H. Christopher Frey, a Nagui M. Rouphail, a,b Haibo Zhai a,c. Department of Civil, Construction, and Environmental Engineering b

Transcription

1 Measurement and Modeling of the Real-World Activity, Fuel Use, and Emissions of Onroad Vehicles: Policy Implications of Fuels, Technologies, and Infrastructure H. Christopher Frey, a Nagui M. Rouphail, a,b Haibo Zhai a,c a Department of Civil, Construction, and Environmental Engineering b Institute for Transportation Research Education North Carolina State University Raleigh, NC and c Now at Carnegie Mellon University 2010 TRB Energy and Environment Research Conference June 6-9, 2010 Raleigh, NC

2 Key Questions What are the real-world energy use and emissions of the transportation system? How sensitive are emissions to infrastructure, vehicle technology, fuels, driving cycles, and landuse? How can fuel consumption be decreased? How can emissions be reduced?

3 VSP Where Estimating Vehicle Fuel Use Based on Vehicle Specific Power (VSP) v a 1 gr gc a = vehicle acceleration (m/s 2 ) A = vehicle frontal area (m 2 ) C D = aerodynamic drag coefficient (dimensionless) C R = rolling resistance coefficient (dimensionless, ~ ) g = acceleration of gravity (9.8 m/s 2 ) m = vehicle mass (in metric tons) r = road grade v = vehicle speed (m/s) VSP = Vehicle Specific Power (kw/ton) ε = factor accounting for rotational masses (~ 0.1) ρ = ambient air density (1.207 kg/m 3 at 20 ºC) R 1 2 v 3 C D m A Frey, H.C., K. Zhang, and N.M. Rouphail, Vehicle-Specific Emissions Modeling Based Upon On-Road Measurements, Environmental Science and Technology, in press (published online 4/10/10)

4 Portable Emission Measurement System OEM-2100 Montana System Clean Air Technologies International, Inc. Carry-on Luggage size Weight: 35 lbs. Global Positioning System (GPS) Gas Analyzer NO and O 2 from electro-chemical sensors HC, CO, and CO 2 from non-dispersive infrared (NDIR) PM from laser light scattering detection Global Positioning System (GPS) GPS system measures vehicle location

5 CO 2 Emission (g/sec) CO 2 Emissions versus Vehicle Specific Power for a Typical Light Duty Gasoline Vehicle Vehicle Specific Power(kW/ton)

6 CO Emissions (mg/s) CO 2 Emissions (g/s) NOx Emissions (mg/s) HC Emissions (mg/s) VSP Mode Based Emissions Model for a 2005 Caravan 10 NO x, Caravan 3.3 L 10 HC, Caravan 3.3 L VSP Bin VSP Bin 1000 CO, Caravan 3.3 L 20.0 CO 2, Caravan 3.3 L VSP Bin VSP Bin Frey, H.C., K. Zhang, and N.M. Rouphail, Fuel Use and Emissions Comparisons for Alternative Routes, Time of Day, Road Grade, and Vehicles Based on In-Use Measurements, Environmental Science and Technology, 42(7): (April 2008)

7 Example of a Real World Field Study: Multiple Routes and Roadway Types Route 1 RTP Route 3 North Raleigh Six Forks Rd Route 2 Wake Forest Rd Route A Route B O/D Pair: NC State to North Raleigh Routes A, B, C O/D Pair: North Raleigh to RTP Routes 1, 2, 3 Route C NC State Frey, H.C., K. Zhang, and N.M. Rouphail, Fuel Use and Emissions Comparisons for Alternative Routes, Time of Day, Road Grade, and Vehicles Based on In-Use Measurements, Environmental Science and Technology, 42(7): (April 2008).

8 Speed (km/h) Percentage of Time (%) Example: Quantifying Activity for Primary Arterials for a Speed Range of Average Speed 120 Average Speed: km/h Runs Distance (km) 70 Average Speed: km/h 60 9 Runs VSP Bin Frey, H.C., N.M. Rouphail, and H. Zhai, Speed- and Facility-Specific Emission Estimates for On-Road Light-Duty Vehicles based on Real-World Speed Profiles, Transportation Research Record, 1987: (2006)

9 NO (mg/s) HC (mg/s) CO 2 (g/s) CO (mg/s) Link-based Average Emission Rates for Light Duty Gasoline Vehicles on Principal Arterials >50 Speed (km/h) >50 Speed (km/h) >50 Speed (km/h) >50 Speed (km/h)

10 NO x (mg/s) Link-based Average Emission Rates of NO x for LDGVs for Selected Roadway Types and Speeds km/h km/h km/h km/h km/h km/h km/h Local & Collector Arterial Freeway Off-Ramp On-Ramp * Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.

11 CO 2 (g/s) Real World CO 2 Emission Rates (and Fuel Use) for Selected Roadway Types and Speeds km/h km/h km/h km/h km/h km/h km/h 0 Local & Collector Arterial Freeway Off-Ramp On-Ramp * Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.

12 FRAMEWORK - Diesel - Gasoline - Diesel Conventional Technology - Biodiesel - Diesel - CNG - CNG Alternative Technology - Ethanol85 - Hybrid - Electric - Fuel cell Trucks Cars Buses Link Type Vehicle Class Link Speed Link-based Emission Factors (EF) per veh-sec Emission Inventory = link veh. class Link Travel Time EF Travel Time Link Volume Volume Regional Travel Demand Models

13 EF Link-based Emissions Model for a Pollutant Y, T, f, V Where: BER CCF EF HCF PCF SCF TCF TECF BER Y, T, f, v TECF HCF PCF CCF = basic emission rate (g/sec); = cycle correction factor for real-world link-based cycle at FTP average speed versus FTP cycle = emission factor (g/sec); = relative humidity correction (dimensionless); = pressure correction factor (dimensionless); = link-based speed correction factor, ratio of emissions at speed V to a baseline speed; = technology correction factor, ratio of emissions for technology T to conventional technology T (=1 for conventional fuels and technologies); = temperature correction factor (dimensionless). Subscripts: f = facility type (freeway, arterial, ramp, local & collector); T = technology class (gasoline, diesel, E85, HEV, CNG cars, etc.); T ' = index of conventional fuels and technologies (gasoline or diesel); v = average driving cycle speed (19.6 mph for LDGV and 20.0 mph for HDDV); V = average link-based speed (mph); Y = calendar year (CY2005, CY2030). T TCF T, T SCF T, f, V

14 Parameter Database Parameter Vehicle Fuel & Technology Source Basic Emission Rates Speed Correction Factors Fuel Economy Technology Correction Factors LDGV, LDDV, HDDT, HDDB LDGV, HDDT HDDB LDDV LDGV LDDV, HEV, CNG Cars MOBILE6 NCSU PEMS EPA PEMS Portugal PEMS EPA Fuel Economy Guide by EPA & DOE E85, HEV, CNG Cars EPA Certification Tests B20 trucks, CNG Buses NCSU PEMS, Literature* Traffic Demand Triangle Region Model ITRE, NCSU * TCFs derived emission comparison studies for B20 versus diesel heavy-duty trucks, and from reported comparisons of CNG versus diesel buses.

15 NO x (mg/s) HC (mg/s) CO 2 (g/s) CO (mg/s) 0.0 Example of Link-based Tailpipe Emission Factors: Light Duty Vehicles, Arterials, CY 2005 Speed (km/h) 4 LDGV E85 CNG LDDV HEV Speed (km/h) Speed (km/h) Speed (km/h) Speed (km/h)

16 Vehicle Class Car Truck Emission Inventory Scenarios & Fleet Characterization Fuel & Tech. Fleet Penetration by Vehicle Class (%) Present Scenario (2005) Future Scenario (2030) Baseline Alternative Baseline Alternative LDGV E HEV LDDV CNG EV & Fuel Cell HDDT B20 Trucks Bus HDDB CNG Bus

17 Effect of Vehicle Technology and Land-Use: Case Study for Mecklenburg County Collaborative Project with UNC-CH Regional and Urban Planning Input-output model of Mecklenburg County s economy with 12 sectors (UNC) Cross-sectional land-market equilibrium model with three sectors (UNC) Multimodal behavioral travel forecasting, including nonmotorized modes and incorporating attributes of the built environment (UNC and ITRE) Modal approach to estimating emissions (NCSU) Nominally looking at 2030 to 2050 time frame.

18 Vehicle Activity for Baseline and 2050 Future Scenarios Roadway Type Future Scenario Baseline Scenario Business-as- Smart Usual Growth Freeways 649, 860 1,232,060 1,337,910 Arterials 1,470,760 2,930,120 2,640,750 Local roads 254, , ,140 Ramps 65, , ,900 Bus rapid transit Light-rail ,220 Commuter-rail Entire network 2,440,640 4,820,310 4,557,480 Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, Impact of Alternative Vehicle Technologies and Land Use Patterns on Long-Term Regional On-Road Vehicle Emissions, 12th World Congress on Transportation Research, Lisbon, Portugal, July 11-15, 2010

19 Peak Hour Emissions for Baseline and 2050 Future Scenarios Model Year Scenario Land use Pattern Alternative vehicle Technologies Total emissions (tons) HC CO NO x CO Baseline No Businessas-usual Businessas-usual Smartgrowth Smartgrowth No Yes No Yes Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, Impact of Alternative Vehicle Technologies and Land Use Patterns on Long-Term Regional On-Road Vehicle Emissions, 12th World Congress on Transportation Research, Lisbon, Portugal, July 11-15, 2010

20 HC emission change relative to BAU scenario without alternative vehicle technologies Sensitivity of Emissions Reduction to Alternative Fuels and Technologies Penetration Rate of Alternative Vehicle Technologies 0% 0% 20% 40% 60% 80% 100% -10% -20% -30% -40% BAU SG -50% Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, Impact of Alternative Vehicle Technologies and Land Use Patterns on Long-Term Regional On-Road Vehicle Emissions, 12th World Congress on Transportation Research, Lisbon, Portugal, July 11-15, 2010

21 Estimated On-Road 2050 Tailpipe Emissions Pollutant Vehicle Fleet Land Use Pattern Trend TOD Hydrocarbons Carbon monoxide (CO) 100% conventional Benchmark -7.8% 73% conventional + 27% alt. -6.0% -11.6% 100% conventional Benchmark -6.3% 73% conventional + 27% alt % -17.4% NO x 73% conventional + 27% alt. -4.9% -9.9% 100% conventional Benchmark -5.5% Carbon dioxide (CO 2 ) 100% conventional Benchmark -7.1% 73% conventional + 27% alt. -3.5% -10.2% TOD = Transit-Oriented Development

22 Percent Different in Link-Based NOx Emissions for Mecklenburg County: NCSU Link-Based Model vs. Mobile6 for Baseline Source: UNC % Change

23 Key Findings from Mecklenburg Case Study Fleet turnover to all Tier 2 compliant vehicles will substantially reduce emissions of Hydrocarbons, Carbon Monoxide, and Nitrogen Oxides by 50 percent or more even with growth in vehicle miles travelled. Modest deployment of alternative vehicles may reduce these emission by an addition 5 to 10 percent. CO 2 emissions increase by approximately 70 percent with conventional vehicles and 64 percent with modest market penetration of alternative vehicles. Compared to Business as Usual land use, Smart Growth landuse may reduce emissions of HC, CO, NO x and CO 2 emissions between 5.5 and 7.8%, and slightly more with modest market penetration of alternative vehicles.

24 Conclusions Improvements in vehicle technology likely to enable continued reductions in emissions of some pollutants (HC, CO, NO x ) despite growth in energy use and miles travelled. Changes in landuse patterns may lead to incremental reductions in these emissions Modest penetration of alternative vehicle technologies is not enough to make a substantial difference more aggressive diffusion of such technologies should be pursued. However, CO 2 emissions are not abated and instead grow significantly under the scenarios considered.

25 Acknowledgments Disclaimer: The contents of this presentation reflect the views of the author and not necessarily the views of the sponsors. The author is responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of US EPA. This presentation does not constitute a standard, specification, or regulation.

26 Real-World Vehicle Activity, Fuel Use and Emissions Measurement Capability Portable Emission Measurement System (PEMS) Infrastructure Data: Vehicle location (GPS), road grade (via altimeter and GPS, if applicable) Vehicle Technology and Fuels: Engine size, fuel properties Behavior (Vehicle Dynamics): Speed, Acceleration, Engine RPM Ambient conditions: temperature, humidity, pressure Vehicle Fuel Use and Emissions: Gas analyzers for NO, HC, CO, CO 2 and opacity (Particulate Matter)

27 HC Emission (g/sec) CO Emission (g/sec) CO2 Emission (g/sec) NOx Emission (g/sec) Emission Rates Versus Vehicle Specific Power: CO 2, NO, Hydrocarbons, and CO Vehicle Specific Power(kW/ton) Vehicle Specific Power(kW/ton) Vehicle Specific Power(kW/ton) Vehicle Specific Power(kW/ton)

28 NCSU VSP Driving Modes VSP Mode VSP (kw/ton) VSP Mode VSP (kw/ton) 1 VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP < VSP 39 Frey, H.C., A. Unal, J. Chen, S. Li, and C. Xuan, Methodology for Developing Modal Emission Rates for EPA s Multi-Scale Motor Vehicle and Equipment Emission Estimation System, EPA420-R , Prepared by NC State University for U.S. Environmental Protection Agency, Ann Arbor, MI, Oct. 2002

29 Comparing Fuel Use and Emission Rates for Different Roadway Types Local Road Arterial Freeway On-Ramp Off-Ramp 33

30 Synthesizing the Micro-Scale Models into a Larger Framework Develop link-based emissions models to couple with transportation models for emission inventory estimates. Characterize regional on-road mobile source emissions. Evaluate the potential reductions in air pollutant emissions associated with real-world operation of advanced fuel and technology vehicles in comparison to conventional vehicles.

31 Speed Correction Factors (SCFs) SCF = ratio of link average emission rate at any speed to link average emission rate at baseline speed range (e.g. 30 to 40 km/h). Link average emission rates for a given technology are estimated using field-measured second-by-second speed profiles and Vehicle Specific Power (VSP)-based emission rates. Frey, H.C., N.M. Rouphail, and H. Zhai, Speed- and Facility-Specific Emission Estimates for On-Road Light- Duty Vehicles based on Real-World Speed Profiles, Transportation Research Record, 1987: (2006) Zhai, H., H.C. Frey, and N.M. Rouphail, A Vehicle-Specific Power Approach to Speed- and Facility-Specific Emissions Estimates for Diesel Transit Buses, Environmental Science and Technology, 42(21): (2008). Frey, H.C., N.M. Rouphail, and H. Zhai, Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data, Transportation Research Record, 2058:23-32 (2008). Coelho, M., H.C. Frey, N.M. Rouphail, H. Zhai, and L. Pelkmans, Assessing Methods for Comparing Emissions from Gasoline and Diesel Light-Duty Vehicles Based on Microscale Measurements, Transportation Research Part D, 14D(2):91-99 (March 2009). Frey, H.C., H. Zhai, and N.M. Rouphail, Regional On-Road Vehicle Running Emissions Modeling and Evaluation for Conventional and Alternative Vehicle Technologies, Environmental Science and Technology, 43(21): (2009).

32 Speed Correction Factor NO x (mg/sec) Speed Correction Factors: Example for Light Duty Gasoline Vehicles on Arterials Speed (km/h) HC CO NOx CO Speed (km/h)

33 Technology Correction Factors (TCFs) TCFs account for differences in emissions rates when replacing conventional with alternative vehicle technology For HC, CO and NO x, TCFs for E85, HEV and CNG are estimated based on average FTP emission rates from EPA s annual certification tests for alternative fuel versus gasoline from 2001 through 2007 (e.g., Frey et al., 2009). For CO 2, TCFs for HEV and CNG are estimated based on fuel economy comparisons for alternative fuel versus gasoline, and for E85 based on fuel combustion theoretical analysis. For B20 biodiesel heavy-duty vehicles, TCFs are estimated from previous studies at NCSU (e.g., Frey and Kim, 2006). Zhai, H., H.C. Frey, N.M. Rouphail, G. Goncalves, and T. Farias, Comparison of Flexible Fuel Vehicle and Life Cycle Fuel Consumption and Emissions of Selected Pollutants and Greenhouse Gases for Ethanol 85 Versus Gasoline, Journal of the Air & Waste Management Association, 59(8): (August 2009). Frey, H.C., and K. Kim, Comparison of Real-World Fuel Use and Emissions for Dump Trucks Fueled with B20 Biodiesel Versus Petroleum Diesel, Transportation Research Record, 1987: (2006). Frey, H.C., and K. Kim, In-Use Measurement of Activity, Fuel Use, and Emissions of Cement Mixer Trucks Operated on Petroleum Diesel and B20 Biodiesel, Trans. Research Part D. 14(8): (2009).

34 Emission Inventory TE CT ct 1 EF ct t ct vol ct Where TE reflects outputs for a SINGLE link ct EF ct t ct vol ct TE = combination of vehicle class and technology; = link-based emission factor for vehicles of class / tech (ct) (g/sec); = average link travel time for vehicles of class / tech (ct) (second); = traffic volume on link for vehicles of class / tech (ct) (vehicles/hr); = total emissions for a single link (g/hr). Vehicle activity (average speed, number and types of vehicles) for the RTP road network estimated using ITRE s Triangle Regional Model (TRM) Data subsequently aggregated across all links in the network.

35 Triangle Regional Transportation Network Durham Chapel Hill Raleigh Present No VMT growth VMT growth (33%), average speed decrease (28%) Future Future

36 Regional Emissions on Weekday Morning Peak Hour Total Network Emissions (tons in peak hour) Scenario HC CO NO x CO 2 Present: Baseline ,380 Present: Alternative ,330 Future, No Growth: Baseline ,200 Future, No Growth: Alternative ,170 Future, Growth: Baseline ,850 Future, Growth: Alternative ,780

37 Regional Emissions Relative Changes for Weekday Morning Peak (continued) Difference in Emissions Relative to Present Baseline Scenario (%) Scenario HC CO NO x CO 2 Present: Alternative Future, No Growth: Baseline Future, No Growth: Alternative Future, Growth: Baseline Future, Growth: Alternative Change in Emissions for Future Alternative versus Future Baseline (%) Scenarios HC CO NO x CO 2 Future, Growth, Alternative versus Future, Growth, Baseline

38 Spatial Characterization of Emissions During AM Peak Hour: Present Baseline Scenario Normalized NO x Link Emissions Total Network NO x Emissions Distribution 20% 29% 51% VMT Distribution 26% 39% grams/mile/hr 28.8% > % Freeway+ Ramp Arterial Local and Collector 35%

39 Impact of Vehicle Fleet Distribution on Regional Network Emissions for Present and Alternative Scenarios Present Baseline 1% 17% HC CO NO x CO 2 83% 1.8% 0.2% 98.0% 7% 48% 45% 30% 3% 67% Future Alternative (Growth) VMT Distr. 11% 88% 34% 1% 7% 59% car truck bus 99% 36% 3% 1% 31% 61% 68%