Real-Time Measurements of Nitrogen Oxide Emissions from In-Use

approximately 170 in-use New York City mass transit buses were sampled ..... Brooklyn between October 23-November 2, 2000, and July. 3-August 3, 2001...
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Environ. Sci. Technol. 2005, 39, 7991-8000

Real-Time Measurements of Nitrogen Oxide Emissions from In-Use New York City Transit Buses Using a Chase Vehicle J O A N N E H . S H O R T E R , * ,† SCOTT HERNDON,† MARK S. ZAHNISER,† DAVID D. NELSON,† JODA WORMHOUDT,† KENNETH L. DEMERJIAN,‡ AND CHARLES E. KOLB† Center for Atmospheric and Environmental Chemistry, Aerodyne Research, Inc., 45 Manning Road, Billerica, Massachusetts 01821, and Atmospheric Sciences Research Center, State University of New York, Albany, New York 12203

New diesel engine technologies and alternative fuel engines are being introduced into fleets of mass transit buses to try to meet stricter emission regulations of nitrogen oxides and particulates. Real-time instruments including an Aerodyne Research tunable infrared laser differential absorption spectrometer (TILDAS) were deployed in a mobile laboratory to assess the impact of the implementation of the new technologies on nitrogen oxide emissions in real world driving conditions. Using a “chase” vehicle sampling strategy, the mobile laboratory followed target vehicles, repeatedly sampling their exhaust. Nitrogen oxides from approximately 170 in-use New York City mass transit buses were sampled during the field campaigns. Emissions from conventional diesel buses, diesel buses with continuously regenerating technology (CRT), diesel hybrid electric buses, and compressed natural gas (CNG) buses were compared. The chase vehicle sampling method yields real world emissions that can be included in more realistic emission inventories. The NOx emissions from the diesel and CNG buses were comparable. The hybrid electric buses had approximately one-half the NOx emissions. In CRT diesels, NO2 accounts for about one-third of the NOx emitted in the exhaust, while for non-CRT buses the NO2 fraction is less than 10%.

Introduction Emissions of oxides of nitrogen (NO and NO2, collectively referred to as NOx) contribute to a variety of environmental problems, including photochemical smog, acid deposition, adverse human health impacts, and visibility reduction (1). For example, ground level ozone (smog) is formed when NOx and volatile organic compounds (VOCs) react in the presence of heat and light. Oxidation of NO2 by ambient OH radicals produces nitric acid contributing to acid deposition, while fine particle health effects and visibility reduction are compounded by the formation of ammonium nitrate secondary aerosol particles. There are also growing health * Corresponding author phone: (978)663-9500; fax: (978)663-4918; e-mail: [email protected]. † Aerodyne Research, Inc. ‡ State University of New York. 10.1021/es048295u CCC: $30.25 Published on Web 09/08/2005

 2005 American Chemical Society

concerns associated with exposure to direct diesel exhaust. Recent evidence indicates that diesel exhaust or diesel particulate matter can cause lung cancer in humans, as well as other respiratory problems (2). As the NOx emissions of passenger cars and light-duty vehicles have become more tightly controlled, the relative importance of heavy-duty diesel vehicles as a NOx source has increased. The 1999 EPA National Emissions inventory estimated that diesel vehicles emitted 42% of the on-road NOx emissions in the U.S. versus 33% from cars and motorcycles, 19% from light gasoline trucks, and 5% from heavy gasoline vehicles (3). A recent assessment of on-road emissions estimated that diesel mobile sources contribute as much NOx as gasoline mobile sources; it also indicated that large uncertainties remain about the magnitude and distribution of these emissions (4). Another study found that California’s emission inventory model may underestimate NOx emissions from heavy-duty diesel trucks by up to a factor of 2.3 (5). Tunnel studies of NOx and PM emissions in California indicate that heavy-duty diesel vehicles are responsible for nearly 50% of the NOx and 75% of exhaust fine particle emissions from on-road motor vehicles (6). The growing recognition of the harmful effects of diesel emissions on air quality and human health led the U.S. EPA to propose new heavy-duty engine and vehicle standards on May 17, 2000 (2). This major regulatory initiative addresses the problem of PM and NOx emissions by setting much stricter standards for emissions and for the sulfur content of diesel fuel. The restriction on sulfur content is needed because sulfur oxides in vehicle exhausts may poison catalytic converters designed to reduce NOx emissions. The regulation calls for a factor of 20 reduction in the NOx standard, to be phased between the years 2007 and 2010. As a result, each new bus would be as much as 95% cleaner than today’s conventional buses. Engine manufacturers and heavy-duty vehicle users, including transit authorities, have begun to take actions to address the changing standards. New diesel technologies and engines using alternative fuels are being developed and deployed. The New York City Metropolitan Transit Authority (NY MTA) has been working to reduce emissions from its fleet of buses. It has initiated deployment of several different new technologies in its fleet, including compressed natural gas (CNG) buses, hybrid electric buses, and diesel buses equipped with continuously regenerating technology (CRT) designed to reduce fine particulate emissions. In addition to testing the in-use performance of these vehicles, the NY MTA has solicited emission testing in dynamometer tests and quantitative real world in-use emission testing. On-road NOx emission measurements are the subject of this paper. A companion paper in this issue presents formaldehyde, methane, and sulfur dioxide emission results from this study (7). A previous paper has presented data on fine PM emissions measured at the same time (8). Emission inventories for pollutant emissions from heavyduty vehicles are based on engine certification data from dynamometer tests following the Federal Test Procedure (FTP) (9). In the FTP certification tests, the engine (not installed in a vehicle) is run in a transient manner over a range of load and speed set points while measuring emissions using specified procedures. Regulated pollutants are reported in units of mass emitted per unit work (in grams per brake horsepower hour (g/bhp-h), or grams per kilowatt hour (g/ kW-h)). The EPA then uses models that combine these emissions numbers with vehicle sales data to get heavy-duty vehicle emission factors. The use of certification results for VOL. 39, NO. 20, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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predicting real use emissions is problematic. Issues include the representativeness of the FTP for actual vehicle use, and the ability to estimate the deterioration in emissions over the engine lifetime. A recent review of heavy-duty vehicle emissions showed that measured trends were different from both certification test data and from model output (10); yet the certification-based emissions are routinely used in air quality evaluations and inventories. The inventories do not take into proper account parameters that probably have significant impact on actual emissions, including vehicle class, weight, vehicle age, terrain traveled, driving cycle, quality and frequency of maintenance, and vehicle vocation (11). The errors in the emissions based on the certification estimations are thus propagated. The fraction of NOx as NO2 in exhaust is also not well described by emission inventories. Carslaw and Beevers (12) have recently formulated a preliminary primary NO2 emission inventory for road transport sources in London, but call for a large database to strengthen the robustness of the inventory. Emission inventories and air quality management plans can be improved through the application of measurement methods to quantify emissions from in-use heavy-duty vehicles. Real world measurement of NOx emissions is also important for evaluating the effectiveness of emission controls and to verify emissions from new classes of heavyduty vehicles (e.g., CNG and hybrid electric). On-road NOx emissions have been evaluated through chassis dynamometer testing (10, 11, 13-15), tunnel studies (6, 16-18), and on-road remote sensing (10, 19-22). These techniques have been recently reviewed with relationship to heavy-duty diesel emissions (10). Each of these three measurement methodologies have their respective strengths and limitations. Chassis dynamometer studies are not real world measurements because they are performed in an artificial environment, which usually fails to simulate the full range of real world driving parameters. The low sample size of dynamometer studies, due to their high cost, adds uncertainty to the results and limits how well they might represent the actual fleet. Tunnel studies are made in the real environment, but do not easily provide emissions by vehicle type; an ensemble average of all vehicles passing through the tunnel is obtained. Cross road remote sensing measurements allow a large number of individual vehicles to be sampled, but each vehicle is usually sampled only under a single driving condition. On-board measurement systems (23) and mobile instrument platforms (24-29) are in development and starting to be deployed to characterize onroad emissions. Real world on-road measurements of a heavyduty diesel (HDD) vehicle by a mobile laboratory being pulled by the HDD vehicle have been reported (30). This arrangement provides total capture gaseous measurements. Although it provides important real world data on HDD emissions, the system is limited in the number and types of vehicles it can study. Over the past few years, we have implemented another technique to quantify individual vehicle in-use emissions (8, 24, 31, 32), chase measurements with our instrumented van. Similar studies have been reported for on-road heavy-duty vehicles by Kittelson and co-workers (26-28) and by Vogt et al. (33) for light-duty vehicles on high-speed test track. Johnson and Caldow have also reported making chase experiments (34). In the chase vehicle/plume extraction method, a mobile laboratory equipped with sensitive instrumentation selects and “chases” individual vehicles to sample their emission plume. Emissions can be monitored for many different driving conditions, terrains, environmental conditions, etc., and for different vehicle types. We have successfully deployed both gas-phase monitoring instruments and particulate characterization instruments in the mobile laboratory for chase experiments (24). This paper 7992

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presents the application of this new technique to the evaluation of heavy-duty vehicle NOx emissions from the New York City bus fleet. The emission measurements of this paper were conducted as part of the CNG/CRT Emission Perturbation Experiment (CEPEX) during the PM2.5 Technology Assessment and Characterization Study in New York City (PMTACS-NY). It included a 2-week demonstration period in October 2000 and a 5-week intensive measurement campaign in summer 2001. The goal of the study was to characterize new and existing bus technologies of the New York Metropolitan Transit Authority (MTA) and to contrast them with the rest of the New York heavy-duty fleet. This project was possible through the cooperation of the MTA. They provided the classification information of each MTA bus in a detailed database of vehicle and engine information.

Experimental Section All of the measurements presented in this paper were obtained with the Aerodyne Research (ARI) Mobile Laboratory, a step-van deployed with a series of sensitive real-time instruments for the measurement of gaseous and particulate emissions (24). Prior to these measurements, the mobile laboratory was used extensively in mobile field measurements of gas-phase species, including gaseous emissions from natural gas systems (35, 36), landfills (37), and urban areas (31, 38, 39). During the New York City campaigns, gas-phase species were measured with an ARI two-color tunable diode laser (TDL) tunable infrared laser differential absorption spectrometer (TILDAS) and a NDIR Licor CO2 instrument, while particulate species were detected with an ARI Aerosol Mass Spectrometer (AMS), a condensation particle counter (CPC), and an electrical low-pressure impactor (ELPI). Additional instruments in the mobile laboratory include a global positioning system (GPS), thermocouple for sample temperature, a video camera monitoring conditions forward of the mobile lab, and a central data logging computer. Interactive notetaking systems were used to collect timestamped experiment notes and observeration. A 5 kW gasoline generator (Honda EZ5000) mounted on a platform on the back of the van provides all of the required power when the truck is moving. We conducted all measurements while traveling at normal roadway speeds. Continuous air samples are drawn through an inlet located on the driver side bulkhead at the front of the van. Two different inlet systems were used in the 2000 and 2001 campaigns. During the 2000 campaign, the TDL and Licor sampled from a common port located at a height of 2.4 m, approximately 5 cm above a second inlet for the particle instruments (AMS, CPC). For the summer 2001 campaign, a common inlet was constructed for the particulate and gas-phase instruments. This inlet was 2.4 m from road level. The common inlet was a 1” OD stainless steel tube, which protruded forward from the driver side bulkhead. The total flow through the inlet was 18.9 L per minute (LPM). Of this total flow, 8.9 LPM was delivered to the gas-phase instruments. The 2.11 cm ID main inlet tube extended 1.2 m forward of the bulkhead. The longer inlet was used to provide sufficient time for laminar flow to develop before the flow was split, to avoid possible vehicle boundary layer sampling artifacts at the mobile lab bulkhead, and to get the inlet closer to the top emitting diesel buses. On average, the longer inlet resulted in increased signal intensity from top emitting buses, because there was less dilution of the exhaust with the inlet in closer proximity. The signal intensity for low emitting buses (i.e., buses with exhaust pipe near the ground or at the bottom of the chassis body) was slightly less than that with the shorter inlet. The reduction is due to sampling of a less concentrated portion of the bus exhaust plume than

with a shorter inlet. Although the height of the inlet was unchanged, the shorter inlet captured the exhaust entrapped in the mobile lab bulkhead air and resulted in a higher signal intensity than that obtained with the longer inlet. Inside the mobile lab, the flow is isokinetically split to provide sample to the different instruments in the mobile lab. After the first isokinetic split, the TILDAS and Licor systems sampled through 1/4” PFA Teflon tubing of 3.8 and 4.6 m length, respectively. The respective sample flows to the TILDAS and Licor instruments were ∼9000 and ∼500 sccm. Instrumentation. The principal gas-phase instrument on the mobile lab was the TILDAS instrument, configured for NO and NO2 detection during the measurements presented in this paper. The high sensitivity, fast response two-color TDL was developed at ARI (20, 40). This instrument simultaneously operates two infrared diode lasers mounted in a single liquid nitrogen dewar, measuring between two and four species at one time. The lasers are scanned across distinct resolved absorption lines, including background to either side of the lines, at a rate of 3 kHz. The absorption features are fit in real time using a nonlinear least squares algorithm, HITRAN (41) tabulated line parameters, and full Voigt line shapes. Absolute concentrations are recorded without the need for calibration gases. The light from the two lasers is directed on separate paths through a long multipass low pressure, low volume absorption cell (153 m path length, 5 L), operating at ∼40 Torr. After passing through the cell, the beams are directed to separate infrared detectors housed in the dewar. The instrument sensitivity is generally 1 ppbv (parts per billion by volume) at a data rate of 1/s, and the gas flow replacement rate is equal to the data rate. Pressure inside the cell was continuously monitored with a 1 atm MKS Baratron. A total of eight lead salt tunable diode lasers (four on each of two mounts) can be housed in the liquid nitrogen dewar. One laser on each mount is operated at a time, but it is easy to switch between lasers. During the fall 2000 campaign, we used this instrument to measure a number of different species. In the first week, NO (1912 cm-1) was measured with 1 laser and NO2 (1600 cm-1) with a second laser. In the second week, we measured CH4 (1348 cm-1) with one laser and N2O and CO (2212 cm-1) with a second laser for 2 days, replacing CH4 with SO2 (1349 cm-1) as a target species on the last 3 days of the campaign. In summer 2001, the pairs of target species were NO (1909-1935 cm-1) and NO2 (15931601 cm-1), H2CO (1700 cm-1) and CH4 (1348 cm-1), and H2CO and SO2 (1349 cm-1). An accompanying paper describes the CH4, H2CO, and SO2 emissions. In the summer campaign, NO and NO2 were targeted for 3 of the 5 weeks of the campaign. During this time, we monitored different NO and NO2 lines as necessitated by changes in the laser mode characteristics. Nitrogen dioxide lines at 1593.3, 1601.3, and 1583.1 cm-1 were monitored. The NO lines at 1909, 1935, and 1930 cm-1 were monitored at different times during the campaign, with the NO lines at 1909 cm-1 the target region for all but a portion of the last week. At all times, mode purity was confirmed using reference cells and nearby optically thick water lines. In the TILDAS method, the accuracy of the mixing ratios is largely determined by how well the line strengths are known. For the NO and NO2 absorption lines used in this study, the presently accepted band strengths are known to within 6% and 4%, respectively (42). We measured carbon dioxide, CO2, with a Licor NDIR instrument (LI-6262) with a 1 s response time and 1 ppmv (part per million by volume) sensitivity. The instrument sampled a small flow from the same inlet as the TDL. Carbon dioxide is a reference gas, which provides a measure of the

extent of dilution of the exhaust plume and allows computation of the emission rate of the other measured exhaust species. The TILDAS and Licor instruments were tested and calibrated in the laboratory prior to deployment. Additional ancillary measurements were made during the campaigns. A bare thermocouple in the sample line yields a continuous measure of the temperature of the sample. A Trimble global positioning system (GPS) yields position data and, consequently, the velocity and acceleration of the mobile lab. A video camera, capturing at one frame every 2 s, is aimed out of the front of the Mobile Laboratory. A visual record of the bus number and type, as well as the roadway condition, is made. An onboard notetaking scheme was deployed by operators, whereby “events” were recorded on multiple computers. This included traffic conditions and whether the mobile laboratory was chasing a bus. During a bus chase event, information including the bus company, number, type, etc. was recorded. Onboard computers were time synchronized to a common time by means of Clockwatch software (Beagle Software). Measurement Strategy. Numerous in-use buses and heavyduty vehicles (e.g., semi-trucks, sanitation trucks, box vans, fuel delivery trucks) were sampled in a range of urban areas. Approximately 300 in-use buses were “chased” on their normal, scheduled routes in Queens, Manhattan, Bronx, and Brooklyn between October 23-November 2, 2000, and July 3-August 3, 2001. Of these buses, NO and NO2 emissions were monitored for 168, including 105 in the New York City Metropolitan Transit Authority (MTA) fleet, 58 non-MTA transit buses, four school buses, and a private coach bus. The MTA fleet of buses was the focus of much of the measurement campaign. This fleet includes traditional diesel buses, alternative fuel buses running with compressed natural gas (CNG), and diesel buses with new emission control technologies. This last group includes diesel-fueled buses with Continuously Regenerating Technology (CRT) and electric/diesel hybrid buses. All of the diesel-fueled buses in the MTA fleet had also switched in early October 2000 to a low sulfur fuel, containing no more than 30 parts-per-millionby-weight (ppmw) sulfur. The cooperation of MTA authorities was obtained in advance. The MTA provided detailed information on the individual MTA buses (e.g., engine type, year built, fuel type, etc). A smaller sample of non-MTA transit buses, school buses, and coach buses in New York City was also examined. The non-MTA transit buses are part of independent bus companies operating as contractors for the MTA. These companies include the Queen’s Surface Corp., The Green Line, and the Command Buses. Although the fuel type (diesel versus CNG) could be identified for each bus, more detailed engine or usage information was not available. These buses are grouped by fuel type for analysis and comparison purposes. The emission measurements were made in all boroughs of New York City, with the exception of Staten Island, to sample buses based at different MTA depots. The buses were followed while driving their normal routes without notification of the operators. The mobile laboratory attempted to follow or “chase” each bus through several cycles of acceleration, cruise, deceleration, and braking/stopping. In general, the mobile laboratory followed directly behind a bus, mimicking its driving pattern as much as possible, including stopping directly behind the bus when it stopped at bus stops to load and unload passengers. Traffic occasionally precluded us from continually following a particular bus. The continuous measurement of carbon dioxide from all vehicles was used to differentiate plumes from different mobile sources. The identification of contaminated bus plumes was accomplished through the combination of CO2 VOL. 39, NO. 20, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Example of chase event data obtained while following an individual bus with the mobile laboratory. In the left-hand panel, each peak in CO2 represents sampling of an exhaust plume. In the right-hand panel, the correlation between NO and CO2, and NO2 and CO2, is used to obtain molar emission ratios. The ratios are given in the plot in units of ppb/ppm CO2. The open data points in the right-hand plot are points that are not included in the fit because they were associated with interferences. data, video images, and operator notes during chase events. Forward-looking video images identified the traffic level and when a nontarget vehicle drove into the gap between the mobile lab and the target. Self-contamination can also be a problem in low speed periods. These periods are identified and eliminated from event analysis. Clear changes in emission ratios, such as in the NO2/NOx ratio when following CRT diesels, serve as markers of possible contamination.

Results and Discussion Emission ratios for trace gas species in vehicle exhaust were determined from real-time data through the correlation of the trace species with the carbon dioxide in the exhaust. Individual time segments of exhaust data are associated with particular buses or other events by association of the data with time stamped documentation made during the event, as well as from the digital pictures of the traffic in front of the mobile lab. A detailed description of the analysis routine was given in a previous publication (43). The analysis procedure is illustrated in Figure 1 with typical data collected when sampling the exhaust of a diesel bus. The left side of Figure 1 shows real-time data collected when sampling an MTA diesel bus over approximately 6 min. The NO and NO2 plumes are clearly correlated with the CO2 in the exhaust. During a typical curbside passenger bus chase event, plumes of 4-10 s in duration (fwhm) of varying elevation are encountered, with plumes occurring during ∼30-60% of the overall duty cycle during the event. The plume strengths during the chase are also greater than the background variability. An additional example of plume strengths and their occurrence is given in the companion paper (7). The real-time data are used to create correlation plots of the data segment, as shown in the right-hand panel of Figure 1. Linear correlations of the NO2 with CO2 and NO with CO2 are shown in the figure. The result is a molar emission ratio in units of µmol of NOx to µmol of CO2. The individual NO and NO2 emission ratios for the bus of Figure 1 are (8.2 ( 0.18) × 10-3 µmol NO/µmol CO2 and (0.0975 ( 0.0028) × 10-3 µmol NO2/µmol CO2. The open data points in the plots are points that are not included in the fits because they were associated with periods of known interference (e.g., exhaust from another vehicle was sampled). During each chase event, the bus operates in a variety of driving modes. It is important to assess the degree to which 7994

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variations in this driving cycle (or in our sampling of this cycle) affect our overall emission measurements. We have done this for a few buses by dividing each chase event into subsections with characterized driving states. The drive states (i.e., idle, acceleration, cruise, and deceleration) are obtained from information from the GPS and rangefinder on the mobile laboratory. Details on the analysis and the definition of each state are given in detail in Herndon et al. in this issue (7). The states are defined as follows: idle is when the bus was stopped; accelerating was when the bus’ acceleration was >0.2 m s-2 and the velocity was between 1 and 5 m s-1; cruise was when the bus’ velocity was over 6.7 m s-1; and decelerating was when the velocity was over 1 m s-1 and the acceleration was less than -0.67 m s-2. The purpose of a simple coarse characterization of drive state was to determine if there was any effect on emissions. Analysis of the subsections (usually individual plumes) shows that the variations in the emissions of NOx between buses are generally significantly larger than the variations for a single bus as a function of its driving cycle. In other words, the emission ratios that we report are at most weakly dependent on drive cycle and are strongly dependent on which bus is being measured. Two examples of this analysis by driving state demonstrate these conclusions. The two MTA Series 50 diesel buses depicted in Figure 2 were measured in the fall 2000, within 1.5 h of each other. The plumes encountered while chasing the buses, similar to those in Figure 1, were analyzed individually and characterized as to driving state. NOx/CO2 ratios for each plume were determined from each correlation plot. Each of these ratios is plotted in the top portion of Figure 2, with its corresponding 1σ error, as a function of bus drive state during the plume (deceleration, cruise, acceleration, or idle). Over the course of these measurements, we did not observe a consistently strong relationship between the NOx emission ratio and the deduced “drive state” of the diesel bus being chased. These data are carried over to the histogram (frequency versus emission ratio) on the bottom of Figure 2. The distributions of the plume emission ratios of the two buses are clearly different. Furthermore, the NOx emission ratios calculated for each total event (bold black symbols in figure) are in excellent agreement with the distributions. The difference in total emission ratio for these two Series 50 diesel buses is a function of the bus and not the uncertainties

FIGURE 2. Plume-by-plume analysis of two Series 50 diesel buses. The figure shows a plume-by-plume analysis of two chase events: MTA#6068, Orion 1999 (blue data), and MTA#8981, Nova 1996 (brown data). The top panel shows the individual plume results for isolated plumes of CO2 in excess of ∼40 ppm as a function of the deduced drive state. The bottom panel is a histogram of the plume-by-plume data. Each black point in the middle of the figure corresponds to the whole event analysis described in the text. This figure demonstrates the ability of the chase method to distinguish two buses with different emission levels (see text for a discussion of additional implications). associated with the measurements or the chase methodology. A detailed plume-by-plume analysis is also given in the companion paper with similar results obtained for other trace species in the bus exhaust (7). The molar emission ratio is formed from the trace species concentration (above background) and the CO2 concentration (above background), where the CO2 level in the exhaust is a measure of fuel consumption. By assuming a carbon content in the fuel, one can convert the molar emission ratio to a ratio in terms of the fuel consumption, that is, units of g NOx per kg fuel. These units are particularly appropriate with the mobile data where a measure of fuel consumption (observed CO2) is available, but gas mileage during the chase is not. A true accounting of carbon species from fuel would require the addition of CO and hydrocarbon emissions to CO2, but these emissions are generally too small for diesel vehicles to be significant in the total carbon balance (10). In addition, Yanowitz et al. (10, 13) have compared NOx emissions from different drive cycles in dynamometer testing and shown that the NOx emission rate is independent of drive cycle when the rate is expressed on a fuel consumption basis (g/gal or g/kg fuel) as compared to when expressed in units of g/mile. When particulate matter and carbon monoxide emission rates are expressed on the same basis (g/gal), variation as a function of drive cycle can still be observed. The carbon content, based on typical fuel composition, is assumed to be 3180 g CO2/kg fuel for diesel fuel and 2757 g CO2/kg fuel for compressed natural gas (CNG) (95% CH4 in CNG). The NOx emission ratios are reported here as equivalent NO2. This was chosen to be consistent with the accepted units for mass emissions for purposes of compliance with heavy-duty emissions standards. Validation of Methodology. The emission ratio of individual trace species in the exhaust is determined using CO2 as the exhaust dilution tracer, as described above. The chase measurement method can be described as essentially the measurement of a diluted mixture of trace gases (e.g., NO, NO2) and a tracer gas (CO2) by instrumentation deployed in a mobile laboratory. This is analogous to previous studies

with the ARI mobile laboratory in which emissions from stationary point sources, including natural gas facilities and landfills, were determined with atmospheric tracer measurements (35-37, 44). The method compares diluted trace gas mixing ratios to tracer gas (typically SF6) mixing ratios at downwind locations. Controlled release tests were conducted to validate the method, as well as intercomparisons of realtime data with downwind canister samples (35). Results for controlled release experiments had excellent agreement, as did the comparison between canister and real-time results. Details of the validation tests are given in Lamb et al. (35). Landfill results from the mobile laboratory have also been compared with static enclosure methods (44). In all of these tests, excellent agreement between the different methods was obtained. A concern that often surfaces in attempts to assign a single emission factor to a category encompassing a large, potentially variable sample set is how representative are the measurements used to generate the emission factor? With regards to the measurements in this paper, there are two aspects to this question. First, have the individual buses been characterized under operating conditions representative of typical conditions, and such that the emissions reported for each bus are free of any systematic bias? We must also consider whether the buses that were studied are representative of the overall fleet of buses in NYC. An attribute of our measurement approach is that it is able to address both of these concerns. With respect to the first concern, the emissions of the buses were measured while in-use under real world driving conditions, and over their entire range of driving conditions. The issue of fleet representation was considered through random selection of individual buses. Measurements were also conducted in four of the five borough of NYC. We estimate that we only measured about 4% of the total MTA fleet; nevertheless, this still is a significant number of vehicles and an unbiased sample to the best of our knowledge. Emissions by Heavy-Duty Vehicle Category. The Aerodyne TILDAS system simultaneously performs independent measurements of NO and NO2 with high accuracy. Using this technique in conjunction with CO2 detection, emission ratios were obtained for MTA and non-MTA buses, as well as other heavy-duty and light-duty vehicles. The total NOx emission ratio and the fraction of NOx as NO2 for each vehicle were determined. The results of the bus chase events from the field campaigns in 2000 and 2001 are given in Figure 3 and Table 1. The MTA diesel buses are categorized by their fuel type (diesel versus CNG) and by engine type. All of the MTA diesel bus engines examined in the NOx studies were manufactured by the Detroit Diesel Corp. (DDC). The traditional diesel buses had either 6V-92 or Series 50 engines. The 6V-92 is a common transit bus engine produced in the 1980s. It is a two-stroke engine with six cylinders. The Series 50 engine is a newer four-stroke, four-cylinder engine model. It has been widely used in transit buses since 1993, in part as a result of tightening emission standards. However, many 6V-92 engines are still in use today. Note that there are also MTA diesel buses with Cummins engines, but these were not monitored in the NOx measurements. The NOx/CO2 and NO2/NOx emission ratios for the MTA 6V-92 and Series 50 buses in our study are comparable. The non-MTA diesel buses, all of standard type but with unknown engine type, also had emissions comparable to the MTA standard diesels. The MTA and non-MTA buses burned diesel fuel with significantly different sulfur content. MTA buses used ultralow sulfur diesel (ULSD), which contains 100

5 4 4 2 2 2 131 60 6 5 8 1 3 13 6 29 41 22 3 10 36 22 4

a CD ) chassis dynamometer. b NO emissions reported as mean ( 95% confidence interval, unless otherwise noted. Values in parentheses x are the range of results. c Assumes diesel bus fuel economy of 3.9 mile/gal (45) and fuel density of 7.1 lb/gal ()3.22 kg/gal). d Assumes CNG bus fuel economy of 3.4 mile/gal (45) and fuel density of 2.43 kg/gal. e 1σ standard deviation of the mean, determined from reported results.

was necessitated by the deployment of CRT diesel buses, which require the ULSD fuel. Three hybrid electric buses were sampled in our study. The hybrid electric system combines a diesel engine with 7996

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electric drive components and electric energy storage capability. This reduces the total energy (fuel) used by the vehicle, increases the fuel economy, and subsequently can lead to lower total vehicular emissions. At the time of the

FIGURE 4. Comparison of typical NO, NO2, and CO2 chase data from a standard diesel bus (on left) and a CRT equipped diesel bus (on right). While the CO2 and NO levels in the exhaust of both buses were comparable, the NO2 emissions from the CRT bus are clearly higher. study, the MTA had only 10 prototype hybrid-electric buses in revenue service. In NYC, the hybrid electric buses in service have shown an average of 10-30% improvement in fuel economy in comparison to the standard diesel buses (45). The NOx emissions from the hybrid electric buses were a factor of about 2 lower than the standard diesel. NOx emissions from CNG buses were comparable to those from the diesel buses. This is consistent with results by Wang et al. (14), who found insignificant NOx reductions in CNGs as compared with comparable diesel buses. Lanni et al. (46) report similar results for dynamometer tests on a small number of New York City buses. Other studies, however, suggest that with careful engineering and maintenance, CNG engines have the capacity for greatly reduced emissions relative to diesel engines (45, 47). Variability of NOx emissions from CNG buses has been observed by several other groups (45, 46, 48). In addition to the effects of bus routes on emissions, possible causes of the variability that have been suggested are vehicle maintenance and variations in the composition of the natural gas, which leads to changes in the stoichiometry and octane number. Higher NOx emissions have also been observed during backfiring events, which might be associated with vehicle maintenance (45). There was variability in the NOx emission ratios within individual categories of buses. In Table 1, the 95% confidence limit in the mean of our measurements is given, as well as the range of the values in each category. In comparison, the 1σ of the emission rate of single bus event is on average ∼3% of the rate. The NOx/CO2 bus-to-bus variability for a single bus/engine type is likely a result of a combination of factors. Operating parameters, including temperature and humidity, maintenance of the individual buses, driver habits, and characteristics of the bus route, may affect the NOx emission ratios. The buses were monitored while driven in actual inuse real world environments on their regular bus routes. As the bus operators were uninformed of the sampling being performed, no attempt was made to operate the buses in any prescribed mode other than the individual operator’s normal driving habits. The total NOx for MTA and non-MTA diesel buses was approximately 31-38 g NOx/kg fuel with some deviating buses. The three MTA hybrid electric buses had lower emission ratios of 17.2 g NOx/kg fuel. Emission ratios for CNG buses were comparable to those of diesel buses, but the emission ratios of the non-MTA CNG buses were slightly lower. Nitrogen Dioxide from CRT Diesel Buses. While the mean NOx emission ratios for the different bus types vary little, the

contribution of nitrogen dioxide (NO2) to total NOx for the different bus categories does vary. The total NOx emissions from heavy-duty vehicles are typically dominated by nitric oxide (NO). This was observed with the exception of the diesel buses with Continuously Regenerating Technology (CRT). As we see in typical data in Figure 4, the ratio of NO2/NOx is significantly higher in the exhaust of buses equipped with CRTs than in the exhaust of traditional diesel buses. Continuously Regenerating Technology (CRT) is a type of exhaust after-treatment designed to reduce fine PM from diesel buses. A combination of an oxidation catalyst and a particulate trap filter reduces both the gaseous and the particulate emissions. The exhaust passes through the catalyst to oxidize carbon monoxide (CO) and hydrocarbons (HC) and to convert the majority of the NOx in the exhaust to NO2. The NO2 is subsequently used to oxidize the particulate matter (PM) in the particulate trap. The CRT operates without fuel additives or heater control system. However, it does require low sulfur fuel because sulfur degrades the catalytic reactions (11). Our data bear out that NO2 is emitted by CRT buses; on average, one-third of NOx is NO2 in the exhaust, while for non-CRT buses the fraction is less than 10%. This increase in the fraction of NOx as NO2 in CRT diesels in comparison to standard diesel buses has been observed by others (49, 50). In a dynamometer study of a CRT equipped diesel bus with Series 50 engine, Ayalo et al. (49) reported that NO2 constituted approximately 40-50% of the total NOx by weight, depending on the drive cycle. Tang et al. (50) report a NO2/ NOx volume ratio of ∼50% for the CRT equipped buses in their study, and 7-14% for five conventional diesel buses without CRT. The NO2/NOx ratios for conventional diesel and CNG buses in their study were all higher than in our study, but include only a very small sample set. Although slightly higher than in our study, both of the above studies had only 1 or 2 CRT equipped buses in their sample set. The slight difference is therefore not surprising. The direct primary emission of NO2 is of significant concern. It is of importance to direct health effects on and near affected roads (51) and could be a significant contributor of NO2 in urban areas, such as street canyons. This is a problem because NO2 has a higher toxicity as compared to NO. Direct primary NO2 emissions also would lead to increased photochemical ozone production (52). It is thought that vehicle-produced smog in the U.S. depends more on NOx and NO2:NO ratio than on hydrocarbons (HCs) and CO (52). A primary NO2 emission inventory has been developed by Carslaw and Beevers (12) for vehicular traffic in London, based on a limited set of vehicle exhaust measurements. They estimate a mean value of NO2 to NOx of 11.3 vol %, VOL. 39, NO. 20, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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considerably higher than the 5% that is often assumed (12). Incorporation of our NO2/NOx results (∼30%) in the model would increase the primary NO2 emission inventory in London. Comparison with Dynamometer Studies. The measurement study presented in this paper is one of the first attempts to extensively quantify exhaust emissions from in-use transit buses. The standard technique to determine exhaust emissions is through the use of chassis dynamometers. In dynamometer testing, a predescribed driving cycle is followed in an attempt to simulate typical driving conditions. The most commonly used cycle is the Central Bus District (CBD) cycle. The CBD cycle has 14 successive acceleration, cruise, deceleration events, which cover 2 miles (3.2 km) in about 10 min. It is widely used as a metric for characterization of emissions. Other driving cycles for bus and other heavyduty vehicle emission tests include the New York Bus (NYB) cycle, more representative of New York operation, covering 0.5 miles with an average speed of 1.5 mph; the heavy-duty transient (HDT) cycle, representing heavy-duty driving in urban areas and covering various speeds including highway speeds; and the West Virginia Truck (WVT) cycle, to simulate typical driving of large trucks using five steps of increasing speed, steady run, and braking with a maximum speed of 40 mph. Our experimental methodology is critically different from chassis dynamometer testing. It studies in-use buses under real world conditions. The “chased” bus does not have a predescribed driving pattern to follow. Instead, it is driven in normal bus transit operation, on its normal bus route. In our experiments, the specific buses were not preselected for sampling and efforts were made to avoid interfering with normal operations. Emissions should therefore reflect normal operating procedures. Buses were measured as encountered, without prior knowledge of maintenance records or of the driving habits of the operator. The total reported NOx from the mobile lab is a sum of the individually measured NO and NO2. In most dynamometer studies, total NOx is reported. Table 1 compares the NOx emission ratios of buses as determined from our studies with reported emission rates from dynamometer studies. All of the results are expressed in units of g NOx/kg fuel in Table 1 to minimize the effect of different drive cycles, or driving habits, on the indices. In general, the ARI real world rates are similar to the dynamometer results. The NOx emission rates for diesel buses agree well, with the mobile lab results at the low end of the range of reported dynamometer emission rates. We can also look at the different engine types within the diesel category of buses. The MTA diesel buses with DDC 6V-92TA engines and Series 50 engines had comparable NOx emissions in our study. The emission rates of each engine type are within the error of the rates reported by the Prucz et al. review (15), which included a large sample size. The mobile results in general have more variability than the other studies. This reflects the variability of the vehicle maintenance and driver habits of real world driving. The average NOx emission rate from the CRT diesel buses from our studies (27.8 ( 6.3 g NOx/kg fuel) agrees well with the rates reported by Lowell (45), that is, 31.2 ( 5.4 and 30.9 ( 5.5 g NOx/kg fuel for CBD and NYB dynamometer cycles, respectively. Our results for CNG buses agree well with other reported values. Our average mobile MTA CNG NOx emission rate lies between the Wang (14) and Lowell (45) rates, while the nonMTA CNGs have a 40% lower emission rate. The Lowell results, which include the results of Lanni et al. (46), have one CNG bus with significantly higher emissions than the other CNG buses in the study. This bus was noted to backfire, with higher NOx emissions correlated to periods of backfiring. This bus is included in the average because it represents real 7998

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world in service behavior. There is evidence that buses experienced backfiring events during our studies, as seen both in changes in NOx and also in higher elevations in methane (7). At this time, we do not have a definitive explanation for why the non-MTA CNGs produced a lower NOx emission ratio in our study. Possible explanations are that more MTA buses had backfiring events, more frequent MTA backfiring events were observed, non-MTA buses had better maintenance, or the fuel ratio was different in MTA and non-MTA buses. Comparison to Tunnel and Remote Sensing Studies. The real world NOx emission ratios of the chase experiments can be compared to in-use emission ratios from tunnel and remote sensing studies presented in the literature, although no such literature results for buses only have been identified. Rather we can compare our results for diesel buses with heavy-duty diesel vehicles, which are predominantly trucks. We have also obtained NOx emission ratios for NYC tunnels, where we sampled NOx emissions from the ensemble of vehicles in the tunnels on five occasions (see Figure 3). Yanowitz et al. (10) reviewed tunnel studies to deduce emissions from heavy-duty diesel vehicles. The NOx emissions of this vehicle class in tunnel studies ranged from 38 to 49 g/kg fuel (see ref 10 and references therein). Kirchstetter reported heavy-duty and light-duty emissions in a California tunnel: 9.0 ( 0.2 g/kg fuel and 42 ( 5 g/kg fuel for light-duty and heavy-duty vehicles, respectively (6). The heavy-duty emission ratio, higher than the average diesel bus emission ratio that we observed in the chase experiments, reflects different vehicle types under different environmental and driving conditions (highway cruise versus city/bus routes). We also compare our NYC tunnel results with those of Kirchstetter. The NOx emissions in tunnels in NYC, 10.9 ( 5.3 g/kg fuel (assuming diesel fuel), are slightly higher than the light-duty vehicle result. Although the vehicles in the NYC tunnels were predominantly light-duty vehicles (automobiles, light-duty trucks, etc.), heavy-duty vehicles also contributed to the tunnel ensemble average NOx emissions, resulting in higher average NOx emissions ratios. There have been several remote sensing studies of heavyduty truck emissions. Jimenez et al. (19) used an Aerodyne TDL-TILDAS system configured for open path cross road detection of NO and NO2. They observed NOx emissions ranging from 35 to 38 g/kg fuel from heavy-duty diesels. Other remote sensing studies had average NOx emissions from 21 to 53 g/kg (see ref 19 and references therein). These results span the NOx emission ratios that we measured via chase experiments (see Table 1). The highest of the literature values, 53 g/kg, was from a study performed in Golden, CO (22). Bishop et al. (53) attribute the increased engine emissions to the higher altitude of the site (1800 m). The chase vehicle sampling method deployed by the ARI mobile laboratory yields real world vehicle emissions. The results of these measurements can be used to generate more realistic emission inventories. This is a task outside of the scope of this paper. The data, however, will be important to researchers presently building and evaluating emission inventories. As described above, our real world rates are similar to the dynamometer results. The NOx emission rates for diesel buses agree well, with the mobile lab results at the low end of the range of reported dynamometer emission rates. If the cited literature values are considered representative of emission factors used to generate emission inventories of NOx, inclusion of our results in the inventory models should cause a slight decrease in total NOx from transit buses in the inventory. Perhaps more important, the knowledge that the emission rates represent actual in-use drive cycle measurements would give increased confidence in the representation of the actual emissions.

The emission of NOx from transit buses will come under ever-increasing scrutiny as their relative importance grows with the increasing control of emissions from light-duty vehicles. Transit buses and other heavy-duty vehicles will likely be the targets of future environmental legislation. A baseline of the real world heavy-duty vehicle emissions is critical to the regulation process, as is the development of techniques and instrumentation to accurately quantify real world emissions.

Acknowledgments We thank the MTA for its cooperation, especially Chris Bush for bus fleet information and Dana Lowell for help in organizing the logistics of the fall 2000 campaign, the NYS DEC for providing drivers, and Queens College for logistical support during the summer 2001 campaign. This work was supported in part by a sub award with the State University at Albany under the U.S. Environmental Protection Agency (EPA) cooperative agreement # R828060010 and the New York State Energy Research and Development Authority (NYSERDA), contract # 4918ERTERES99. Although the research described in this Article has been funded in part by the U.S. Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.

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Received for review November 1, 2004. Revised manuscript received June 30, 2005. Accepted July 28, 2005. ES048295U