Correlation between automotive CO, HC, NO, and PM ...

Transportation Research Part D 9 (2004) 477–496 www.elsevier.com/locate/trd

Correlation between automotive CO, HC, NO, and PM emission factors from on-road remote sensing: implications for inspection and maintenance programs Claudio Mazzoleni *, Hans Moosmu¨ller, Hampden D. Kuhns, Robert E. Keislar, Peter W. Barber, Djordje Nikolic, Nicholas J. Nussbaum, John G. Watson Desert Research Institute, University and Community College System of Nevada, 2215 Raggio Parkway, Reno, NV 89512, United States

Abstract Carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO) emission factors (EFs) are measured with a commercial vehicle emissions remote sensing system (VERSS) during a large-scale vehicle exhaust emissions study in Las Vegas. Particulate matter (PM) EFs are simultaneously measured for individual vehicles with a newly developed PM-VERSS based on ultraviolet backscatter light detection and ranging (Lidar). The effectiveness of CO and HC EFs as proxy for NO and PM EFs for spark-ignition vehicles is evaluated. Poor correlations were found between EFs for pollutants on an individual vehicle basis indicating that high EFs for one or more pollutants cannot be used as a predictor of high EFs for other pollutants. Stronger functional relationships became evident after averaging the EF data in bins based on rank-order of a single pollutant EF. Low overlap between the highest 10% emitters for CO, HC, NO, and PM was found. These results imply that for an effective reduction of the four pollutants, inspection and maintenance (I/M) programs, including clean screening, should measure all four pollutants individually. Fleet average CO and HC concentrations determined by gaseous VERSS were compared with fleet average CO and HC concentrations measured at low-idle and at high-idle during local I/M tests for spark-ignition vehicles. The fleet average CO concentrations measured by I/M tests at either idle were about half of those measured by remote sensing. The fleet average high-idle HC concentration measured by I/M tests was about half of that measured by VERSS while low-idle I/M and VERSS HC average concentrations were in better agreement. For a typical vehicle trip, most of the fuel is burned during non-idle conditions.

*

Corresponding author. Tel.: +1 775 674 7021. E-mail address: [email protected] (C. Mazzoleni).

1361-9209/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2004.08.006

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C. Mazzoleni et al. / Transportation Research Part D 9 (2004) 477–496

I/M measurements collected during idling conditions may not be a good indicator of a vehicles potential to be a high emitter. VERSS measurements, when the vehicle is under a load, should more effectively identify high emitting vehicles that have a large contribution to the mobile emissions inventory. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: On-road sensing; Maintenance inspections; Automobile emissions; Atmospheric pollution

1. Introduction Motor vehicles are a major source of emissions contributing to air pollution on local to global scales. New vehicle emission standards have become more stringent, regulating carbon monoxide (CO), nitrogen oxides (NO), hydrocarbon (HC), and particulate matter (PM) emissions. In nonattainment areas and other areas with air quality concerns, vehicle emissions inspection and maintenance (I/M) programs, including clean screening, are being used to ensure that emission standards are attained over a vehicles lifetime. Due to technologic and economic constraints, I/M programs test only for emissions of a subset of pollutants (typically CO and HC) under a limited number of operating conditions (often just idle). Only a few enhanced I/M programs include NO measurements on dynamometers. In the US, I/M programs are regulated and overseen by the US Environmental Protection Agency and implemented by state agencies. This has led to different tests being used in different states or counties. Major differences between individual I/M programs occur with respect to I/M network type, engine load and speed during testing, mass measurements (mass of pollutant emitted per distance traveled or mass of fuel consumed) vs. concentration measurements (volume concentration of the pollutant in the exhaust), pollutants measured, visual inspection, evaporative tests, pass/fail emissions cut-points, and waiver limits (National Research Council, 2001). The choice of a particular I/M procedure by the local authorities is influenced by the cost and duration of a single test and by the extent of the air quality deterioration in the area. Specifically, the State of Nevada uses an annual steady-state two-speed (low-idle 800 revolutions per minute (rpm) and high-idle 2500 rpm) concentration test, using BAR90 (1990 California Bureau of Automotive Repairs specifications) emissions testing equipment. I/M tests are required for gasoline passenger cars and trucks, and diesel vehicles with gross vehicle weight under 8500 lb (3863 kg) if registered in the urban areas of Clark county or Washoe county, and with model year of 1968 or later. New vehicles are exempt on their first and second annual registrations, as are motorcycles, mopeds, and alternative fuel vehicles. On-board diagnostics (OBD) testing is used in Nevada for 1996 and newer vehicles. OBD systems electronically monitor the performance of devices related to emission control installed in each vehicle, such as catalyst efficiency, oxygen sensor response, evaporative leaks, fuel injection system etc. OBD systems do not measure the emissions, but warn in case of system malfunctions. OBD I/M testing probes the on-board computer for such system malfunctions (National Research Council, 2001). Presently, only CO2, O2, CO, and HC concentrations are measured in Nevadas I/ M tests, which is typical of programs in other states across the US, as the focus is mostly on CO and HC emissions from gasoline engines. The lack of widespread NO and PM emission testing in the US is of particular concern in view of ozone and PM non-attainment in many areas. In addi-


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tion, I/M programs test vehicles only on an annual schedule at best. The potential for privately owned emissions testing businesses to tamper with test procedures and results is of concern with I/M. In general, I/M programs have not achieved the reductions in emissions that have been predicted (Beaton et al., 1995; Holmes and Cicerone, 2002; Lawson, 1993; Zhang et al., 1996). In this study, both remotely sensed and I/M data are examined to address the extent that NO and PM emission factors (EFs) are related to CO and HC EFs and the likelihood that CO and HC tests identify NO and PM high emitters. A large on-road remote sensing dataset from the Las Vegas metropolitan area containing fuelbased CO, NO, HC, and PM EFs is analyzed and EFs for different pollutants are compared. Additionally, CO and HC vehicle emissions remote sensing system (VERSS) fleet averaged concentrations are compared with the Las Vegas I/M database. This work is limited to gasoline-powered vehicles that are subject to CO and HC I/M tests. For diesel vehicles the most common test performed in I/M programs is the measurement of exhaust opacity, for an estimation of PM emissions. The relationships between infrared and ultraviolet opacity and PM mass emitted is discussed elsewhere (Mazzoleni et al., 2004; Moosmu¨ller et al., 2003). Other related questions such as the effectiveness of CO and HC reductions due to the introduction of new or improved I/M programs in non-attainment areas (Beaton et al., 1995; Holmes and Cicerone, 2002; Lawson, 1993, 1995; Stedman et al., 1997; Zhang et al., 1996), and correlations of VERSS results with I/M and federal test procedure (FTP) readings on an individual vehicle basis, and on model year averages have been addressed previously (Bishop et al., 1996; Pokharel et al., 2002; Stephens et al., 1994).

2. Methodology 2.1. Field measurements Field measurements were conducted over 20 days between 4 April 2000 and 16 May 2002 primarily during spring and summer months in the Las Vegas metropolitan area. Gaseous and PM EFs were measured on-road by VERSS, generally on freeway on-ramps. PM EFs were measured only during 2001 and 2002. The sampling locations were selected to have a wide spatial coverage of registration addresses across the Las Vegas metropolitan area (Mazzoleni et al., 2004). Site selection criteria and instrumentation setup have been described elsewhere (Moosmu¨ller et al., 2003). A commercial gaseous VERSS (ESPi RSD3000) was used to measure fuel-based EFs and concentrations for CO, HC, and NO and a PM VERSS based on path-integrated particle backscatter and extinction measured fuel-based PM EFs. The RSD3000 VERSS measured the column contents of CO2, CO, and HC by infrared absorption and NO by ultraviolet absorption. The light source projected collinear infrared and ultraviolet beams across a single traffic lane. Reflectors placed on the opposite side of the road redirected the beams to the detector, which was mounted directly below the light source. After a vehicle passed through the beams, the gases emitted by the vehicle partially absorbed the infrared and ultraviolet beams at characteristic wavelengths. Band-pass filters limited the detected radiation to specific wavelengths. The attenuation of the IR and UV radiation by exhaust gases was used

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to quantify the mass column content of each individual gas (Guenther et al., 1995). The signal measured immediately before the vehicle transited through the sensing beams was used as a background measurement. The RSD3000 processed 20 measurements before each vehicle passage and 50 measurements immediately after, for a background and emission measurement time of 0.2 and 0.5 s for each vehicle, respectively. A reference infrared beam was used to compensate for wavelength independent signal reduction and to estimate an infrared opacity for PM high emitters. The slope of a linear regression of the instantaneous column content for each pollutant vs. the instantaneous total carbon column content yielded a fuel-based pollutant EF (in mass of pollutant emitted per mass of fuel consumed) when the fuel carbon fraction was known. Alternatively, the EF could also be expressed as volume pollutant concentration per volume of total dry exhaust. Most gasoline fuels contain 85–87% carbon by weight. A good approximation of the exhaust total carbon column content can be calculated using the CO2, CO, and HC column content measurements (Moosmu¨ller et al., 2003). For most four stroke engines, carbon content is dominated (>90%) by CO2 emissions. Vehicle speed and acceleration were determined with two diode lasers on one side and two detectors on the opposite side of the lane. Front and back tires of each vehicle blocked the diode laser beams in sequence. The blocking–unblocking timing and the separation distance of the laser beams were used to calculate vehicle speed and acceleration. The vehicle specific power (VSP), measured in units of kW/Mg and defined as the power per vehicle mass required by the engine to maintain the measured acceleration, was determined for each vehicle from the speed and the acceleration data and from the lane slope. The VSP is an estimate of road load and a surrogate for engine load. The VSP permitted classification of on-road tests into load categories (Kuhns et al., 2004). A video camera acquired an image of the rear license plate of each vehicle. The license plate number was associated with entries in the Nevada Department of Motor Vehicle (DMV) database to obtain gross vehicle weight, model year, make, and fuel type. PM mass column content was measured with an ultraviolet (UV) backscattering Lidar (light detection and ranging) system using a pulsed Nd:YAG laser as a radiation source. The laser was frequency quadrupled to a wavelength of 266 nm. A laser pulse, with a pulse width of 1 ns and peak power of 200 W, was transmitted across the lane approximately every 150 ls. A 5-cm diameter telescope collected the light backscattered by the exhaust particles along the laser beam path at 180° and focused it onto a fast photomultiplier tube (PMT). The PMT generated a current proportional to the incident optical power. A high bandwidth (1.5 GHz) digital oscilloscope measured this current yielding a time-dependent waveform for each laser pulse. This waveform was converted to a distance dependent waveform by multiplication of the time with one half the speed of light to account for the roundtrip of the laser pulse across the road. The resultant Lidar backscatter signal was a function of distance or range. A computer-based data acquisition system collected 40 waveforms before and 100 waveforms just after the vehicle passage for a total collection time of 0.2 and 0.5 s, respectively. The backscattering waveform was range corrected and integrated (after proper calibration) over a range that included the likely location of the tailpipe plume. The resulting waveform, after subtraction of the average pre-vehicle background, was converted to a PM mass column content with the help of backscattering mass efficiency constants derived from theoretical assumptions and particle models. The mass efficiency constant was calculated using published measurements of PM bulk density, composition, and size distribution. For spark-ignition vehicles, PM optical


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properties were modeled assuming homogeneous, spherical particles with a real index of refraction of 1.5 and negligible absorption at 266 nm. The particle diameters were assumed to be lognormally distributed with mass median diameter of 0.15 lm and geometric standard deviation of 1.5. The bulk density of each individual particle was assumed to be equal to 1.25 g cm 3. From these assumptions a backscattering mass efficiency of 0.16 ± 0.05 m2/(g sr) was calculated. The error was estimated assuming reasonable uncertainties for the parameters used in the mass efficiency calculation (Barber et al., in press). By using the total carbon mass column content determined by the gaseous VERSS, the fuel-based PM EF (i.e., mass of PM emitted per mass of fuel consumed) was calculated from the PM mass column content. Because the PM EFs are directly proportional to the mass efficiency constant, the strength of the correlation between PM EFs and CO and HC EFs should not be affected by possible biases in the particle model. Therefore, the main conclusions of this paper regarding PM emissions are independent of the physical model chosen to represent the PM emitted by vehicles. The Clark County I/M database contains results from more than 2 million I/M tests conducted in Las Vegas between September 1998 and December 2001. The I/M database includes vehicle data (vehicle identification number, license plate, model year, make etc.), low-idle and high-idle CO and HC emission concentration data in % volume CO and ppmv HC, measured low-idle and high-idle engine speeds and pass-fail status for each test.

3. Results 3.1. Summary of field results and DMV database Table 1 reports the number of valid measurements of pollutants emitted by gasoline vehicles during the entire study duration. The term ‘‘valid’’ refers to a measurement with measurable CO2 and measurable pollutant column contents, for a vehicle with readable license plate that is registered in Nevada. During the entire study 41,569, 40,994, 39,531, and 14,815 valid EFs, from gasoline vehicles, were obtained for CO, HC, NO, and PM emissions, respectively. The VERSS detection limit for each pollutant, as calculated from a statistical analysis of the measurement noise distributions (Mazzoleni et al., 2004), is reported in the bottom row of Table 1. For the comparison presented in this paper, we used vehicles that appear both in the Clark County DMV I/M database and in the remote sensing database with a time difference of less than

Table 1 Number of valid gasoline vehicles measurements for the different pollutants during the three-year study Number of matched license plates with valid CO2 2000 22,152 2001 10,188 2002 9561 Detection limits in gPollutant/kgFuela a

CO

HC

NO

PM

22,130 10,030 9409 5

21,831 9914 9249 1

21,155 9660 8716 1

Not measured 6047 8768 0.3

Detection limit as one standard deviation of noise distribution.

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one year between the VERSS measurement and the closest I/M test. This was done to compare similar fleets so that findings derived from VERSS measurements could be used to evaluate the effectiveness of the Las Vegas I/M test for PM and NO emission reductions. The VERSS measurements main characteristics and limitations were: (1) each measurement represents only a snapshot (0.5 s) of each vehicles emissions while it is well known that emissions may be highly variable in time; (2) the emissions were measured on-road and only for a limited set of operating conditions; (3) during the study most engines were operating in hot-stabilized mode as a result of site characteristics; (4) evaporative HC emissions were not measured by VERSS; and (5) emissions were quantified exclusively by using optical techniques. The primary I/M test characteristics and limitations were: (1) emissions were measured only in a controlled situation and at steady-state idling, no engine load was applied; (2) cold start emissions were not measured; (3) only CO and HC emissions were measured; and (4) some vehicles may have been serviced directly before or tampered directly after the I/M test. 3.2. Frequency distributions Frequency distributions of gasoline engine CO and HC emission concentrations derived from on-road VERSS data are reported in Fig. 1 and compared with I/M test results. Each pollutant was sorted by increasing concentration and the vehicles are divided into 10 groups with the same number of vehicles in each group (i.e., deciles). Average concentrations and standard errors (i.e., standard deviations divided by the square root of the number of samples) were calculated for each group. For comparison, the decile distributions for CO and HC emission concentrations were independently calculated and plotted for VERSS measurements, low-idle and high-idle I/M tests. VERSS, low-idle and high-idle distributions were similar for CO concentrations; differences were less than the standard errors. For HC concentrations, VERSS and low-idle measurements yielded similar distributions, but the high-idle measurements are more skewed toward high concentrations; the 10th decile (i.e., highest 10% fraction) was 10% higher for high-idle than for VERSS and low-idle, while the lower HC concentrations occurred less frequently. Possible expla-

(a)

100

90

I/M Low-idle

80

I/M High-idle

70

VERSS

Percent of fleet concentration

Percent of fleet concentration

100

60 50 40 30 20 10

90

I/M Low-idle

80

I/M High-idle

70

VERSS

60 50 40 30 20 10

0

0

-10

-10

1

2

3

4 5 6 7 8 CO concentration decile

9

10

(b)

1

2

3

4 5 6 7 8 HC concentration decile

Fig. 1. CO and HC deciles distributions for VERSS compared to I/M test. (a) CO; (b) HC.

9

10


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nations for the difference between I/M high-idle HC and VERSS and low-idle HC are: (1) lower emitters generally have a well functioning emission control system; when the engine is running at higher speed and in absence of ventilation both combustion temperature and catalyst temperature are higher, resulting in improved combustion and exhaust oxidation efficiencies compared to the lower engine speed tests. (2) HC gross polluters emit a large amount of HC due to emission control system malfunction, low combustion efficiency (or misfiring), and HC volatilization from engine oil entering the combustion chamber. For these vehicles, higher engine speed in absence of load will result in higher HC concentrations in the exhaust compared to lower engine speed concentrations. Therefore, higher emitters would contribute more to the fleet concentrations, while lower emitters would contribute less, causing the high-idle deciles distribution to have higher skewness toward high concentrations compared to the low-idle deciles emission concentrations distribution. During VERSS measurements, engines are under load and additionally the vehicle speed contributes to the cooling of engine and of the catalytic converter, therefore the above effects have less influence on VERSS emissions. In Table 2, VERSS CO and HC average concentrations are compared with I/M averages for the measured fleet. The uncertainties represent the 95% level confidence limit calculated by bootstrap with resampling technique (Wilks, 1995; Mazzoleni et al., 2004) and approximately correspond to two times the standard errors. The skewness of the distributions for HC and especially for CO was evident in all cases by the contribution of the highest 10% emitters to the fleet average emission concentrations. The skewness was also evident from the difference between the mean and the median of the concentrations normalized by the mean. Symmetric distributions would have identical mean and median and therefore the normalized difference would be zero, while values close to one are caused by highly skewed distributions. In this case, a small fraction of vehicles contributed a large fraction of the total fleet emissions. These results were consistent with previous reports covering other locations and times (Bishop and Stedman, 1990; Pokharel et al., 2002; Stephens et al., 1997; Zhang et al., 1994). However, for both VERSS and I/M results, vehicle emissions were predominantly measured in hot-stabilized mode and emissions during hard acceleration were rarely included in the distribution. These factors may contribute to higher fleet emission skewness than during real world vehicle operation. In fact, real world vehicle operation would include cold starts and hard (i.e., ‘‘of cycle’’) accelerations during which even well maintained modern vehicles might have high emissions.

Table 2 VERSS and I/M fleet averages and contribution to emission fleet by highest 10% emitters Test

Average concentration

Difference between mean concentration and median normalized by the mean

Contribution to fleet emission by highest 10% of emitters (%)

CO

VERSS Low-idle High-idle

0.52 ± 0.01% 0.216 ± 0.007% 0.193 ± 0.006%

0.87 ± 0.05 0.86 ± 0.08 0.95 ± 0.09

77 ± 2 74 ± 2 75 ± 2

HC

VERSS Low-idle High-idle

81 ± 1.4 ppmv 65 ± 1.4 ppmv 31 ± 1 ppmv

0.46 ± 0.03 0.58 ± 0.04 0.68 ± 0.07

47 ± 1 48 ± 1 58 ± 2

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The CO average concentration measured by VERSS was approximately twice that measured by the low-idle and high-idle I/M tests. These differences could be due to: (1) instrumental biases, (2) different engine operation conditions during the I/M tests and the VERSS tests, (3) vehicle maintenance prior to I/M testing, (4) vehicle tampering after I/M testing, (5) time variability of the emissions between the VERSS and the I/M tests. The RSD3000 was calibrated at least every two hours and VERSS accuracy has been verified in previous studies and by intercomparison between different VERSSs (Pokharel et al., 2002). Idle conditions were rarely sampled during the on-road tests as vehicles were typically accelerating to merge onto the freeway. As previously shown by Lawson (1993), human behavior affects emission measurements. For example, it is likely that oil change and/or general engine checks/tune-ups are performed by the vehicle owner immediately before the I/M test to increase the probability of passing the test, thereby reducing the average emissions measured by the test. It is also possible that vehicle tampering after passing the I/M test and/or illegal tests using a different vehicle may be performed. The VERSS average HC emission concentration was more than twice the HC high-idle I/M average, while the VERSS average was closer to the HC low-idle I/M average concentration. Lower HC concentrations at 2500 rpm than at 800 rpm may be due to higher combustion temperatures and better combustion efficiency. HC concentrations also decreased with increasing VSP during the on-road measurements. The difference between low-idle and high-idle HC average concentrations further demonstrates the importance of engine operating conditions in emissions measurements. Idle emissions may be not representative of real-driving conditions that occur over a typical vehicle trip. 3.3. Correlations between HC and CO from I/M testing I/M HC concentrations and I/M CO concentrations were poorly correlated on an individual vehicle basis (r2 0.2). HC emissions were not predictable from the CO measurements for an individual vehicle. Even considering only the highest emitters (i.e., vehicles emitting more than 1% CO or 300 ppmv HC), the correlation coefficient r2 was less than 0.2 (see Table 3). An example for individual low-idle I/M data is shown in Fig. 2; included are all vehicles measured through the I/M program within a year from the date of their VERSS measurement (a total of 55,903 data points). A data cluster at 10% CO appears to be a reporting problem in the I/M testing system. The correlation was poor although statistically different from zero at the 95% level confidence

Table 3 Correlations of I/M HC (ppmv) vs. I/M CO (%) concentrations

Low-idle HC vs. CO Low-idle HC vs. CO for CO > 1% Low-idle HC vs. CO for HC > 300 ppmv High-idle HC vs. CO High-idle HC vs. CO for CO > 1% High-idle HC vs. CO for HC > 300 ppmv

Correlation coefficient r2

Slope (ppmv/% = 10 4)

Intercept (ppmv)

Number of data points

0.21 ± 0.03 0.13 ± 0.04 0.02 ± 0.02 0.17 ± 0.03 0.08 ± 0.04 0.004 ± 0.01

100 ± 13 92 ± 25 40 ± 30 74 ± 11 65 ± 20 20 ± 30

44 ± 2 60 ± 60 600 ± 70 18 ± 2 40 ± 40 770 ± 90

55,903 2455 1360 55,903 2298 543


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Fig. 2. Correlation between HC and CO concentrations from the same vehicle and test for low-idle I/M tests in Las Vegas, NV.

limit (the uncertainties of the regression parameters represent the 95% level confidence limit calculated by bootstrap with resampling technique). Averaging the data within discrete concentration intervals improved the correlation as shown in Fig. 3. The HC and CO data from low-idle and high-idle I/M tests were sorted by increasing CO

1000

1000

100

10 0.001

(a)

a

Averaged HC [ppmv]

Averaged HC [ppmv]

a

Fit: [HC] = b[CO] 2 r = 0.96 +/- 0.03 a = 0.41 +/- 0.04 b = 195 +/- 18 Number of points: 33

0.01

0.1

Averaged CO [%]

1

Fit: [HC] = b[CO] 2 r = 0.982 +/- 0.009 a = 0.49 +/- 0.04 b = 115 +/- 11 Number of points: 26 100

10 0.001

10

(b)

0.01

0.1

1

10

Averaged CO [%]

Fig. 3. Averaged HC concentration vs. averaged CO concentration from I/M tests. (a) Low-idle; (b) high-idle.

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concentrations, the series of data is then divided into 50 groups with the same number of data (each point represented an average of 1118 measurements) in each group. Average and standard error were calculated for each group for both CO and HC and plotted on a scatter plot. The 50 resulting data points are not linearly spaced because CO and HC distributions were skewed (see Fig. 1a and b). Because the two pollutant distributions were disjoint from each other and had different skewness, the resulting plot is not expected to show a linear relation between the two pollutants. However, particularly for higher emitters, the emission distributions are approximated by exponential relationships (Mazzoleni et al., 2004; Stephens et al., 1997; Zhang et al., 1994) that appear linear on the logarithmic scales of Fig. 3. Only positive averages were plotted and used in the regression calculation. The results of power fit HC = b Æ COa were reported in the text boxes. The parameters a and b were calculated for CO expressed in % and HC in ppmv. The high and positive correlation coefficients between the logarithm of CO and HC concentrations imply that the average of the high CO emitters also had higher HC average concentrations. However, testing one pollutant concentration does not indicate the concentration level of the other pollutants for individual vehicles as shown in Fig. 2. These results imply that for an effective reduction of all the pollutant emissions, vehicle emission testing programs should measure them individually, possibly by using VERSS. 3.4. VERSS emission factors vs. road load In Fig. 4, VERSS CO and HC concentrations are plotted as a function of VSP and compared with low and high-idle I/M averages. I/M concentrations were measured at no load so they are represented by the circle and the square at 0 VSP. For comparison with the VERSS data, a dotted line (low-idle) and a dashed line (high-idle) are drawn along the entire VSP range. Each VERSS point is an average over 450 measurements grouped by rank-order of VSP. The error bars represent the standard error of the mean. VERSS CO measurements over the whole range of VSPs

180

1.6

VERSS [HC]

VERSS [CO]

1.4

160

HC concentration [ppm]

I/MLow-Idle [CO]

CO concentration [%]

I/MHigh-Idle [CO]

1.2 1.0 0.8 0.6 0.4 0.2

I/MHigh-Idle [HC]

140 120 100 80 60 40 20 0

0.0

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(a)

I/MLow-Idle [HC]

10

20

VSP [kW/Mg]

30

0

40

(b)

10

20

30

40

VSP [kW/Mg]

Fig. 4. VERSS exhaust concentrations as function of positive values of the vehicle specific power (VSP) compared to I/M average concentrations. (a) CO concentrations; (b) HC concentrations.


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were higher than the average from all I/M tests. In particular, CO average VERSS concentration at zero VSP was 0.6% while I/M average concentrations at either idle were 0.2%. The difference between CO concentrations measured by VERSS and I/M at both idles remained approximately constant with a minimum value of 0.3% for VSP between 5 and 20 kW/Mg. VERSS HC measurements were in better agreement with the low-idle I/M test HC concentrations than with those from the high-idle I/M tests, as it appears evident from panel (b) of Fig. 4. However, HC average VERSS concentration at zero VSP was 140 ppmv while I/M average concentration at low-idle was 65 ppmv and at high-idle was 31 ppmv. The differences between VERSS and I/M HC concentrations remained approximately constant with a minimum value of 30 ppmv and 0 ppmv between 10 and 30 kW/Mg at low and high-idle, respectively. These suggest that VSP must be considered when analyzing whether CO and HC testing can be effective to predict NO and PM emissions of individual vehicles in the context of I/M tests. 3.5. Are CO and HC emissions related to NO and PM emissions? If emissions for several pollutants on individual vehicles are consistently and quantitatively related to one another, there is no need to measure each pollutant. Conversely, if the relationships are poor, each pollutant should be measured separately to ensure that the variability of EFs are properly characterized. The VERSS measurements were examined to determine how well CO and HC EFs predict the measured NO and PM EFs. For on-road testing, correlation coefficients and linear regressions were calculated for HC vs. CO, NO vs. CO, NO vs. HC, PM vs. CO and PM vs. HC EFs. Correlation coefficients (r2) for each of pair of pollutant EFs (Tables 4–6) were low even when only measurements in a limited range of road load or only higher emitters were included in the analysis. The correlations had a maximum of 0.26 for NO vs. CO, 0.20 for HC vs. CO, 0.07 for NO vs. HC, 0.02 for PM vs. HC, and 0.01 for PM vs. CO EFs. The uncertainties of the regression parameters represent the 95% level confidence limit calculated by bootstrap with resampling technique. Multivariate correlations (e.g., NO and PM EFs as a functions of both CO and HC EFs) were also considered. This analysis showed negligible improvement in the correlation (i.e., NO EFs vs. CO and HC EFs gave r2 = 0.05, while PM EFs vs. CO and HC EFs gave r2 = 0.015). Such weak correlations are not sufficient to estimate NO and PM EFs from the CO and HC concentrations commonly measured in I/M tests. Previous studies have shown good correlations between PM and CO distance-based EFs (i.e. mass of pollutant emitted per distance traveled) from dynamometer tests for individual heavyduty diesel trucks. However, the regression slope depends on the vehicle tested (Clark et al.,

Table 4 VERSS correlations for HC (gHC/kgFuel) vs. CO (gCO/kgFuel) emission factors

All HC vs. CO HC vs. CO for 10 kW/Mg < VSP < 20 kW/Mg HC vs. CO for CO > 500 gCO/kgFuel HC vs. CO for HC > 13 gHC/kgFuel

Correlation coefficient r2

Slope (gHC/gCO)

Intercept (gHC/kgFuel)


0.20 ± 0.03 0.17 ± 0.03 0.01 ± 0.01 0.006 ± 0.009

0.016 ± 0.001 0.014 ± 0.001 0.009 ± 0.005 0.005 ± 0.003

2.07 ± 0.06 1.84 ± 0.05 5±4 21 ± 1

40,962 18,880 1053 1178

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Table 5 VERSS correlations for NO (gNO/kgFuel) vs. CO (gCO/kgFuel) and vs. HC (gHC/kgFuel) emission factors Correlation coefficient r2 All NO vs. CO NO vs. CO for 10 kW/Mg < VSP < 20 kW/Mg NO vs. CO for CO > 500 gCO/kgFuel NO vs. CO for CO > 500 gCO/kgFuel and HC > 13 gHC/kgFuel All NO vs. HC NO vs. HC for 10 kW/Mg < VSP < 20 kW/Mg NO vs. HC for HC > 13 gHC/kgFuel NO vs. HC for CO > 500 gCO/kgFuel and HC > 13 gHC/kgFuel

Slope (gNO/gCO) or (gNO/gHC)

Intercept (gNO/kgFuel)


0.0007 ± 0.0005 0.0008 ± 0.0005 0.19 ± 0.03 0.26 ± 0.07

0.003 ± 0.001 0.003 ± 0.001 0.012 ± 0.001 0.012 ± 0.002

8.3 ± 0.2 9.1 ± 0.21 14 ± 1 14 ± 2

39,132 18,130 1033 272

0.04 ± 0.01 0.07 ± 0.01 0.04 ± 0.01 0.03 ± 0.02

0.6 ± 0.2 0.9 ± 0.1 0.21 ± 0.04 0.03 ± 0.02

6.7 ± 0.4 7.0 ± 0.3 21 ± 2 5.8 ± 0.7

39,132 18,130 1120 272

Table 6 VERSS correlations for PM (gPM/kgFuel) vs. CO (gCO/kgFuel) and vs. HC (gHC/kgFuel) emission factors Correlation coefficient r2 All PM vs. CO PM vs. CO for 10 kW/Mg < VSP < 20 kW/Mg PM vs. CO for CO > 500 gCO/kgFuel PM vs. CO for CO > 500 gCO/kgFuel and HC > 13 gHC/kgFuel All PM vs. HC PM vs. HC for 10 kW/Mg < VSP < 20 kW/Mg PM vs. HC for HC > 13 gHC/kgFuel PM vs. HC for CO > 500 gCO/kgFuel and HC > 13 gHC/kgFuel

Slope (gPM/gCO) or (gPM/gHC)

0.006 ± 0.009 0.01 ± 0.01 0.003 ± 0.01 0.01 ± 0.03

0.0004 ± 0.0003 0.0005 ± 0.0002 0.0005 ± 0.0005 0.0004 ± 0.0001

0.02 ± 0.01 0.02 ± 0.01 0.001 ± 0.003 0.005 ± 0.01

0.020 ± 0.006 0.020 ± 0.009 0.004 ± 0.005 0.003 ± 0.006

Intercept (gPM/kgFuel)


0.04 ± 0.01 0.02 ± 0.01 0.1 ± 0.4 0.3 ± 0.8

16,094 7243 375 116

0.004 ± 0.02 0.006 ± 0.02 0.7 ± 0.2 0.5 ± 0.2

16,094 7243 423 116

1999). This does not extend to the large number of different vehicles measured by VERSS. Sagebiel et al. (1997) showed poor correlation between PM and CO distance-based EFs, and PM and HC EFs for 23 high emitting vehicles in an on-road study. The same authors also reported better correlation when only non-smoking vehicles were considered. Despite the poor correlation coefficients, the CO, HC, and PM EFs did have a positive correlation with each other. NO EFs were weakly positively correlated with HC and CO EFs, for the entire dataset. NO EFs were negatively correlated with HC and CO EFs for EFs larger than 500 gCO/kgFuel and 13 gHC/kgFuel, respectively; these CO and HC EFs were equivalent to concentrations close to 1% of CO (for small concentration of HC) and of 300 ppmv of HC (for small concentration of CO), respectively (Bishop and Stedman, 1996). Vehicles with EFs higher than these levels represented about 2.5–3% of the measurements (that contributed about 37% of the fleet average CO EF and 23% of the fleet average HC EF factor, respectively). The correlation values were mostly statistically significant (i.e.; r2 > 0), at a 95% confidence level, for HC vs. CO, NO vs. CO, and NO vs. HC EFs, however the correlations were non-significant for PM vs. CO and PM vs. HC EFs by the same criteria.


489

Positive correlations of PM, CO, and HC EFs with each other were expected because excessive emissions are caused by non-ideal combustion conditions. Increased CO and HC emissions can also be due to poor oxidation efficiency of the catalytic converter. For a normal spark-ignition engine, CO EFs increase as the oxygen supply for the combustion process is reduced (i.e., rich burning conditions). For lean fuel/air mixtures, CO EFs are generally lower. HC EFs increase at richer fuel/air conditions, but can also increase slightly for very lean mixtures when combustion efficiency deteriorates. NO is formed by the thermal reaction of nitrogen and oxygen present in the air/fuel mixture and is sensitive to combustion temperature and oxygen availability. Therefore, NO formation peaks at slightly lean fuel/air mixtures and falls off rapidly for leaner mixtures (temperature limited) or richer mixtures (oxygen limited and additionally temperature limited for very rich conditions) (Heywood, 1988). For these reasons one would expect an inverse relationship between NO and CO or HC EFs for higher CO or HC EFs. A positive correlation would be expected for fuel/air ratios on the lean side close to stoichiometric when both HC and CO EFs are lower. Relationships among pollutant EFs were more evident for averages of multiple measurements as is the case for I/M data. Figs. 5–7 compare averaged CO, HC, NO, and PM EFs from VERSS measurements. The data were sorted by increasing independent EF and then divided into 50 groups. Independent (X) and dependent (Y) average EFs and standard errors were calculated for each group. Only positive averages were plotted and used in the regression calculation. The results of power fit Y = b Æ Xa are reported in the text box. The uncertainties of the regression parameters represent the 95% level confidence limit calculated by bootstrap with resampling technique. CO EFs reported in Fig. 5 (819 measurements for each group) were positively correlated with HC EFs and the data followed a power law (r2 = 0.97, power a = 0.36). The parameters a and b were calculated for CO expressed in gCO/kgFuel and HC in gHC/kgFuel. The comparison between averaged NO and CO EFs in Fig. 6a (783 measurements for each group) showed a behavior

100 a

Averaged EFHC [gHC/kgFuel]

Fit: EFHC = b[EFCO] 2

r = 0.97 +/- 0.02 a = 0.36 +/- 0.02 b = 0.97 +/- 0.07 Number of points: 44 10

1 1

10

100

1000

Averaged EFCO [gCO/kgFuel] Fig. 5. Averaged VERSS HC vs. averaged VERSS CO emission factors.

490

C. Mazzoleni et al. / Transportation Research Part D 9 (2004) 477–496 100

100 Fit: EFNO = b[EFCO]a

2

r = 0.9 +/- 0.1 a = -0.5 +/- 0.2 b = 190 +/- 280 Number of points: 6

2

r = 0.97 +/- 0.02 a = 0.29 +/- 0.02 b = 4.6 +/- 0.2 Number of points: 39

10

Averaged EFNO [gNO/kgFuel]

Averaged EFNO [gNO/kgFuel]

Fit: EFNO = b[EFHC]a

Fit: EFNO = b[EFCO]a

1 1

10

(a)

100

r 2 = 0.3 +/- 0.3 a = 0.06 +/- 0.05 b = 5.2 +/- 0.2 Number of points: 14

10

Fit: EFNO = b[EFHC]a r 2 = 0.8 +/- 0.4 a = -0.1 +/- 0.2 b = 24 +/- 10 Number of points: 5

1 0.01

1000

Averaged EFCO [gCO/kgFuel]

Fit: EFNO = b[EFHC]a

r 2 = 0.98 +/- 0.02 a = 0.74 +/- 0.05 b = 4.7 +/- 0.3 Number of points: 28

0.1

1

10

100


(b)

Fig. 6. Average VERSS NO vs. CO and NO vs. HC emission factors. (a) NO vs. CO; (b) NO vs. HC.

1

0.1

0.01

Fit: EFPM = b[EFCO]a r2 = 0.69 +/- 0.14 a = 0.7 +/- 0.1 b = 0.006 +/- 0.003 Number of points: 38

Averaged EFPM [gPM/kgFuel]

Averaged EFPM [gPM/kgFuel]

1

0.001

Fit: EFPM = b[EFHC ]a

0.01

r2 = 0.6 +/- 0.2 a = 1.2 +/- 0.4 b = 0.01 +/- 0.005 Number of points: 42

0.001 0.1

(a)

0.1

1

10

100

Averaged EFCO [gCO/kgFuel]

1000

0.1

(b)

1

10

100


Fig. 7. Average VERSS PM vs. CO and PM vs. HC emission factors. (a) PM vs. CO; (b) PM vs. HC.

similar to that for HC vs. CO EFs, but only for CO EFs lower than 90 gCO/kgFuel. NO and CO EFs correlated positively for CO EFs lower than 90 gCO/kgFuel (r2 = 0.97, power a = 0.29). However for CO EFs above 90 gCO/kgFuel the correlation became negative (r2 = 0.9, power a = 0.5). From Fig. 6b (783 measurements for each group), NO EFs correlated poorly (r2 = 0.3, power a = 0.06) with HC EFs for HC EFs below 1 gHC/kgFuel; NO EFs appeared independent from HC EFs for lower HC EFs. NO and HC EFs correlated well and positively (r2 = 0.98, power a = 0.74), for EFs between 1 gHC/kgFuel and 7 gHC/kgFuel. However, for HC EFs above 7 gHC/kgFuel the correlation became negative (r2 = 0.8, power a = 0.1). The parameters a and b were calculated for CO expressed in gCO/kgFuel, HC in gHC/kgFuel and NO in gNO/kgFuel.


491

The functional relationships between average NO EFs and average CO and HC EFs were in agreement with previous findings. Jimeńez et al. (2000) proposed that lower emitters have functioning emission control systems with different efficiencies for each individual vehicle; reduced efficiency in the emission control systems results in increased HC and CO EFs as well as NO EFs. However, for the highest emitters, the emission control system is malfunctioning or totally inoperative. In these cases, the engine is probably operating at the rich or at the lean side of the optimum air/fuel ratio resulting in low combustion temperature and therefore in lower NO emissions as mentioned above. Fig. 7 (292 measurements for each group) shows relationships of PM to CO and HC EFs. Positive correlations were evident in both plots, as expected. The larger error bars were due to higher measurement uncertainties and variability for the PM EFs. The parameters a and b were calculated for CO expressed in gCO/kgFuel, HC in gHC/kgFuel and PM in gPM/kgFuel. 3.6. The likelihood that individual vehicles are high emitters for single or multiple pollutants The question of the overlap between high polluters in different pollutant categories is addressed in Tables 7 and 8. These tables report the overlap between the groups of emitters belonging to the highest 10% emitters for the different pollutants. Results for CO, HC, NO, and PM EFs measured by VERSS are presented in Table 7. Each pollutant dataset was sorted by ascending values; the

Table 7 Overlap between CO, HC, NO and PM highest 10% emitters for the VERSS data Number of vehicles Total number of vehicles No high emitters in any category High emitters in one category CO HC NO PM

13,786 9975

Fraction of measured fleet

556 326 812 816

72.36% Total: 18.20% 4.03% 2.36% 5.89% 5.92%

High emitters in two categories CO & HC CO & NO CO & PM HC & NO HC & PM NO & PM

436 49 56 194 70 137

Total: 6.84% 3.16% 0.36% 0.41% 1.41% 0.51% 0.99%

High emitters in three categories CO & HC & NO CO & HC & PM CO & NO & PM HC & NO & PM

62 174 9 79

Total: 2.35% 0.45% 1.26% 0.07% 0.57%

High emitters in all four categories

38

0.28%

492


Table 8 Overlap between CO and HC for low-idle and high-idle highest 10% emitters for the I/M data Number of vehicles Total number of vehicles No high emitters in any category High emitter in one category COlow-idle HClow-idle COhigh-idle HChigh-idle

55,903 44,359

Fraction of entire fleet

1856 1226 975 1191

79.35% Total: 9.38% 3.32% 2.19% 1.74% 2.13%

High emitter in two categories COlow-idle & HClow-idle COlow-idle & COhigh-idle COlow-idle & HChigh-idle HClow-idle & COhigh-idle HClow-idle & HChigh-idle COhigh-idle &HChigh-idle

632 675 70 243 740 880

Total: 5.79% 1.13% 1.21% 0.13% 0.43% 1.32% 1.57%

High emitter in three categories COlow-idle & HClow-idle & COhigh-idle COlow-idle & HClow-idle & HChigh-idle COlow-idle & COhigh-idle & HChigh-idle HClow-idle & COhigh-idle & HChigh-idle

347 239 307 699

Total: 2.85% 0.62% 0.43% 0.55% 1.25%

High emitter in all four categories

1464

2.62%

vehicles in the highest decile (i.e., highest 10% fraction) were selected and termed high emitters for that pollutant. Numbers of high emitters and high emitters as fraction of the measured vehicles were calculated for single pollutants and all combinations of multiple pollutants. The number of vehicles that do not appear as high emitters for any pollutant is also reported. Vehicles in the highest 10% of the sorted measurements had EFs higher than 98 gCO/kgFuel, 6.5 gHC/kgFuel, 26 gNO/kgFuel, 0.3 gPM/kgFuel for CO, HC, NO and PM, respectively. Table 8 summarizes results for CO and HC, for low-idle and high-idle I/M test data, to understand the level of higher emitter overlap in the different test measurements. Each pollutant dataset at each idle was sorted by ascending values; the vehicles in the highest decile (i.e., highest 10% fraction) were selected and termed high emitters for that pollutant. Numbers of high emitters and corresponding fraction of the measured vehicles were calculated for single pollutant and idle and all combinations of multiple pollutants and idles. The number of vehicles that do not appear as high emitters for any pollutant and idle is also reported. Vehicles in the highest 10% of the sorted measurements had exhaust concentrations higher than 0.43% CO (low-idle), 150 ppmv HC (low-idle), 0.48% CO (high-idle), 72 ppmv HC (high-idle), respectively. Figs. 8 and 9 permit a visual perception of the overlap between different categories. Each circular ring was drawn so that the area of each ring is proportional to the total fraction of none, one, two, three or all the pollutants belonging to the highest 10% emitters, respectively. In each ring the single sector angle was calculated so that the area included in it is proportional to the particular category. For space reasons the ring in which no high emitters are present in any of the pollutant categories (‘‘none’’) was just partially drawn by using a dashed line. See Tables 7


493

None 72.36%

HC 2.36% CO 4.03% CO&HC

NO 5.89%

CO&NO

CO&HC &PM

CO&PM

CO&HC &NO HC&NO &PM

CO&NO &PM

NO&PM

HC&NO HC&PM

PM 5.92%

Fig. 8. Graphical depiction of the overlap fleet fractions for pollutants measured by VERSS.

and 8 for complete list of percentages. In Fig. 8 for example, the area of the ‘‘HC + PM’’ sector in the third ring is proportional to the 0.51% of the total fleet shown in Table 7 and represents the fleet fraction in which a vehicle was a high emitter (meaning belonging to the group of highest 10% emitters) in PM and HC. To indicate the overlap between highest NO and PM emitters, disregarding whether the vehicle is or is not in the HC and CO high emitter category, the areas included in the sectors NO + PM, HC + PM, CO + PM, HC + NO, and CO + NO are added. The central black dot represents the fraction of the fleet that is among the highest 10% of emitters for all four pollutants. The small area of the central black dot shows that it is very unlikely for a vehicle to be a high emitter for all four pollutants (0.28% of all measurements). The detection of the highest emitters of one pollutant does not help substantially with the detection of the highest emitter for the remaining three pollutants. Fig. 9 depicts the low-idle and high-idle I/M HC and CO overlap fleet fractions reported in Table 8. ‘‘HC L’’ and ‘‘CO L’’ stand for HC and CO concentrations measured at low-idle; and ‘‘HC H’’ and ‘‘CO H’’ stand for HC and CO concentrations measured at high-idle. The overlapping fleet fractions reported in Table 8 were higher than those reported in Table 7, as expected. However, only 2.62% of the measurements belonged to the group of higher emitters for both HC and CO for both low- and high-idle speeds. Tests performed on the same vehicles, but under different conditions (800 and 2500 rpm), indicated disjoint high emitter categories even for the same pollutant. For HC, this disjunction probably also reflected the different fleet distributions shown in Fig. 1 for the two engine idle speeds.

494


None 79.35%

CO L 3.32% CO L&H

HC L 2.19%

CO L& HC H HC L& CO H

CO L&HC L CO L&HC L &HC H CO L&H &HC L

CO L&H &HC H

HC L&H&CO H

HC L&H CO H&HC H

HC H 2.13% CO H 1.74%

Fig. 9. Graphical depiction of the overlap fleet fractions for low-idle and high-idle HC and CO I/M measurements.

High emitters may be identified based on one pollutant measurement at one operating condition, but are not detected from the measurement of other pollutants at different engine operating conditions. Therefore for an effective screening of high emitters, it is important to measure each pollutant independently. It is also desirable to measure the emissions at different engine operation conditions, ideally under realistic loads. This could be accomplished by remotely sensing emissions while a vehicle is idling at a stop, cruising on a road and as the vehicle is accelerating.

4. Conclusions Results from this study show that CO and HC high emitters emitted a larger amount of PM and NO as measured by VERSS, but there is no consistent quantitative relationship between emissions of these pollutants on an individual vehicle basis. These results preclude the possibility of effectively screening for high NO and PM emitters solely by using the common CO and HC I/M test results. Very small overlap was found between high emitters for different pollutants in both I/M and VERSS data. The conclusions drawn from this and previous studies suggest that for an effective PM and NO emission reduction policy, PM and NO must be tested by I/M and/or clean screening programs.


495

Dynamometer I/M testing for all regulated emissions could be a valid although expensive and time consuming approach. Especially in PM and ozone non-attainment areas, gaseous and PM VERSSs should be used in addition to existing I/M tests to study and screen fleet emissions at low cost and low inconvenience for vehicle owners. Acknowledgments This work was supported in part by the Federal Transit Administration (Contract 26-70030), Clark County (Contract C1062-99-item 26), Department of Defense Strategic Environmental Research and Development Program (Contract CP-1336), Coordinating Research Council (Contract E56-1), and by the Desert Research Institute. We would also like to thank Diane Stortz-Linz and Terry Swenson of the Nevada Department of Motor Vehicles, and Karim Youseff of the Nevada Department of Transportation for their assistance in obtaining vehicle registration databases and right of way permits for the measurements. Special thanks goes to Vicken Etyemezian for his support in Las Vegas and to Sean Ahonen, Casey Collins, Arsineh Hecobian, Robert Hill, and Ali Yimer for their help in the field. References Barber, P.W., Moosmu¨ller, H., Keislar, R.E., Kuhns, H.D., Mazzoleni, C., Watson, J.G., in press. On-road measurement of automotive particle emissions by ultraviolet Lidar and Transmissometer: theory. Measurement Science and Technology, in press. Beaton, S.P., Bishop, G.A., Zhang, Y., Ashbaugh, L.L., Lawson, D.R., Stedman, D.H., 1995. On-road vehicle emissions: regulations, costs, and benefits. Science 268, 991–993. Bishop, G.A., Stedman, D.H., 1990. On-road carbon monoxide emission measurement comparisons for the 1988–1989 Colorado oxy-fuels program. Environmental Science and Technology 24, 843–847. Bishop, G.A., Stedman, D.H., 1996. Measuring the emissions of passing cars. Accounts of Chemical Research 29, 489– 495. Bishop, G.A., Stedman, D.H., Ashbaugh, L.L., 1996. Motor vehicle emissions variability. Journal of the Air and Waste Management Association 46, 667–675. Clark, N.N., Jarrett, R.P., Atkinson, C.M., 1999. Field measurements of particulate matter emissions, carbon monoxide, and exhaust opacity from heavy-duty diesel vehicles. Journal of the Air and Waste Management Association 49, PM76–PM84. Guenther, P.L., Stedman, D.H., Bishop, G.A., Beaton, S.P., Bean, J.H., Quine, R.W., 1995. A hydrocarbon detector for the remote sensing of vehicle exhaust emissions. Review of Scientific Instruments 66, 3024–3029. Heywood, J.B., 1988. Internal Combustion Engine Fundamentals. McGraw-Hill, New York. Holmes, K.J., Cicerone, R.J., 2002. The Road Ahead for Vehicle Emissions Inspection and Maintenance Programs. Environmental Manager (July), 15–22. Jimeńez, J.L., McRae, G.J., Nelson, D.D., Zahniser, M.S., Kolb, C.E., 2000. Remote sensing of NO and NO2 emissions from heavy-duty diesel trucks using tunable diode lasers. Environmental Science and Technology 34, 2380–2387. Kuhns, H.D., Mazzoleni, C., Moosmu¨ller, H., Nikolic, D., Barber, P.W., Keislar, R.E., Li, Z., Etyemezian, V., Watson, J.G., 2004. Remote sensing of PM, NO, CO, and HC emission factors for on-road gasoline and diesel engine vehicles in Las Vegas, NV. Science of the Total Environment 322, 123–137. Lawson, D.R., 1993. ‘‘Passing the test’’—Human behavior and Californias smog check program. Journal of the Air and Waste Management Association. 43, 1567–1575. Lawson, D.R., 1995. The costs of ‘‘M’’ in I/M—Reflections on inspection/maintenance programs. Journal of the Air and Waste Management Association 45, 468–476.

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