28

EMFAC7C predicted

Measured

EMFAC7C predicted

FIGURE 16.29 Ratio of VOC to NOt measured in the 1987 Southern California Air Quality Study (SCAQS) in a tunnel compared to predictions using the California mobile source model, EMFAC7C (see Pierson et al., 1990; and Ingalls et al., 1989).

source emissions (e.g., see Ross et al., 1998). These include (1) inadequate consideration of evaporative emissions that occur while the car is running and the fuel warms up, as well as while the car is parked, either due to residual engine heat or to changes in the external temperature; (2) actual driving habits, e.g., accelerations, decelerations, and idling, being poorly represented by the test cycles used to develop the mobile source emissions models; (3) tampering with emission control devices and engine parameters; (4) overestimated effectiveness of catalysts, especially as they age; and (5) overestimated effectiveness of vehicle inspection and maintenance programs.

There is evidence for contributions from all of these factors. For example, the ambient air data summarized in Fig. 16.30 suggest that emissions of whole gasoline are significant (Harley et al., 1992). The source is not clear but likely includes unburned fuel emitted in the exhaust as well as fuel spillage etc. Similar observations have been made in other locations such as Chicago (Doskey et al., 1992), Atlanta (Conner et al., 1995), Mexico City (Riveros et al., 1995), and Toronto, Canada, where unburned gasoline appeared to account for up to 37% of total gasoline-related organics in the summer (McLaren et al., 1996a). Estimates of the contribution of whole gasoline to exhaust emissions based on tunnel studies cover a wide range. This contribution of whole gasoline to VOC in tunnels has been estimated to be from <5 to 25%, depending on the vehicle exhaust emission profile used to analyze the data (Fraser et al., 1998), and as high as 63% when the data are analyzed using the composition of gasoline as a surrogate for exhaust emissions (McLaren et al., 1996b).

It is also clear that on-road driving habits may not be well represented by the test cycles used to develop the emissions models. For example, Kelly and Grob-licki (1993) point out that lower acceleration rates have been historically used in the test cycles in order to avoid slipping of the belt-driven dynamometers than are encountered in real driving. Figure 16.31 shows the results of on-board tailpipe emissions measurements during a portion of a real driving cycle in which the

Time

FIGURE 16.31 Increased CO emissions due to rapid acceleration uphill (adapted from Kelly and Groblicki, 1993).

Time

FIGURE 16.31 Increased CO emissions due to rapid acceleration uphill (adapted from Kelly and Groblicki, 1993).

driver was accelerating up a hill (Kelly and Groblicki, 1993). At point A, the throttle changed from 20 to 50% open, resulting in a rich air/fuel ratio and increase in the CO emission from <0.f to more than 3.5 g s_l. At point B, the throttle was released slightly, and at point C, the throttle was opened again until point D. During this particular, 29-s period, a total of 165 g of CO was emitted, equivalent to the emissions from driving almost 50 miles at the U.S. federal standard of 3.4 g of CO per mile! Organic emissions are also increased during such accelerations, but not by as large a factor as the CO (Kelly and Groblicki, 1993). For example, over a similar acceleration period, the hydrocarbon emission rate would be equivalent to driving about 17 miles at the U.S. standard of 0.4f g of hydrocarbon per mile (see Table 16.3).

Similar results have been obtained by other researchers. For example, De Vlieger (1997) measured emissions while driving in a small town, in a rural area, and on a highway, respectively, in Belgium. In the urban driving, "aggressive" driving (also noted as "sporty") led to emissions of CO and hydrocarbons that were a factor of 2-3 times those during normal driving while those of NOx were about 50% higher; differences during highway driving were much smaller, ~5-20%. Similarly, Cicero-Fernández et al. (1997) measured emissions from a vehicle during various driving cycles in the Los Angeles area and found significant increases in emissions while traveling up grades. The increases in emissions for each f% increase in the grade were 3.0 g per mile for CO and 0.04 g per mile for hydrocarbons.

Since the first tunnel and roadway studies suggested that organic and CO mobile source emissions were being underestimated, remote-sensing techniques that can be applied to individual automobiles as they travel past a sensor have been developed and applied. The technique has been used not only as cars pass by the sensor on freeway on-ramps, etc. (e.g., see Bishop and Stedman, 1990, 1996; Stephens and Cadle, 1991; Rueff, 1992; Bishop et al., 1992, 1993, 1996, 1997; Lawson et al., 1990; and Cadle and Stephens, 1994), but also in tunnels (Bishop et al., 1994). The device is based on using an infrared beam to measure absorbances due to CO, hydrocarbons, and C02; from the ratios of CO and hydrocarbons to C02, the percentage of these two species in the exhaust and the grams emitted per gallon of fuel used can be derived. TDLS systems have also been developed for such measurements (Nelson et al., 1998). NO emissions have also been measured (Zhang et al., 1996b; Jiménez et al., 1999). Simultaneously, an image of the license plate is recorded so that its participation in mandated emission control programs can be traced. High CO emitters identified by remote sensors have been shown to correlate well with high emitters identified by the traditional test cycle approach (Stephens et al., f996a), although a fraction of automobiles appear to have sufficiently variable emissions that they may escape identification by this method (Bishop et al., 1996). The measurement for hydrocarbons appears not to be as consistent. For example, Stephens et al. (1996b) compared the results of remote sensors to other approaches, including gas chromatography, flame ionization, FTIR, and nondispersive IR. Individual VOC were measured in vehicle exhaust generated using dynamometers. For the exhaust samples, the V0C/C02 ratios measured using remote sensing were smaller than measured at the dynamometer bench, suggesting that remote sensing may not as accurately reflect total organics in vehicle exhaust.

Remote sensor studies as well as tunnel studies using conventional sampling (e.g., Rogak et al., 1998b) have provided substantial evidence for increased emissions due to poor maintenance, tampering with emission control devices, and overestimating the effectiveness of catalysts, other emission control systems, and inspection and maintenance (I/M) programs (e.g., see Tiao et al., 1989; Bishop and Stedman, 1990; Lawson et al., 1990; Lawson, 1993; Zhang et al., 1993, 1995, 1996a; Bishop et al., 1993, 1996; Calvert et al., 1993; Sjodin, 1994; Stephens, 1994; Harrington and McConnell, 1994; Beaton et al., 1995; Gabele, 1995; Pierson, 1996; and Stedman et al., 1997, 1998).

Remote sensor studies have also been effective in identifying the problems of "superemitters," that is, cars that have extremely high emissions. It has now been shown in a variety of studies that a small fraction of automobiles account for a large fraction of the total mobile source emissions of both CO and hydrocarbons. For example, during studies in Utah and in California, approximately 10% of the vehicles were found to be responsible for 50% of the CO emissions as well as 50% of the on-road hydrocarbon emissions; some (~5%) were super-emitters for both pollutants (Bishop et al., 1993; Stephens, 1994; Beaton et al., 1995). Similarly, remote-sensing studies in Mexico City showed that 12% of the fleet was responsible for half of the hydrocarbon emissions and 24% of the fleet produced half of the CO emissions (Beaton et al., f992).

Figure 16.32, for example, shows the average hydrocarbon exhaust emissions measured in four cities worldwide as a function of model year and divided into five groups by percentages, i.e., quintiles (Zhang et al., 1995). Note that the scales are quite different, with the vehicles in Leicester emitting far higher concentrations than those in other cities. Zhang et al. (1995) propose that, given the similarity in vehicles and fuels in the two European fleets, the lower emissions in Gothenburg are due to better maintenance. However, in all cases, it is seen that the automobiles with the highest 20%

4 5 6 7 8 9 1011 12 1314 15 Vehicle age (year)
2 3 4 5 6 7 8 9 1011 12 1314 15 Vehicle age (year)
1 2 3 4 5 6 7 8 9 10 11 12 1314 15 Vehicle age (year)

1 2 3 4 5 6 7 8 9 1011 12 1314 15 Vehicle age (year)

FIGURE 16.32 Average percentage of hydrocarbon in exhaust emissions by model year and grouped into five groups from lowest emissions (front) to highest (back) in four cities. Note the different scales (adapted from Zhang et al, 1995).

1 2 3 4 5 6 7 8 9 1011 12 1314 15 Vehicle age (year)

FIGURE 16.32 Average percentage of hydrocarbon in exhaust emissions by model year and grouped into five groups from lowest emissions (front) to highest (back) in four cities. Note the different scales (adapted from Zhang et al, 1995).

hydrocarbon emissions are emitting hydrocarbons at concentrations that are a factor of two or more, on average, than the next quintile. Similar distributions were measured for CO emissions, in addition, it is not exclusively the older vehicles that fall into this superemitter class; even some relatively new automobiles had much higher emissions than expected, indeed higher than many of the well-performing older vehicles. However, the fraction of cars that are super-emitters declines for newer vehicles (Stephens, 1994).

Similar conclusions have been reached with respect to NO emissions; in an initial application of this remote-sensing technology, the highest 10% of automobile emitters were responsible for almost half of the NO emissions (Zhang et al., 1996b; Jiménez et al., 1999).

Because of large variations in emissions from car to car and from one set of driving habits to another, it has been suggested that a mobile source emissions inventory based on total fuel consumption rather than on predetermined test cycles may be more appropriate in the future (e.g., see Singer and Harley, 1996). This is supported by tunnel studies in which emissions of or-ganics in grams per gallon of fuel used were relatively independent of whether the vehicle was going uphill or downhill, despite the fact that emissions on a grams per mile basis were much greater for the uphill vehicles (as was the fuel consumed) (Sagebiel et al., 1996).

However, while these unanticipated deviations from control programs are clearly important in devising future control strategies, it is important to recognize the tremendous achievements that have occurred in mobile source emission controls over the past two decades. Table 16.3 shows the history of light-duty motor vehicle emission standards for NOx, CO, and hydrocarbons in the United States and the more stringent California standards. Figure 16.33 shows the distribution of exhaust emissions of CO and hydrocarbons measured using a remote-sensing technique for three different model years (Stephens, 1994). Clearly, a dramatic decline has occurred in these emissions as the emissions standards have been made more stringent. A similar

TABLE 16.3 Light-Duty Motor Vehicle Emission Standards in the United States and California"

Federal California

TABLE 16.3 Light-Duty Motor Vehicle Emission Standards in the United States and California"

Federal California

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