Remote Sensin
OF V E H I C L E E X H A U S s of December 31, 1 9 9 2 , there were 189,674,000 motor vehicles registered in the United States: 75.8% cars, 23.9% trucks, and the remainder buses and motorcycles. The total vehicle miles traveled (VMT) that year was 2.24 trillion. Currently, the vehicle fleet has a wide age distribution, with a mean of 8.1 years for passenger cars and 8.4 years for light-duty trucks; 18.9% of the light-duty trucks are 15 or more years old ( 1 ) . There is also a wide diversity of vehicle makes and models. In the 1992 model year alone, 28 manufacturers sold 333 models of light-duty vehicles (2). The challenge is to determine the emissions from this widely divergent in-use vehicle fleet and to develop effective control strategies to reduce emissions. We will briefly discuss vehicle emissions and the vehicle emissions inventory before reviewing remote-sensing technology for exhaust emissions and remote-sensing applications that help characterize and minimize exhaust emissions in the real world.
A
Emissions and inventories
carbon dioxide (CO,). Federal exhaust emissions standards for HC and CO were implemented in 1968, NO, emission standards were implemented in 1972, a n d the new vehicle corporate average fuel economy standards (which are essentially CO, standards) were implemented in 1978. Passenger car HC, CO, a n d NO, exhaust emissions have been controlled stepwise from their 1960 precontrol rates of 10.6, 84.0, and 4.1 glmi to the 1993 rates of 0.41, 3.4, and 1.0 glmi, respectively. Federal standards of 0.25, 3.4, and 0.4 glmi nonmethane hydrocarbons, CO, and NO, will he phased in from 1994 through 1996. Additional standards apply to diesel particulate matter a n d NO,, cold-start CO, and formaldehyde. There are separate standards for light-duty, medium-duty, and heavy-duty trucks. More stringent standards have been adopted by California and are under consideration by other states. As a result of these regulations, from 1974 to 1993 the new car average fleet fuel economy increased from 14.2 mug .- to 28.3 mpg. Unfortunatelv. air aualitv improvements resulting from the implementation of these standards have not been as great as anticipated. There are several reasons. 2 ,
The primary exhaust emission components of concern are hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NO,), and
STEVEN
H.
CADLE
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ROBERT
First, the average age of the vehicle fleet has been increasing, thereby slowing the introduction of new, cleaner cars. Second, VMT growth of 120% from 1968 to 1992 has offset some of the emissions gains. Third, and most important, the understanding of real-world emissions has been inadequate (31,resulting in emission test procedures and standards that have not adequately addressed the actual source of emissions. To understand this last point, it is important to examine how the vehicle emissions inventory is determined. The exhaust mobile source inventory is basically the product of the emission rates of all in-use vehicles and their respective VMT number. Emission rates are determined from laboratory chassis dynamometer tests, which utilize the urban dynamometer driving schedule (UDDS), a driving cycle designed to represent driving in Los Angeles in 1975. This is a time-consuming and costly test. Other tests determine correction factors for variables such as average speed, ambient temperature, and vehicle load. Emission factors must he generated for the different classes of vehicles as a function of the control technology, odometer reading, and condition of the emission control system.
D.
STEPHENS
General Motors, North American Operations, RbD Center, Warren, MI 48090-9055 258 A Environ. Sci. Technol., Val. 28. No. 6, 1994
001 3-936X/94/0927-258A$O4.50~00 1994 American Chemical Society
Environ. Sci. Technol.. VoI. 28. No. 6, 1994 25s
Problems arise because it is very difficult to obtain and test a representative portion of the in-use fleet and to represent all real-world driving conditions with a single driving cycle. As new cars become cleaner, these problems are exacerbated. For example, a malfunctioning or illegally altered vehicle running at a rich air-to-fuel ratio can have a CO emission rate of 250 glmi or greater. This rate is approximately three times higher than a properly functioning precontrol car and at least 74 times higher than a vehicle meeting the 3.4 glmi standard. It has been suggested that emissions from such vehicles are underrepresented in the current vehicle emissions inventory. Similarly, the failure of the UDDS to capture certain high-emission driving modes w a s not a major problem when vehicles had higher average emission rates. Vehicles have always used enrichment (Le., rich air-to-fuel ratio) to provide extra power under high-load conditions and to protect the engine from overheating. Modern vehicles also employ enrichment to protect the catalyst from overheating. These events are brief a n d infrequent. However, one enrichment event lasting 6 s can result in the same amount of CO emissions as 40 min of driving a warmed-up, properly functioning late-model vehicle. Such events are not captured in the current driving cycle. Some of these problems would be avoided if emissions were measured on-road, rather than in a laboratory. Remote sensing is the first technique t h a t can monitor t h e emissions from thousands of vehicles a day on the roadway. It can be used to improve our understanding of real-world vehicle emissions and as an inspectionlmaintenance (IM) tool. However, there are limitations to the technique that must be considered when designing a remotesensing program.
Remote-sensing methodology Remote-sensing devices (RSDs) for exhaust CO were introduced at the University of Denver (4,5) in 1987 a n d at the General Motors (GM) R&D Center (6) in 1988. Simultaneous determination of HC was added to the sensors in 1990. These RSDs operate on the same general principles. A n infrared beam is c o n t i n u o u s l y d i r e c t e d across a single lane of traffic to a detector. The detector uses band pass filters to isolate the HC, CO, and
Remote sensing in action
CO, absorption regions of the spectrum, as well as a nonabsorbing region that is used to monitor the beam intensity. Multiple detectors continuously monitor the frequency regions so that integrated concentrations over the beam path can be determined simultaneously. The beam is positioned approximately 10 inches above the road surface, the average height of light-duty vehicle tailpipes. As a vehicle passes through t h e b e a m , t h e beam is blocked, and the vehicle’s presence recorded. Data are collected for 0.5-1.0 s after the vehicle leaves the beam. The concentration determined immediately before the vehicle enters the beam is used for background correction of the exhaust plume signal. Longer data collection times provide no benefit, because dispersion of the exhaust plume is very rapid. Chopping the signal at a rate of 200 Hz provides 100-200 i n d e p e n d e n t measurements of the HC, CO, and CO, concentrations during the data collection period. Because the beam passes through only a small portion of the exhaust plume, the measured concentrations cannot be directly converted into emission rates. Instead, the HCI CO, and COlCO, ratios are used to determine the exhaust concentration. These ratios are determined from the slope of the linear regression fit to the data, for example, CO versus CO,. Figure 1 shows the display from the GM instrument. The time scale, 0 to 190, is the number of
260 A Environ. Sci. Technol., VoI. 28, No. 6 , 1994
data points collected at the 200 Hz rate. Acceptance criteria based on the quality of the regression fit, the absolute CO, concentration, and other parameters are used to screen the data. Essentially all of the carbon contained in the combusted gasoline is emitted as HC, CO, and CO,. Thus, by summing the carbon in these species, assuming a n average hydrogen-to-carbon ratio for the fuel, and taking an average gasoline density, both the amount of HC and CO emitted per gallon of gasoline and the exhaust concentration can be calculated. Remote-sensing results generally are reported either as the HCICO, and COICO, ratios or as the percentage CO and HC in exhaust. A COICO, ratio of 0.07 corresponds approximately to a 1% CO exhaust concentration. The conversion factor for the HCICO, ratio is not as straightforward because a significant fraction of the total exhaust carbon can be present as CO. RSD data can also be converted to the more traditional glmi emission rates by estimating the fuel economy. For fleet averages, such estimates can be based on the new vehicle fuel economy figures reported by manufacturers (6).However, there is no way of knowing the fuel economy of the vehicle at the time of an RSD measurement. Thus, glmi emission rates for individual vehicles are highly uncertain. Conversion factors for 1 % CO and 0.1% HC at 20 mpg are 17.4 glmi and 2.73 glmi (as propane), respectively.
I
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3GURE 1
General Motors sensor computer screen display for emissions from one vehicle a
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The University of Denver has added NO remote sensing to their instrument by attaching a collinear UV source and detection system. Because 95% or more of the NO, emitted by vehicles is NO, this is effectively a NO, i n s t r u m e n t . Unisearch Associates, Inc. (Concord, ON, Canada) is investigating the possibility of remotely sensing NO and CO, with a tunable diode laser system. These systems remain under research and will not he discussed further here. In most remote-sensing studies, information on individual vehicles and the fleet composition is required. Thus, remote-sensing systems have incorporated a video camera that is focused on the license plates of the passing vehicles. A frame grabber captures the image, which is then integrated with the remote-sensor output. The license plate numbers are used with state vehicle registration data to obtain the vehicle’s age, make, and model. Vehicle identification numbers, which can also he obtained from the state registration database, provide more detail about the vehicles. To date, the license plate numbers have been read manually from the video records. This laborious process will be eliminated when a reli-
able automated license plate reader becomes available.
Accuracy and validation The accuracy and lower limit of detection of individual measurements are a function of how the exhaust plume disperses and what part of the plume is intersected by the RSD beam. Thus, it is difficult to give an absolute accuracy for RSDs. Overall accuracies have been reported by the University of Denver as 5% and 15% for CO and HC, respectively, and by GM as 15% for COICO, and HCICO, ratios. RSDs were developed primarily to detect high-emission vehicles, and they clearly have the sensitivity and accuracy for that task. They cannot, however, discriminate hetween vehicles that meet the current emission control standard and those that slightly exceed it. Improving the lower limits of detection and accuracy would not necessarily improve the discrimination between failing and passing vehicles because other factors such as emissions Variability also contribute to the uncertainty. It would, however, improve fleet average emission measurements. Emission variability is discussed in more detail later.
Several studies have been conducted in which a vehicle with onboard exhaust emissions measurement capability has been driven repeatedly past an RSD. The vehicle was equipped with instrumentation to change its air-to-fuel ratio and hence its emissions rates. Excellent agreements were obtained between the simultaneous remote-sensing and instrumented vehicle exhaust measurements, proving that remote sensors can accurately measure both CO and HC in the exhaust plume of a passing vehicle. It is not widely recognized, however, that the instrumented vehicle studies have utilized nondispersive infrared (NDIR) HC analyzers for the onboard HC measurement. The NDlR principle of operation is similar to that of the remote sensors. Both the on-board instruments and the RSDs were calibrated with propane, the standard calibration gas for vehicle emissions test facilities. Unlike the standard flame ionization detector (F1D)-basedanalyzers used for lahoratory HC measurement, which are essentially carbon counters, NDIR instruments do not respond equally to the individual hydrocarbons in exhaust. In fact, tests using one fuel showed that the total HC response of the on-board analyzer was 3.5
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times lower than that of a FID ( 7 ) . This factor varies because both the gasoline composition and the vehicle operating condition affect the exhaust speciation profile. This correction factor has not been utilized i n remote-sensing s t u d i e s conducted to date.
Cold start and emissions variability Figure 2 shows the exhaust GO concentration versus time trace for two vehicles driven on a chassis dynamometer over the UDDS. The traces show a properly functioning, late-model car with a GO emission rate of 2.8 g/mi and a vehicle with a malfunctioning emission control system that emits GO at a rate of 151 g/mi. The test procedure dictates that the vehicle be started while cold [i.e., after sitting at room temperature for at least 12 hl. For the properly functioning vehicle, the emissions during the first few minutes are variable and generally high, a result of the rich air-to-fuel ratios necessary during starting and hecause the catalyst system does not function until it is hot. Emissions are also elevated for a
short time when the vehicle is restarted at approximately 1900 s, after a 10 min soak [engine of0 period. These are referred to as "hotstart" emissions. Average emissions are much lower during the rest of the test. Clearly, if this vehicle's emissions are measured by an RSD while in either the cold- or hot-start mode, chances are increased that it will be misidentified as a high-emission vehicle. In addition, there are occasional spikes of higher CO concentration lasting a few seconds. For some vehicles, the peak CO concentrations are high enough that they would be identified as a high-emission vehicle by a RSD. Such a false identification is referred to as a false positive. HC emissions follow a similar pattern. NO, emissions, on the other hand, are not dominated by the cold start and exhibit greater variability than GO and HC. This will make the interpretation of data from a remote-sensing NO channel more difficult. Emissions from the malfunctioning vehicle illustrated in Figure 2 were highly variable. Clearly, this vehicle would he identified as a
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high emitter a large fraction of the time. However, depending on the defined high-emitter concentration cutoff point, it would also be identified as a low emitter for some passes by a single RSD. These false passes constitute a false negative. It has been recommended that multiple remote sensors spaced approximately 100 m apart along a roadway be used to minimize false negatives and positives. False positives also would be minimized by restricting remote-sensing measurements to driving modes that do not cause emission spikes. Studies conducted to date show that remote sensing discriminates well between very high emission vehicles and properly functioning late-model vehicles: that is, error rates for both are low. However, errors increase rapidly if one tries to distinguish vehicles with slightly elevated emissions from properly functioning vehicles. These false positives and negatives are important only when sorting individual vehicles i n t o emission classes. These errors do not affect the accuracy of fleet average emission calculations.
Off-cycle emissions Emissions under driving conditions not represented by the UDDS are referred to as “off-cycle.” Two emission modes are of particular interest for remote-sensing measurements. First, vehicles under high load conditions such as a wideopen throttle acceleration, trailer pulling, or hill climbing are designed to operate at rich air-to-fuel ratios. CO emissions during commanded enrichment are very high. Thus, if a vehicle’s emissions are measured by an RSD under these conditions, it will likely be identified as a high-emission vehicle. Second, very rapid throttle closure can result in a flash of gasoline into the engine. The resulting low air-to-fuel ratio a n d possible misfire may cause high emissions. This condition could lead to false positives. Site selection Proper site selection is the single most important consideration in planning a remote-sensing study. The site should have a single lane of traffic with sufficient shoulder room for the source, detector, video camera, and data acquisition equipment. Current remote sensors use a van or trailer for the data acquisition instrumentation, supplies, and the operator. The unit can operate across two lanes if the traffic volume is light enough to minimize the chance of multiple vehicles simultaneously driving past the RSD. Optical path lengths greater than 13 m decrease instrument sensitivity because of increased background CO,. Sites are frequently selected to provide the maximum number of vehicle observations. Depending on the manufacturer, the auxiliary equipment, and the software version, RSDs can measure one vehicle every 1-1.5 s. Research shows that there is a small positive interference for a vehicle trailing within 4 s of a high-emission vehicle (6, 8). This effect will have a minimal impact when identifying high emitters, but could have a significant impact on fleet average emissions measurements. Traffic densities of 6000 vehicles per eight-hour period can readily be accommodated, but vehicle intervals will frequently be less than 4 s. Daylight operation is preferred for capturing video images of license plates, Consideration must also be given to the impact of vehicles started while cold and off-cycle emissions. If t h e goal is to identify highemission vehicles, the site should
be several minutes away from any significant source of cold-starting vehicles. Freeway off-ramps are ideal for this purpose. High-load, high-emission off-cycle events caused by rapid acceleration should also be avoided. Worst locations include steep hills and metered freeway on-ramps. Light rain, snow, and fog should not drastically affect RSD operation because particles of this size are not expected to greatly affect the transmission of infrared light. Despite this, the spray generated from tires on a wet road does appear to interfere w i t h s o m e m e a s u r e m e n t s . Thus, remote sensing should be done under dry roadway conditions. Utilization RSDs can be used in a number of applications. First, they can characterize the in-use emissions of the vehicle fleet. A number of these programs have been run in the United States and elsewhere. The overall database is best for CO, because the original remote sensors d i d not measure HC. The HC emissions database is less robust, and there is essentially no data on NO, emissions. Although these studies have usually focused on the entire in-use fleet, there are opportunities to examine emissions from vehicles with high annual VMT such as taxis and light- and medium-duty delivery trucks. RSDs can also be used to characterize emissions at locations such as intersections, hills, a n d freeway on-ramps that might be emission hot spots because of the vehicle operating mode. This data would help in the development of an emission model based on modal emissions rather than average emission rates. Second, remote sensing can be used as an IM tool. The aforementioned remote-sensing studies show that less than 10% of the in-use vehicles contribute 50% or more of the warm-running CO and HC emissions. It has been suggested that remote sensors be used to screen the in-use fleet for the high-emission vehicles, which would then be subject to further test and repair. This would lower IM costs because only those vehicles with major emissions problems would be repaired, and properly functioning vehicles wouldn’t be tested at IM stations. Remote screening would have the additional advantage of being performed without warning, thereby identifying vehicles that may be
tampered with before and after inspection and whose drivers avoid inspections altogether. Others argue that all vehicles whose emissions are significantly above the standards should be captured and repaired, and that remote-sensing false negatives and positives are too frequent for use as a stand-alone IM method. An intermediate approach that has been suggested is to use multiple remote sensors as a very rapid screening tool in a drive-through lane at a central test facility. The debate remains unresolved. Lawson 19)has suggested that a rigorous test of IM effectiveness be conducted for a variety of IM programs to resolve this issue. In the short term, it is likely that states will use RSDs to meet EPA’s requirement that enhanced IM programs conduct supplemental emission measurements on at least 0.5% of the vehicles subject to IM testing each year (10). Third, remote sensing also can be used specifically as an anti-tampering enforcement tool. The EPA 1990 Motor Vehicle Tampering Survey showed that the average tampering rate in the United States is 17%. Owners of illegally altered vehicles are subject to fines, although such fines are rarely levied. Remote sensing could be used to identify vehicles for roadside tampering checks, and drivers or owners of altered vehicles could receive warning notices or tickets. Remote-sensing studies conducted in California were very successful when used to stop vehicles identified as high emitters. Finally, RSDs should be considered for long-term monitoring programs. Permanent sites could be selected in urban areas for indefinite, long-term monitoring to determine progress in reducing in-use emissions. Monitoring need not be continuous, because a great deal of data can be obtained in a relatively short time. Locating sites inside and out of IM areas would permit the assessment of IM program effectiveness. Remote-sensing system improvements Research a n d development is continuing in several areas with the goal of improving the RSD systems. The biggest advance would be an automated license plate reader; an effective one is expected to be introduced shortly. Packaging of the entire RSD system is also expected to receive continued attention. Current systems are easily spotted by
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who then slo\r doivn. gawk. or even speed up to get past the site. Ultiniatel!~. it is desirable to have an integrated, stand-alone. unohtrusive package that can be secured by the road for unattended operation. Both improved HC sensitivity and NO measurement capability are under current stndy. At this time. the number of high NO emitters is not k n o w n . Because the difference i n concentration between high and l o w NO emitters is not expected to he as great as i t is for CO or HC. discriminating hetween low and high NO emitters may be more diffir:ult. Thus. the utility of an NO channel remains unclear. Finally. research to develop practical methods for remotely nicasuring exhaust gas temperatiire and vehicle acceleration and deceleration rates is continuing. Exhaust temperatiire data could screen for vcliicles started while cold. Acceleration and deceleration rates could identify vehicles operated in driving modes that result in elevated emissions. Commercial exhaust remote-sensing devices are currently available from two sources: Hughes Santa Barbara Research Center (Goleta. CAI markets the KES-100, which was recently described ( ? ? I , and Remote Sensing Technology (Columbia, MD) sells the RSD-1000.
Summary Remote s e n s i n g is t h e o n l y method available that can measure the HC and CO exhaust emission rates of large nnmhers of individual in-use vehicles. These measurements have helped focus attention on the problem of in-use emissions from malfunctioning and illegally altered vehicles. The method has a bright future as a n aid in understanding in-use emissions, a monitor of progrcss i n reducing fleet emission rates, and as an IM and enforcement tool.
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