Integrated Bus Information System (IBIS) A Vehicle Procurement Resource for Transit

Transcription

1 Integrated Bus Information System (IBIS) A Vehicle Procurement Resource for Transit W. Scott Wayne *, Mario Perhinschi, Nigel Clark, Sergio Tamayo & Jun Tu West Virginia University P.O. Box 6106 Morgantown WV Phone: (304) Fax: (304) Scott.Wayne@mail.wvu.edu Mario.Perhinschi@mail.wvu.edu Nigel.Clark@mail.wvu.edu syamayo@mix.wvu.edu jtu1@mix.wvu.edu Word Count: Table Count: Figure Count: Equivalent Words: 5477 words 0 tables 7 figures 7227 words * Corresponding Author

2 2 ABSTRACT West Virginia University (WVU), under contract to the Federal Transit Administration, has developed two tools for evaluating the pollutant emissions, greenhouse gases, and fuel economy of transit buses: 1) a searchable database of transit vehicle emissions data and 2) a transit fleet emissions inventory model. These tools complemented by a transit vehicle life cycle cost model developed by WVU, Battelle, and Transit Resource Center for the Transportation Research Board provide an interactive, approachable, and reliable method for users, primarily transit agencies, to evaluate overall fleet emissions and fuel consumption for optimization of fleet configuration and operation. These tools will be made available through an online website called the Integrated Bus Information System (IBIS). This paper describes development of the transit fleet inventory model and comparison with the Environmental Protection Agency (EPA) MOBILE6 and MOVES emissions inventory models. The IBIS model was developed from extensive chassis dynamometer data from several reference vehicles from which polynomial models of the emissions could be built through linear regression. These backbone models characterized the effects of driving characteristics on vehicle emissions and fuel consumption. Comparison of predicted emissions showed good agreement for hydrocarbon, carbon monoxide and oxides of nitrogen emissions and acceptable agreement for particulate matter emissions. The EPA MOBILE6 model assumed constant values as a function of duty cycle for carbon dioxide emissions and fuel economy. The WVU IBIS model and EPA MOVES model displayed similar trends for carbon dioxide emissions but MOVES predicted substantially lower carbon dioxide levels. INTRODUCTION The mission of public transit agencies is to provide safe, efficient, environmentally conscious, reliable and cost effective transportation. The evaluation, selection, and implementation of fuel and powertrain technology choices are critically important to accomplishing this mission. Transit vehicle procurement decisions are influenced by complex and intertwined considerations and constraints including: Emissions and greenhouse gas implications Compliance with federal, state, and local environmental and procurement regulations Initial investment expense and life cycle costs Reliability, availability, and maintainability implications of the technology Vendor stability, support, and warranty Fueling and maintenance facility requirements Operator and mechanic training requirements Rarely does an obvious technology choice emerge and the vehicle procurement decisions evolve to be a difficult compromise between economic, environmental, and operability requirements. In addition, there is often inherent conservatism in decision making, because the reliability of novel technologies is not proven and is difficult to assess. Compounding the difficulty of fleet planning and procurement is the dearth of information, resources and tools to enable the evaluation of environmental, economic, and operational implications of the various fuel and technology choices for transit vehicles. West Virginia University (WVU), under contract to the Federal Transit Administration, has developed a set of tools for evaluating the pollutant emissions, greenhouse gases, and fuel economy of transit buses: 1) a searchable database of transit vehicle emissions test data and 2) a transit fleet emissions inventory model. WVU, Battelle and Transit Resource Center have also developed a transit vehicle life cycle cost model under contract to the Transit Cooperative Research Program Project C-15 [1]. These tools, along with other transit vehicle procurement information resources will be made available online at the Integrated Bus Information System (IBIS) website currently under development by WVU. The overall goal of IBIS is to provide an interactive, approachable, and reliable method for users - primarily transit agencies - to evaluate overall fleet emissions and fuel consumption and optimize fleet configuration and operation. The development of the life cycle cost model has been previously published [1]. This paper presents an overview of the methodology, capability, and application of the IBIS transit fleet emission inventory model.

3 3 BACKGROUND Six out of ten Americans live in urban areas where air pollution can cause major health problems. The American Lung Association State of the Air 2010 report [2] shows that over 175 million people roughly 58 percent still suffer pollution levels that are often dangerous. The State of the Air 2010 report [2] examined the levels of fine particulate matter (PM 2.5 ) and ozone. The report found that over half of US people live in areas with unhealthy ozone levels. Exposure to elevated ozone levels places people at risk for decreased lung function, respiratory infection, lung inflammation, and aggravation of existing respiratory illness such as asthma and chronic obstructive pulmonary disease (COPD) [2]. The report also found that nearly one-quarter of the US population live in areas with unhealthy levels of fine particulate pollution. Numerous research studies have concluded that fine particle pollution poses serious health threats including premature death [3]; increased risk of heart attacks, heart disease and congestive heart failure [2, 4] and strokes, [5]; respiratory harm including worsened asthma and COPD [6, 7]; impairment of the immune system and central nervous system [8] increased risk of cancer [9] and potential reproductive and developmental harm [2, 10]. Diesel engines are a major source of both NOx and particulate matter (PM) emissions. The Environmental Protection Agency (EPA), the California Air Resources Board (CARB), and National Institute for Occupational Safety and Health (NIOSH) have all classified diesel exhaust as a potential occupational carcinogen [2, 11, 12]. Diesel fuel is the most commonly used fuel in transit accounting for over 70% of the national bus fleet in 2009 including conventional-drive and diesel-electric hybrid buses [14]. In communities where transit vehicles operate frequently, tailpipe emissions accumulate around localized hotspots such as transit bus depots, intermodal transfer stations and maintenance facilities [14]. Transit agencies are increasingly pressured by federal, state and local legislation, public boards and environmental interest groups to employ fuel efficient, low emitting vehicles. In addition to stringent EPA and CARB emissions standards, many transit agencies are or may soon be impacted by other state and local legislation. Examples include the CARB Zero Emission Bus rule which will require 15% of transit vehicle purchase to be zero tailpipe emissions [15]; the South Coast Air Quality Management District Fleet Procurement Rule which prohibits purchase of diesel powered vehicles [16]; Connecticut Special Act which requires the state Department of Environmental Protection to develop a plan to reduce the impact of diesel emissions from public transportation [17]. Environmental regulations and pressure from municipalities, boards and local contingencies for green transportation place additional pressures on transit vehicle procurement process that in some instances may lead to purchase of vehicles that are not the best option for the particular transit agency. All aspects including fuel efficiency, emissions, life cycle cost, maintenance, and operation need to be considered. This paper addresses the fuel efficiency and emissions aspect of the bus procurement process. There are several models available for predicting emissions; the most widely used being EPA s MOBILE6, and MOVES models and CARB s EMFAC model. MOBILE6 [18] and MOVES [19] are emissions inventory models intended primarily for use in the development of State Implementation Plans (SIP) and regional conformity analysis. MOBILE6 and MOVES are capable of predicting emissions inventories. MOBILE6 and MOVES include default databases of meteorology, vehicle fleet makeup, vehicle activity data, fuel and emissions program data for the entire United States [19]. A recognized limitation is that the databases, derived from a variety of sources, do not necessarily include the most accurate or up-to-date information available at the local level for a local level analysis [19]. MOBILE6 and MOVES also use default driving behavior source types and road type distributions. MOBILE6 and MOVES are capable of modeling emissions inventories, at the national, state, regional and project level. However, performing a county or project level analysis requires the user to provide detailed information on vehicle age distribution, vehicle average speed distribution, vehicle miles traveled distribution, road type distribution, and meteorological data in the form of user specified databases. The creation of these databases is complicated and time consuming making these models difficult to use for transit agency level vehicle procurement analysis. The EMFAC model developed by CARB is a model that calculates emission inventories for motor vehicles operating on roads in California [20]. EMFAC computes

4 4 baseline emissions based on chassis dynamometer emissions data and speed correction factors. Emissions rates are combined with vehicle activity data to compute emissions inventories. Due to their complexity, these existing models are difficult to use as a procurement tool for comparing the emissions impact of different fuel and powertrain technologies. WVU, with funding from the U.S. Federal Transit Administration has developed an emissions inventory model specifically intended for comparison of fuel and powertrain technology options for transit vehicle procurement. The model was designed to provide fleet level estimates of emission and fuel efficiency of sufficient accuracy for vehicle procurement analysis based on simple inputs that are available at most transit properties. MODELING METHODOLGY General Modeling Strategy To model a fleet in IBIS, the user defines a set of virtual buses. Each virtual bus represents the characteristics of an actual vehicle in the existing fleet or a vehicle that is being considered for purchase. The characteristics defined for each virtual vehicle include: Category 1 - Vehicle Parameters Category 2 - Driving Characteristics Category 3 - External Operating Conditions The vehicle parameters included technical characteristics of the vehicle such as: type of fuel, powertrain type (conventional or hybrid), length, model year, curb weight, occupancy, engine rated power, aftertreatment equipment, displacement, number of cylinders, transmission type, type of heating system, and capacity of air conditioning. The driving characteristics describe the manner in which the vehicle is driven in service and include: average speed with idle, number of starts/stops per mile, percentage idle, standard deviation of speed with idle, and kinetic intensity [21]. The external operating conditions, which have not been implemented in the model to date, may include relevant parameters such as road grade, geographical location, and season. A fleet is then comprised by specifying the number of each virtual vehicle. Model outputs include: Fuel economy Carbon dioxide emissions Carbon monoxide emissions Oxides of nitrogen emissions Total hydrocarbon emissions Particulate matter emissions The fuel economy and emissions models were developed from a set of reference vehicles (RV) for which extensive reliable chassis dynamometer data were available and for which polynomial models of the six output variables were built through linear regression as functions of driving cycle characteristics. These models were referred to as Backbone Models (BM). Where available data did not cover adequately the range of the independent variables, a genetic algorithm was used to produce new virtual cycles with desired values for the explanatory parameters in Category #2 through the concatenation of micro-trips extracted from measured data over standard dynamometer driving cycles. A micro-trip was defined as the interval between two consecutive segments of idle operation. The data obtained in this manner were used to determine the polynomial models through linear regression. Additional repair and correction algorithms were included to accommodate for inaccuracies and border limits. Generation of New Driving Cycles The backbone models of emissions and fuel economy consisted of polynomials determined through linear regression based on the five explanatory variables describing driving characteristics. Experimental data uniformly distributed throughout this five-dimensional hyperspace were necessary for adequate

5 5 polynomial development. FIGURE 1 shows fuel efficiency as a function of average cycle speed and standard deviation of average cycle speed. Each points designated by blue circles represent a measured data point from chassis dynamometer testing on one particular driving cycle. The sparse data from experimental emissions tests were not adequate for generation of backbone polynomial models and it was not economically feasible to perform an exhaustive testing program to fully populate the parameter hyperspace. Considering that standard test driving cycles consist of series of segments or microtrips defined as periods between consecutive idles, it was possible to create new virtual cycles by combining segments of data from existing second-by-second experimental data. Second-by-second emissions and fuel consumption data were isolated for each microtrip from available tests. These microtrips were then recombined to obtain the desired overall characteristics of the new virtual cycle. By repeatedly applying this technique, virtual data points were generated to populate sparse areas of the parameter space not adequately covered by experimental data. These virtual data points were generated using a genetic algorithm approach. Genetic algorithms are parameter iterative search techniques that rely on analogies to natural biological processes [22, 23]. This class of artificial intelligence techniques simulates the evolution of species and individual selection based on Darwin s survival of the fittest principle to perform parameter optimization. In our case, an optimum set of micro-trips was eventually determined such that its driving characteristics were within an imposed margin to specified values. A computational tool for interactive cycle generation was developed at WVU in MATLAB. An example of virtual cycles generated using this approach is presented in FIGURE 1. FIGURE 1 Fuel efficiency of generated cycles. Backbone Model Development A linear regression algorithm was implemented in Matlab to model the fuel consumption and the emissions as polynomials with the five Category #2 parameters as independent variables. As expected, the average speed was found to be the most important parameter. For each of the output models, four different polynomial models were eventually developed. Each of them had, as independent variables, average speed plus one of the other four Category #2 inputs. An example of the fuel economy modeled as a polynomial function of average speed with idle and standard deviation of speed for a 2006 model year

6 6 diesel fueled reference vehicle is presented in FIGURE 2. The carbon dioxide emissions for a CNG fueled vehicle as predicted by a backbone model function of average speed and percentage idle are shown in FIGURE 3. FIGURE 2 Backbone model of fuel economy for a 2006 diesel fueled vehicle. FIGURE 3 Backbone model of carbon dioxide emissions for a CNG fueled vehicle. IBIS User Interface The intent of the IBIS Transit Fleet Emissions Model is to provide fleet level emissions inventory predictions for the purposes of comparing available transit bus fuel and powertrain options to assist with vehicle procurement decisions and fleet planning. The model was designed to predict the emissions of a fleet of transit buses composed of various technologies with sufficient accuracy to evaluate the emissions impact of different vehicle technology options using input data that are available at the transit agency. FIGURE 4 shows the IBIS data entry interface.

7 7 FIGURE 4 IBIS bus fleet data entry screen capture

8 8 In preparation for using the IBIS model the transit manager would compile information on the number, power train type (conventional or hybrid), fuel type (diesel or CNG) and model year. Diesel fuel and CNG are the primary fuels in transit use. The buses included in the model could represent existing buses in the fleet as well as buses that are under consideration for purchase. Buses should be grouped into the following model year categories: Post 2010 These model year groups correspond to changes in the EPA emissions regulations for heavy-duty urban buses. Within these model year categories, the user may also want to group buses that operate over similar routes together as sub groups. The user should also collect data on number of miles traveled annually by each subgroup of buses. This information will be used to build a virtual bus fleet in IBIS. The second category of data that will be needed in order to model a fleet of buses in IBIS relates to characterizing the duty cycles which represent how the buses are driven. The user has the option of selecting from a set of standard duty cycles that are commonly used for testing buses on a chassis dynamometer. These standard driving cycles were developed from real world operation data. The user can also specify a custom duty cycle by entering numeric values of the cycle metrics. Vehicle duty cycles are characterized in IBIS by a variety of engineering metrics. The metrics were meant to numerically describe specific characteristics of the driving activity. A brief description of the cycle metrics follows: Average Speed (U) is defined as distance travelled divided by cycle time, Ū=D/T. It links idle with driving periods and has been widely used to characterize driving behaviors. Average speed is the primary explanatory variable for prediction of fuel economy and emissions of transit buses. Percentage Idle represents the fraction of time that the bus is at stand by. Stops per mile represent the average number of stops that the bus makes per mile traveled. All types of stops are included, e.g. pick-up or traffic stops, and are not differentiated. Stops per mile is related to average speed such that the larger its value, the lower the average speed. Standard Deviation of Speed (Stdv U) represents the transient character of the drive cycle. For a given average speed, a higher value of Stdv U signifies a more transient operation (with lots of accelerations and decelerations) whereas a lower value implies a more constant speed and more cruise. Given the conditions assumed above, the cycle with higher Stdv U would has higher emissions and lower fuel economy and also would be more suitable for hybrid vehicle operation. Kinetic Intensity (ki) is an important factor for hybrid vehicles because a cycle energy use analysis [21] shows that high values of kinetic intensity translate into higher fractions of available braking energy and give room to fuel economy improvements through hybridization. Data to develop a custom duty cycle may come from GPS data logging of actual vehicle activity, or route profiles. The IBIS model was developed with the understanding that many transit agencies may not have information that allows the calculation of all five cycle metrics. Average speed has been identified as the metric with the strongest influence on vehicle emissions. When creating a customized duty cycle the user must supply a value of average speed. The user has the option to provide as many of the metrics as possible. Prediction accuracy improves as more metrics are provided; however, in many cases average speed alone provides acceptable results. MODEL VALIDATION A comparison of the IBIS Transit Fleet Emissions Model against EPA s MOBILE6 and MOVES models was conducted. Emissions of 40-foot, diesel-powered, non-hybrid transit buses of model years ranging from 1988 to 2006 and operated over duty cycles of varying average speed were modeled in IBIS,

9 9 MOBILE6, and MOVES2009. Although the inputs required by each model were different, an attempt was made to match the modeled scenarios as closely as possible. The following paragraphs describe how each model was configured. The IBIS Transit Fleet Emissions Model characterizes buses in broad technology categories such as; conventional-drive (non-hybrid) diesel-powered, conventional-drive CNG-powered, diesel hybridelectric, etc. It does not attempt to distinguish vehicle or engine manufacturer nor does it attempt to account for detailed configuration differences among buses in the same technology category. The current version of the model does not account for climatic (heating or air conditioning fuel burdens) or terrain effects on emissions. These features are still in development. FIGURE 4 shows the IBIS data entry form. The data entry page allows the user to name the model and displays a table showing the vehicles included in the modeled fleet. The insets show the currently available bus fuel and technology options. Model year ranges correspond to EPA emissions regulation changes. The model allows the user to select from eleven industry standard chassis dynamometer driving cycles or the user can customize their own driving cycle by providing values of the cycle metrics in the data entry fields. For the comparison with MOBILE6 and MOVES individual simulations were performed to obtain emissions results for the various model year groups over each of the eleven standard driving cycles plus two custom cycles with average speeds of 32 mph and 38 mph. For Mobile 6, an input file was written in a text file format with the characteristics of the run and the desired output. In this example, a transit bus was modeled operating over an Arterial route. The input file header included the pollutants that were required as outputs, and the components comprising total particulate matter. It also included the vehicle type (in this case only transit buses), the model year of the vehicle, and the amount of hours for which the bus operated. The run section included output data characteristics along with fuel characteristics and the variation of temperature at which the vehicle was operated. The scenario section included the filenames of the databases required to calculate the particulate matter, and the size of the particulates. Furthermore, it included the year during which the emissions were modeled, the season, the altitude, the sulfur content of the diesel fuel, and the average speed value and the type of driving for that average speed. The arterial type was chosen since it was the driving type that most closely matched normal usage of transit buses within a city. In the case of MOVES2009, a graphic user interface (GUI) was used to enter the appropriate data to obtain the corresponding comparison with the IBIS model. For a transit fleet analysis, a local or county scale would typically be used; however, in order to perform a local or county level analysis, MOVES requires that the user provide a set of custom databases. These databases require detailed data regarding road type distributions, vehicle population distributions, etc. which were difficult to obtain. Therefore, a national scale was selected taking into account that specific factors for the local area were not verified and were based on default national values. A time span for a normal weekday was used and a time of operation from 9 am to 5 pm. For the vehicle type, a transit bus fueled with diesel was selected. In order to analyze properly the driving characteristics of each cycle, a similar approach to MOBILE6 was used. The Arterial driving type was selected to describe the driving characteristics of the cycles. In MOVES, the Arterial driving type was included in the Urban Restricted and Unrestricted access options, with some standard allocation of average speeds and the amount of driving that occurred for the Arterial driving type. Hence, it was required to use the data importer to generate a table describing the road type distribution and average speed distribution for each cycle allocating percentages for each of the speed bins. In the pollutants tab, the pollutants corresponding to IBIS were selected, including the same particulate size as the particulates measured in the IBIS model. RESULTS COMPARISON FIGURE 5 shows a comparison of predicted emissions for model year conventional-drive diesel powered transit buses over a range of average speeds. Two simulations were conducted using the IBIS Transit Fleet Emissions Model. The points indicated by the x and marked Simulator (5 parameters) used all five cycle metrics. The points marked with an o and designated Simulator (Ave. Speed) specified only the average duty cycle speed.

10 10 FIGURE 5 Comparison of IBIS, MOBILE6, and MOVES emissions for diesel transit buses. The top left panel shows predicted total hydrocarbon (HC) emissions. The IBIS model using five parameters and only average speed agreed fairly well with the EPA MOVES model across the average speed domain. MOBILE6 agreed with IBIS and MOVES at higher average duty cycle speeds but predicted significantly lower HC emissions at low average speeds. The top right panel shows predicted carbon monoxide emissions (CO). IBIS and MOVES showed similar trends across the entire speed

11 11 domain while MOBLE6 predicted significantly higher CO emissions. In terms of NOx emissions the WVU IBIS model agreed more closely with EPA s MOBILE6 model than with MOVES at low average speeds. At average cycle speeds above 12 mph, MOBILE6 and MOVES were in very close agreement. Below 12 mph, NOx emissions predicted by the MOVES model rose more steeply with decreasing average speed than did the predictions of MOBILE6 and IBIS. NOx emissions predicted by IBIS were slightly lower than the MOBILE6 predictions across the entire average cycle speed domain. Particulate matter (PM) emissions were more difficult to model due to the lack of a continuous PM data for model development. The accuracy of the gaseous emissions models were improved through the use of second-by-second emissions data and genetic algorithms as described above. For PM emissions, which were quantified by weighing the PM mass accumulated on filter media, no continuous second-by-second data existed. Due to the lack of continuous PM data, only 12 data points were available (one per test cycle) to characterize PM emissions variation as a function of duty cycle. EPA s MOBILE6 model curiously predicted constant PM emissions as a function of average speed. WVU s IBIS model agreed well with the EPA MOVES model at cycle average speeds above approximately 12 mph. At low average duty cycle speeds, the MOVES model predicted rapidly increasing PM emissions with decreasing speed. The 5-parameter IBIS model also predicts an increase in PM emissions with decreasing average speed but at a lower rate than the MOVES model. Agreement of the three models for carbon dioxide (CO 2 ) was poorer than expected. Carbon dioxide results predicted by MOBILE6 did not vary with average duty cycle speed as would be expected based on experimental data. WVU s IBIS model and EPA s MOVES model showed similar trends with average speed; however, MOVES predicted much lower CO 2 emissions in general than the IBIS model. which was developed based on chassis dynamometer measurements. In general, fuel consumption varies in direct proportion to CO 2 emissions. The lower right panel shows predicted fuel economy. MOBILE6 assumed constant fuel consumption as a function of duty cycle average speed. Fuel economy as predicted by the WVU IBIS model shows increasing fuel efficiency with average duty cycle speed as expected. The authors are skeptical about the CO 2 and fuel economy predictions by MOVES. Carbon dioxide emissions can be directly related to fuel consumption by a carbon balance which relates carbon in the fuel to carbon emissions. In the lower right panel of FIGURE 5, the diamonds represent fuel economy computed from total energy expenditure results from MOVES while the squares represent fuel economy estimated from the CO 2 emissions results from MOVES. Both of these methods indicate fuel economy approaching 8 miles/gallon which is not realistic for a transit bus. WVU has performed extensive testing of transit buses both on a chassis dynamometer and on-road and have collected and analyzed bus fueling records at transit agencies throughout the United States as part of the TRB C-15 study [1]. FIGURE 6 shows these data which agree well with the IBIS model and are substantially lower than the fuel economy predicted by MOVES. FIGURE 7 shows a comparison of results between IBIS, MOBILE6 and MOVES for model year diesel transit buses. In this model year group, IBIS predicted significantly lower HC emissions than EPA s MOVES model and slightly lower CO emissions. Predicted NOx emissions between IBIS and MOVES showed acceptable agreement at low cycle speeds; however, MOVES predicted lower NOx emissions at average cycle speeds above 15 mph. Both IBIS and MOVES exhibited similar trends for PM emissions but MOVES predicted slightly higher values. Carbon dioxide and fuel economy results showed the same behavior as in the model year category with MOVES exhibiting substantially lower CO 2 emissions and high fuel economy. The version of MOVES used for these comparisons did not offer the option to model CNG fuel. Therefore, no comparison was possible. The next release of MOVES is expected to include CNG as a fuel option. MOVES and MOBILE6 do not currently allow hybrid powertrains to be modeled as a distinct option so no comparison of hybrid electric transit buses was possible.

12 12 FIGURE 6: Diesel transit bus fuel economy determined from chassis dynamometer testing and inuse fueling records [1]. CONCLUSIONS Public transit agencies are constantly pressured to employ alternative fuels and advanced vehicle technologies to replace conventional diesel powered buses. Pressure arises from federal, state, and local environmental regulations, public boards, environmental groups, and local citizens. The most predominant alternatives to conventional diesel buses include CNG and diesel-electric hybrid buses. Emerging technologies including gasoline and CNG hybrids, and battery electric and fuel cell powered buses are in various stages of pre-production development. The decision of which alternative technology to embrace is often difficult and should carefully consider regulatory constraints, fuel efficiency, emissions implications, initial investment and lifecycle, costs and maintenance, reliability, and availability. WVU with funding from the Federal Transit Administration is developing an online accessible Transit Fleet Emissions Model that allows a transit agency to evaluate the effect of vehicle procurement choices on their fleet emissions profile. The model uses simple input information that is available to most transit fleet managers. A comparison of the emissions predictions from the IBIS Transit Fleet Emissions Model against more sophisticated EPA MOBILE6 and MOVES models was performed. The comparison included buses ranging in model year from 1998 to present (in the interest of brevity only results were shown here). The easy to use IBIS model, which was specifically designed to compare technologies for the purposes of bus procurement showed satisfactory agreement with MOBILE6 and MOVES models. Together with a searchable transit vehicle emissions test results database and a life cycle cost model developed under contract to the Transit Cooperative Research Program, the IBIS emissions model will provide a valuable resource for transit bus procurement activities.

13 13 FIGURE 7 Comparison of IBIS, MOBILE6, and MOVES emissions for diesel transit buses.

14 14 REFERENCES 1. Clark N., F. Zhen, S. Wayne, J. Schiavone, C. Chambers, A. Golub, and K. Chandler. Assessment of Hybrid-Electric Transit Bus Technology. Publication TCRP Report 132. Transit Cooperative Research Program, Transportation Research Board of the National Academies, Washington D.C., The State of the Air The American Lung Association, Washington D.C., 2010, assessed July 21, Woodruff T., J. Grillo, and K. Schoendorf. The Relationship between Selected Causes of Post Neonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental Health Perspectives, Vol. 105, 1997, pp Janssen N., T. Lanki, G. Hoek, M. Vallius, J. Hartog, R. Grieken, J. Pekkanen, and B. Brunekreef. Associations between Ambient Personal and Indoor Exposure to Fine Particulate Matter Constituents in Dutch and Finnish Panel of Cardiovascular Patients. Occupational Environ Medicine, Vol. 62, 2005, pp Hong Y., J. Lee, D. Kin, E. Ha, J. Schwartz, and D. Christiani. Effects of Air Pollutants on Acute Stroke Mortality. Environmental Health Perspectives, Vol. 110, No. 2, 2002 pp Rudell, B., M. Ledin, U. Hammarstrom, N. Stjernberg, B. Lundback, and T. Sandstrom. Effects on Symptoms and Lung Function in Humans Experimentally Exposed to Diesel Exhaust. Occupational Environmental Medicine, Vol. 53, 1996, pp Avol E., A. Gauderman, S. Tan, S. London, and J. Peters. Respiratory Effects of Relocating to Areas of Differing Air Pollution Levels. American Journal of Respiratory and Critical Care Medicine, Vol. 164, 2001, pp Kilburn K. Effects of Diesel Exhaust on Neurobehavioral and Pulmonary Functions. Archives of Environmental Health, Vol. 55, No. 1, 2000, pp Diesel Emissions and Lung Cancer: Epidemiology and Quantitative Risk Assessment A special Report of the Institute s Diesel Epidemiology Expert Panel. Heath Effects Institute, June Ritz B., F. Yu, S. Fruin, G. Chapa, G. Shaw, and J. Harris. Ambient Air Pollution and Risk of Birth Defects in Southern California. American Journal of Epidemiology, Vol. 155, No. 1, 2002, pp Proposed Identification of Diesel Exhaust as a Toxic Air Contaminant: Health Risk Assessment for Diesel Exhaust. California Air Resources Board, Sacramento, CA, May accessed August 2, Carcinogenic Effects of Exposure to Diesel Exhaust, NIOSH Current Intelligence Bulletin 50. Publication no National Institute for Occupational Safety and Health, Washington D.C, Public Transportation Fact Book. American Public Transportation Association, Washington DC, Alternative Fuels Study: A Report to Congress on Policy Option for Increasing the Use of Alternative Fuels in Transit. United States Department of Transportation Federal Transit Administration, Washington D.C., assessed August 2, Final Regulation Order Amendments to the Zero-Emissions Bus Regulation, California Code of Regulations, Title 13, Sections , and California Air Resources Board, Sacramento, CA. accessed January 22, Rule 1196 Clean On-Road Heavy-Duty Public Fleet Vehicles, South Coast Air Quality Management District, Diamond Bar, CA. accessed February 12, Connecticut Clean Diesel Plan Special Act No Connecticut Department of Environmental Protection, January accessed July 20, 2010.

15 MOBILE6 Vehicle Emissions Modeling Software. United States Environmental Protection Agency, Office of Transportation and Air Quality, Washington D.C. accessed July 22, MOVES Motor Vehicle Emission Simulator. United States Environmental Protection Agency, Office of Transportation and Air Quality, accessed July 22, EMFAC the Emissions Factors Model. California Air Resources Board, Sacramento CA. accessed July 22, O Keefe M. P., A. Simpson, J. Kelly, and D.S. Pedersen. Duty Cycle Characterization and Evaluation towards Heavy Hybrid Vehicle Applications. Publication no SAE International, Warrendale PA, Hiroyasu H., H. Miao, T. Hiroyasu, M. Miki, J. Kamiura J, and S. Watan. Genetic Algorithms Optimization of Diesel Engine Emissions and Fuel Efficiency with Air Swirl, EGR, Injection Timing and Multiple Injections. SAE Transactions, Vol. 112, No. 4, 2003, pp Davis, L. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, New York 1991.