions and this is due to higher rates of transport cause by leisure and vacation travel. January has the lowest amount of emissions due to a much lower amount of trips performed by private vehicles. April and October have similar values and they are intermediate to those of July and January. The differences are not very large, as expected, because traffic does not vary greatly when integrated over whole months. Diurnal Variations in CO 2 Emissions in Ohio Roads The figures below show the variation in CO 2 emissions at four different time periods on July 3, 1999. July 3rd was chosen because it is a weekday and the rush hour traffic can be appreciated more clearly than on a weekend day. The five hours that are chosen are 3 AM, 8 AM, 5 PM, and 10 PM. There are clear peaks at rush hours (8 AM and 5 PM) and a lower amount of nighttime activity at 10 PM. 3 AM gives the lowest values. This diurnal cycle does not distinguish between different road types or vehicle classes. A more precise diurnal cycle is being prepared that will take into account variations due to a combination of road and vehicle type. The two pie charts compare the CO2 emissions versus the miles traveled for each road type. The road types are obtained from the Federal Highway Administration (FHWA) guidelines. The most striking characteristic of the VMTs is that in every category, urban highways have more miles traveled except for collector roads where their rural counterpart which are 1% greater. However, as whole, 50% more miles are traveled in urban roads. The distribution of emissions, on the other hand, is much more evenly spread. It can be seen that the rural sector has 54% of the emissions with the two largest contributors being interstates and major collectors (state highways). Therefore even though there is less traffic on rural roads, the emissions generated there total more than those of urban roads. CO 2 Emissions vs. Vehicle Miles Traveled (VMT) by Road Type CO2 Emissions vs. Vehicle Miles Traveled by Vehicle Class The two pie charts compare the CO 2 emissions versus the miles traveled for each vehicle class. The vehicle classes are obtained from the Federal Highway Administration guidelines. Gasoline powered vehicles cover 92% of the miles traveled every year. However they only account for 76% of the total CO 2 emissions. While HHDDV (the heaviest duty of diesel vehicles such as semi trailer trucks) traveled only about 5% of the total vehicle miles, they produced 17% of the CO 2 emissions. Conversely, private cars (LDGVs) accounted for 59% of the mileage while only contributing to 41% of the CO 2 emissions. A general conclusion that can be drawn is that while diesel- powered vehicles make up less of the miles traveled, they produce about 2.5 times more CO proportion. METHODS The motor vehicle sector has been estimated to produce 32% of the US total fossil fuel CO2 emissions (EPA, 2005). The motor vehicle sector poses a variety of challenges to the generation of spatiotemporal emission estimates. In addition to generating explicit space and time estimates of emissions for the United States, understanding the underlying drivers to emissions is a critical component in supporting research on the US carbon budget and carbon cycling studies. We present new estimates of carbon dioxide emissions generated by motor vehicles for the Continental United States using two different modeling systems developed by the Environmental Protection Agency (EPA). One is the National Mobile Inventory Model (NMIM) combined with the Motor Vehicle Emission Simulator (MOVES). We present emission estimates for the United States, highlighting the diurnal, weekly and seasonal cycles of emissions as well as comparing the emission of CO2 distributed over different vehicle classes and road types versus the vehicle miles traveled distributed over different vehicle classes and road types. Key drivers of motor vehicle CO 2 emissions include vehicle miles traveled, fuel efficiency, fuel used, and traffic patterns. We will present output validation by comparing these spatiotemporally explicit estimates to sectoral totals from independent estimates such as the EDGAR and EIA databases. The total emission result obtained in this project is 0.46 GtC/year for the transport sector which compares very well with the estimates from EIA (0.51 GtC/year) and EPA (0.48 GtC/year). These results are part of the "Vulcan" project at Purdue University funded by NASA (contract: NNG05GG12G) under the North American Carbon Program. References: 1. D. Medvigy et al, Mass conservation and atmospheric dynamics in the Regional Atmospheric Modeling System (RAMS) , Environm 2. Energy Information Administration: http://www.eia.doe.gov/environment.html 3. Emissions of Greenhouse Gases in the United States 2000, DOE/EIA-0573(2000), Washington, DC, November 2001. 4. Gregg Marland et al, CO2 from fossil fuel burning: a comparison of ORNL and EDGAR estimates of national emissions, Environ p 265-273, 1999. 5. Inventory of U.S Greenhouse Gas Emissions and Sinks: 1990-2003, EPA 430-R-05-003, Washington DC, April 2005. Distribution of CO2 Em issions by Vehicle Class 0% 1% 4% 1% 1% 17% 41% 0% 9% 6% 20% 0% Motorcycles(MC) Light DutyGasolineVehicles(LDGV) Light DutyGasolineTrucks1(LDGT1) Light DutyGasolineTrucks2(LDGT2) HeavyDutyGasolineVehicles(HDGV) Light DutyDiesel Vehicles(LDDV) Light DutyDiesel Trucks(LDDT) Light HeavyDutyDiesel Vehicles(LHDDV) Medium HeavyDutyDiesel Vehicles(MHDDV) HeavyDutyDiesel Vehicles(BUSES) 2B HeavyDutyDiesel Vehicles(2BHDDV) HeavyHeavyDutyDiesel Vehicles(HHDDV) Distribution of CO2 Em issions byRoad Type 10% 13% 7% 5% 10% 7% 3% 4% 16% 14% 9% 2% Interstate:UrbanTotal Interstate:RuralTotal Local:UrbanTotal Local:RuralTotal MinorArterial:UrbanTotal MinorArterial:RuralTotal Collector:UrbanTotal MinorCollector:RuralTotal MajorCollector:RuralTotal OthrPrincipalArterial:UrbanTotal OthrPrincipalArterial:RuralTotal OtherFreeways&Expressways:UrbanTotal Distribution of Vehicle M iles Traveled byVehicle Class 0% 1% 1% 0% 3% 8% 22% 1% 0% 5% 0% 59% Motorcycles(MC) Light DutyGasoline Vehicles(LDGV) Light DutyGasoline Trucks1 (LDGT1) Light DutyGasoline Trucks2 (LDGT2) HeavyDutyGasoline Vehicles(HDGV) Light DutyDiesel Vehicles(LDDV) Light DutyDiesel Trucks(LDDT) Light HeavyDutyDiesel Vehicles(LHDDV) Medium HeavyDutyDiesel Vehicles(MHDDV) HeavyDutyDiesel Vehicles(BUSES) 2B HeavyDutyDiesel Vehicles(2BHDDV) HeavyHeavyDutyDiesel Vehicles(HHDDV) Distribution ofVehicle M iles Traveled by Road Type 14% 10% 9% 5% 12% 6% 5% 2% 7% 15% 9% 6% Interstate:UrbanTotal Interstate:RuralTotal Local:UrbanTotal Local:RuralTotal MinorArterial:UrbanTotal MinorArterial:RuralTotal Collector:UrbanTotal MinorCollector:RuralTotal MajorCollector:RuralTotal OthrPrincipalArterial:UrbanTotal OthrPrincipalArterial:RuralTotal OtherFreeways&Expressways:UrbanTotal January April July October
