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Nanoparticle emissions on Minnesota highways
ARTICLE IN PRESS AE International – North America Atmospheric Environment 38 (2004) 9–19
Nanoparticle emissions on Minnesota highways D.B. Kittelson*, W.F. Watts, J.P. Johnson Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA Received 30 April 2003; received in revised form 9 August 2003; accepted 15 September 2003
Abstract The objective of this project was to characterize on-road aerosol on highways surrounding the Minneapolis area. Data were collected under varying on-road traffic conditions and in residential areas to determine the impact of highway traffic on air quality. The study was focused on determining on-road nanoparticle concentrations, and estimating fuel-specific and particle emissions km1. On-road aerosol number concentrations ranged from 104 to 106 particles cm3. The highest nanoparticle concentrations were associated with high-speed traffic. At high vehicular speeds engine load, exhaust temperature, and exhaust flow all increase resulting in higher emissions. Less variation was observed in particle volume, a surrogate measure of particle mass. Most of the particles added by the on-road fleet were below 50 nm in diameter. Particles in this size range may dominate particle number, but contribute little to particle volume or mass. Furthermore, particle number is strongly influenced by nucleation and coagulation, which have little or no effect on particle volume. Measurements made in heavy traffic, speedso32 km h1, produced lower number concentrations and larger particles. Number concentrations measured in residential areas, 10–20 m from the highway, were considerably lower than onroad concentrations, but the size distributions were similar to on-road aerosol with high concentrations of very small (o20 nm) particles. Much lower number concentrations and larger particles were observed in residential areas located 500–700 m from the highway. Estimated emissions of total particle number larger than 3 nm ranged from 1.9 to 9.9 1014 particles km1 and 2.2–11 1015 particles (kg fuel)1 for a gasoline-dominated vehicle fleet. r 2003 Elsevier Ltd. All rights reserved. Keywords: Nanoparticles; Nuclei mode; Particulate matter; On-road measurements; Particle size
1. Introduction Fig. 1 illustrates relationships between idealized trimodal Diesel aerosol number, and mass weighted size distributions (Whitby and Cantrell, 1976), and an alveolar deposition curve (ICRP, 1994). A sparkignition (SI) size distribution is similar, but has a mass median diameter that is typically smaller. For Diesel aerosol, the nuclei mode typically contains o10% of the particle mass but >90% of the particle number. Most of the mass is composed of carbonaceous agglomerates and adsorbed materials; it is found in the accumulation *Corresponding author. E-mail address: [email protected] (D.B. Kittelson).
mode. The coarse mode contains 5–20% of the mass. Nuclei mode particles emitted by either Diesel or SI engines are usually composed of nearly all-volatile material (Abdul-Khalek and Kittelson, 1995; Graskow et al., 1998; Mayer et al., 1998; Kittelson et al., 2002). Epidemiological and laboratory studies have linked environmental exposure to particles less than 2.5 mm in size with adverse health effects (Dockery et al., 1993; . Pope et al., 1995; Lippmann et al., 2000; Oberdorster et al., 2000; Wichmann et al., 2000). These studies have elucidated a range of causal mechanisms, but have not developed a quantitative understanding of their relative importance. These studies have not, for the most part, focused on determining the physical characteristics of the aerosol to which people are exposed. Although
1352-2310/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2003.09.037
ARTICLE IN PRESS D.B. Kittelson et al. / Atmospheric Environment 38 (2004) 9–19
10 0.25
1
Ultrafine Particles Dp < 100 nm
0.8 PM10 Dp <10 µm
0.15
0.6 Deposition Accumulation Mode
0.1
0.4
Deposition
Normalized Concentration (Ctotal-1)dC dlogDp-1
Fine Particles Dp < 2.5 µm
Nanoparticles Dp < 50 nm
0.2
Nuclei Mode Coarse Mode 0.05
0.2
0 1
10
100
1,000
0 10,000
Diameter (nm)
Number
Surface
Mass
Deposition (Alveolar + Tracheo-Bronchial, ICRP 1994)
Fig. 1. Typical Diesel mass and number weighted size distributions shown with alveolar deposition.
regulatory agencies such as the US Environmental Protection Agency (EPA) have adopted mass-based air pollution regulations for particulate matter, other metrics, such as particle number or surface area, may be important in characterizing the physical properties of aerosol related to health effects (McCawley, 1990). Laboratory studies (Andersson and Wedekind, 2001; Booker, 1997; CONCAWE, 1998; Graskow et al., 1998; Greenwood et al., 1996; Hall et al., 1998; McAughey et al., 1996; Maricq et al., 1999a, b) have shown that nanoparticle emissions from SI engines are much more speed- and load-dependent than Diesel engines, and that SI engines emit a higher proportion of smaller particles than do Diesels. Our on-road studies have shown that high speed and load conditions, such as freeway cruise and hard acceleration, produce SI number emissions nearly as high as those produced by Diesel engines. Under less severe conditions, SI emissions are considerably lower (Kittelson et al., 2002, 2003). Measurements from recent model SI vehicles, over the FTP and ECE driving cycles, showed tailpipe particulate matter emissions to be very low (Maricq et al., 1999a). Mass emission rates from a light duty truck ranged from 4.3, during the cold-start phase of the FTP drive cycle, to o0.062 mg km1, during phase 2, for nearly half the vehicles reported. All of the conventional gasoline vehicles in this study emitted o1.24 mg km1 particulate matter, compared to the current 50 mg km1 particulate matter standard. The mean particle size for all vehicles tested ranged from 35 to 65 nm; by mass it ranged from 100 to 300 nm.Minor changes in distribution were observed between vehicles. The number of particles emitted over the transient test conditions ranged from
3 1010 to 7.9 1013 particles phase1. Peak particle emissions occur principally during short periods of heavy acceleration and coincide with peak hydrocarbon, CO, and NOx emissions. The Northern Front Range Air Quality Study (Watson et al., 1998; Cadle et al., 1998) found that vehicle exhaust was the largest PM2.5 total carbon (TC) contributor, constituting about 85% of PM2.5 carbon at sites in the Denver metropolitan area. Sources with emissions similar to light-duty-gasoline vehicles contributed about 60% of PM2.5 TC at urban Denver sites; these contributions were 2.5–3 times the Diesel exhaust contributions. Emissions were particularly high during cold, cold starts and from vehicles identified as ‘‘smokers’’. Still, Diesels contributed the largest share of the ‘‘elemental’’ carbon (EC) at these locations. Uncertainty exists regarding the relative contribution of atmospheric aerosol from SI- and Diesel-exhaust in real-world conditions. Diesels emit more particulate matter per vehicle; but, because SI vehicles, at least in the US, account for most of the vehicles operating onroad, the direct PM emissions from SI engines may be more important (Cadle et al., 1998).
2. Mobile emissions laboratory and instrumentation The University of Minnesota mobile emissions laboratory (MEL) was used to collect gaseous and aerosol data while operating on Minnesota roadways around Minneapolis. The sample air intake was located 69 cm above the highway and 43 cm in front of the MEL to simulate an automobile’s air inlet. Total
ARTICLE IN PRESS D.B. Kittelson et al. / Atmospheric Environment 38 (2004) 9–19
sample-line length was less than 11 m with a flow rate of 400 l min1 through a 10.2 cm diameter tube; the calculated penetration efficiency for 10 nm size particles was greater than 95%. A manifold distributed the sample air to the instruments. The TSI 3071 scanning mobility particle sizer (SMPS) was used to determine the number, surface area, and volume size distributions. The SMPS classified particles from 8 to 300 nm and was operated with a TSI 3025A CPC. A standalone TSI 3025A CPC was used to determine the total number concentration for particles ranging in size from about 3 to 1000 nm (Kesten et al., 1991; Wiedensohler et al., 1990). This CPC has a maximum concentration of 9.99 104 particles cm3 so it was used with a leaky-filter dilutor, which had a dilution ratio of 220:1. The leaky filter dilutor is a passive dilutor, and is dependent upon the instrument flow rate. A glass capillary tube was placed inside an absolute capsule filter to create a leak through the filter. The diameter of the capillary tube determined the dilution ratio. The dilution ratio was established using a polydisperse ammonium sulfate aerosol, both with and without the leaky filter dilutor in line. A nebulizer was used to spray the polydisperse ammonium sulfate aerosol to the particle instruments on a daily basis to check the consistency of the size distributions and number concentrations. The total particle number concentrations measured by the 3025A CPC and calculated from the SMPS were different because the 3025A CPC counts particles as small as 3 nm while the SMPS goes down to only 8 nm. This situation is analogous to PM10 and PM2.5, where the metric being measured is the same but the cutoff size is different. Three gas analyzers were also used: a non-dispersive infrared (NDIR) CO2 analyzer, a NDIR CO analyzer, and a chemiluminescence NOx analyzer. These instruments have a response time of about 1 s; they were used to determine on-road and background ambient gas concentrations. Each instrument was zeroed and spanned daily according to the manufacturers’ instructions and 10 point calibrations were done every 6 months. The quality assurance protocol was developed during the Coordinating Research Council E-43 project and is reported elsewhere (Ayala et al., 2002, 2003).
3. Methods and approach The MEL speed, location, traffic conditions, weather, presence of Diesel traffic, and other pertinent information were recorded while on-road. These logs were electronically transcribed, coded, and associated with the raw data obtained by the instruments. Date and time
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stamps in the electronically recorded instrument data allowed logged information to be associated with electronically recorded data. Routes with high daily traffic volumes and speeds, representative of the Twin Cities area, comprised the sampling region. Stationary sampling points located upwind or downwind of the roadways, in residential areas, and with low-traffic volumes were sampled to obtain residential background concentrations. The MEL covered about 800 km over 5 days of sampling; a variety of traffic conditions were encountered and sampled. A conscious effort was made to avoid directly sampling exhaust plumes from on-road vehicles. The driver of the MEL was asked to maintain a distance of at least 10 m between the MEL and the vehicle directly in front of the MEL. This distance was established based upon safety and the desire not to impede traffic flow.
4. Results and discussion Fig. 2 shows data covering a 3 h sampling period. The calculated SMPS integrated number concentration tracks with the total CPC number concentration, but the SMPS has a lower concentration. This is consistent with the CPC having a wider counting range, especially on the low-end of the particle size spectrum. Fig. 2 also shows the impact of the interstate highway on surrounding neighborhoods located about 700 m upwind (14:30) and downwind (15:10) of the roadway, where the MEL was parked to collect residential area background data. Concentrations increased dramatically as the MEL got closer to and finally crossed over the interstate highway around 14:45. Fig. 2 and Tables 1 and 2 show the relationship between particle number concentration and MEL speed. Table 1 shows data obtained from the CPC and Table 2 shows data from the SMPS. On-road particle number concentrations ranged from 104 to >106 particles cm3; higher speeds were associated with higher concentrations. The MEL moved with traffic and at a low speed when vehicle density was high; traffic was generally bumper to bumper. At high speeds vehicle density was low, allowing the MEL to move at posted speed levels. The traffic pattern was consistent with a large metropolitan area where rush hours congest main arteries in the morning and late afternoon. Our sampling generally took place between 1 and 5 PM allowing us to experience both free flowing and congested traffic conditions. The average weekday traffic data collected from the Minnesota Department of Transportation showed that the Diesel to SI ratio for specific observation points on the routes taken in this study was about 0.09. We presume that ambient weather conditions may have influenced the number concentrations. This study
ARTICLE IN PRESS D.B. Kittelson et al. / Atmospheric Environment 38 (2004) 9–19
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Fig. 2. Strip chart for 15 November 2000.
Table 1 On-road averages by condition and speed Condition
Speed range (km h1)
CO2 (ppm)
CPC (particles cm3)
NOx (ppb)
Avg
Std
Avg
Std
Avg
Std
Median
Diesel No Diesel Diesel No Diesel Diesel No Diesel
>0o32 >0o32 X32p80 X32p80 >80 >80
416 395 437 406 403 396
40 38 50 43 32 25
147 98 271 160 261 129
135 139 250 196 225 133
9.21E+04 5.75E+04 5.85E+05 4.01E+05 1.28E+06 3.74E+05
8.82E+04 2.97E+05 2.35E+06 2.13E+06 3.89E+06 1.80E+06
7.33E+04 1.88E+04 1.88E+05 6.85E+04 4.79E+05 1.12E+05
All on-road
>0
404
38
155
179
4.03E+05
2.03E+06
9.12E+04
was conducted in November 2000, when ambient daily temperatures ranged between 1 C and 13 C, with relative humidity between 40% and 60%. Our results show that colder ambient temperatures contribute to increased nanoparticle formation (Wei et al., 2001; Kittelson et al., 2003). Tables 1 and 2 show data classified by speed and the presence or absence of neighboring Diesel vehicles. When a heavy-duty Diesel passed the MEL or entered the highway in front of the MEL, a sampling window of 60 s was defined, because of the possible impact that the exhaust plume may have had on our measurements. The collected data were coded to reflect that a heavy-duty
vehicle was present within the 60 s window of time. The tables illustrate that Diesel traffic increased the particle number concentration in all speed groups. Table 2 also shows the average integrated number-to-volume ratio ðN=V Þ (Kittelson, 1998) and geometric mean particle size (DGN) derived from the SMPS data. The particle size decreased with increasing speed and did not appear to be affected by the presence of heavy-duty Diesel trucks. The N=V ratio increased with increasing speed and was larger in the presence of heavy-duty Diesel trucks. An N=V ratio >104 indicates the presence of a large nuclei mode (Kittelson, 1998; Kittelson et al., 2002).
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Table 2 Integrated SMPS averages by condition and speed Condition
Speed range (km h1)
Sample (N)
N total (particles cm3)
N=V (part mm3)
DGN (nm)
Avg
Std
Avg
Std
Avg
Std
Diesel No Diesel Diesel No Diesel Diesel No Diesel
>0o32 >0o32 X32p80 X32p80 >80 >80
31 33 32 66 40 111
1.02E+05 4.72E+04 1.46E+05 9.68E+04 1.80E+05 8.27E+04
7.94E+04 5.14E+04 1.00E+05 1.36E+05 2.94E+05 9.90E+04
1.70E+04 1.07E+04 2.18E+04 1.92E+04 3.32E+04 3.07E+04
1.34E+04 1.06E+04 1.37E+04 2.36E+04 3.11E+04 3.72E+04
25 26 20 20 14 16
8 13 7 7 3 5
All on-road
>0
313
1.03E+05
1.46E+05
2.42E+04
2.88E+04
19
8
Table 3 Average residential area data by location Location
CPC (particles cm3) Avg
Std
Median
SMPS N/V (part mm3)
SMPS DGN (nm)
CO2 (ppm)
NOx (ppb)
Avg
Std
Avg
Std
Avg
Std
Avg
Std
9.16E+03
23
9
370
14
31
49
1.17E+03
30
8
364
6
15
27
10 m from 3.80E+04 3.57E+04 2.45E+04 8.05E+03 highway 700 m from 9.44E+03 8.70E+03 8.37E+03 3.17E+03 highway
Table 3 shows the average CO2, NOx, and CPC particle concentrations and the average SMPS N=V and DGN for data collected in residential areas 10 and 700 m from the highway. Particle number, CO2, and NOx concentrations decreased with increasing distance from the highway. The N=V also decreased, but the DGN increased indicating that the nuclei mode was less pronounced farther away from the highway. The N=V ratios shown in Tables 2 and 3 give the number of particles formed per-unit-volume of emitted particulate matter (particles mm3). Since particle mass is proportional to particle volume, the N=V ratio gives a measure of the particles formed per unit-mass emitted. For spherical particles with an effective density of 1 g cm3, an N=V ratio of 1 particle mm3 corresponds to 1012 particles g1. On-road samples are more likely to have N=V >104, as shown in Table 2, while samples collected away from major highways nearly always have an N=V o104, as shown in Table 3. The higher the vehicle speed the greater the N=V ratio and the smaller the particle size. Park et al. (2003) have shown that Diesel particles exhibit a fractal like behavior where the effective density decreases with increasing particle size. This decrease ranges from about 1.1 to 0.3 g cm3 as particle size increases from 50 to 300 nm. Schaberg et al. (2002) measured the ratio of filter mass to SMPS volume for two different Diesel engines and found the effective density ranged from 0.74 to 0.87. These results are for Diesel engines and, although similar work has not been done for SI engine particles, we have assumed an
effective density of 1.0 for our calculations. Our assumption is supported by the fact that the nuclei mode for a Diesel engine is mainly composed of volatile material from lubrication oil, which has a density of 0.9 g cm3 (Sakurai et al., 2003a, b). 4.1. SMPS size distributions SMPS data are shown as average size distributions grouped by condition. The number (or volume) concentration of particles, per log-diameter interval of the size distribution (dN d log D1 or dV d log D1 p p ), are shown on the y-axis; N is the particle number (or V is the volume) and Dp is the particle diameter. When data are plotted in this way, the area under the curve gives the number (or volume) concentration of particles in that size range, given that the y-axis is on a linear scale and the x-axis is on a log scale. Fig. 3 shows the average number and volume size distributions for the residential and on-road samples with the standard deviation of the mean (SDOM). There is a significant difference between the nuclei modes of the three number size distributions; as the distance from the highway increases, the magnitude of the nuclei mode decreases. There is less difference in the volume distributions. Since the SMPS was operated to size particles from 8 to 300 nm, the complete accumulation mode (where most of the volume resides) was not captured. This is indicated by the upward trend of the volume-size distributions.
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14 2.5E+05
2.0E+01
Error bars are standard deviation of the mean.
1.8E+01 1.6E+01 1.4E+01
1.5E+05
1.2E+01 1.0E+01 8.0E+00
1.0E+05
6.0E+00 5.0E+04
dV dlogDp-1, µm3 cm-3
dN dlogDp-1, particles cm-3
2.0E+05
4.0E+00 2.0E+00
0.0E+00 1
10
0.0E+00 1000
100
Midpoint diameter, nm Onroad (Number)
Residential (Number) 10 m from Hwy 62
Residential (Number) 700 m from I-494
On road (Volume)
Residential (Volume) 10 m from Hwy 62
Resdential (V) 700 m from I-494
Fig. 3. Average number and volume SMPS size distributions grouped by location.
Number, 151 scans, Speed >= 80 Number, 131 scans, Speed >=8<=48 Volume, 51 scans, Speed > 48 < 80
3.5E+05
Number, 51 scans, Speed >48 < 80 Volume, 151 scans, Speed >= 80 Volume, 131 scans, Speed >=8<=48
25
Error bars are standard deviation of the mean 3.0E+05
2.5E+05 15
2.0E+05
1.5E+05
10
1.0E+05 5
5.0E+04
0.0E+00 1
10
100
Midpoint diameter, nm Fig. 4. On-highway SMPS number and volume distributions grouped by speed.
0 1000
dV dlogDp-1, µm3 cm-3
dN dlogDp-1, particles cm-3
20
ARTICLE IN PRESS D.B. Kittelson et al. / Atmospheric Environment 38 (2004) 9–19
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on particle number than volume. The increase in particle volume or mass, at lower vehicle speeds, was consistent with expectations of higher concentrations under congested conditions. Less variation was observed in particle volume compared to particle number size distributions.
Fig. 4 shows the relationship between speed, aerosol number, and volume size distributions. The magnitude of the nuclei mode increases with vehicle speed, while particle size decreases. The error bars on the nuclei mode portion of the size distributions do not overlap; this suggests that the increase in magnitude is statistically significant. DGN and N/V data from Table 2 show the magnitude of the particle size decrease, which is about 10 nm, and the corresponding increase in the N/V ratio. The magnitude of the increase in particle number concentration is even greater for the CPC data shown in Table 1. At high vehicular speeds, particulate matter emissions increase because of higher engine load, exhaust temperature, and exhaust flow. This observation has been reported in other studies (Zhu et al., 2002a, b), but in these studies samples were collected near the roadway, as opposed to traveling on-road. Some of the particles observed at higher speeds are likely due to transient release of particle-associated materials stored in exhaust systems during lower speed operation (Hall and Dickens, 2000). Diesel engine particle emissions are relatively independent of speed and load (Kittelson et al., 2002); conversely, SI engine emissions are strongly dependent upon speed and load, as previously discussed. Slower speeds produced larger particles and larger aerosol volumes, as illustrated by the volume distributions in Fig. 4. Particle volume is a surrogate measure of particle mass and, unlike particle number, it is not influenced by coagulation and is less influenced by nucleation. Furthermore, particles added by the on-road fleet are much smaller than residential background particles, therefore, they have a much stronger influence
4.2. Fuel-specific particulate matter emissions Emission-factor estimates can be derived from our data. The estimates in Tables 4 and 5 were calculated for the 10th and 15th of November and are representative of a predominantly gasoline-vehicle dominated scenario. As mentioned previously, the Diesel to SI ratio for specific observation points on the routes taken in this study was about 0.09 during weekday periods. The highway concentrations were obtained from data collected on a section of roadway parallel to and between the residential locations; residential location data were used to correct the on-road data for the background contribution. The calculation was based on the ratio of particles to carbon added by the fleet. It was assumed that all of the carbon added by highway traffic was in the form of either CO2 or CO. This was a reasonable assumption since gasoline engine combustion efficiencies are better than 90% (Heywood, 1988); thus, more than 90% of the carbon in the fuel was converted to CO2 and most of the incompletely burned carbon is emitted as CO. The latter is illustrated by the US Federal tailpipe emission standards for new vehicles that allow emissions of more than 10 times the amount of CO compared to non-methane organic gases (2.112 versus 0.255 g km1). The particles per mass of carbon may be
Table 4 On-road particle number emission estimates derived from the CPC and SMPS Date
11/10/00 11/10/00 11/10/00 11/15/00
Time From
To
15:38 15:51 16:39 15:09
15:51 16:39 16:48 15:29
Location
CPC (part km1)
SMPS (part km1)
CPC (part kg1 fuel)
SMPS (part kg1 fuel)
I-494S I-494S traffic jam I-494E I-494S
1.93E+14
8.70E+13
2.92E+14 9.94E+14
2.73E+14 2.24E+14
2.2E+15 2.8E+15 3.4E+15 1.1E+16
1.0E+15 3.0E+15 3.1E+15 2.6E+15
Table 5 On-road fuel specific mass emission estimates derived from the SMPS Date
11/10/00 11/10/00 11/10/00 11/15/00
Time
Location
From
To
15:38 15:51 16:39 15:09
15:51 16:39 16:48 15:29
I-494 I-494 I-494 I-494
going going going going
SMPS (mg km1) S S traffic jam E S
7.8 11.6 6.2
SMPS (mg kg1 fuel) 89 325 132 71
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converted to particles per mass of fuel if the carbon content of the fuel is known. The carbon content of gasoline ranges from 0.83 to 0.86 as a mass fraction (Chevron, 1996); for our calculations, 0.85 was used. These calculations yield the fuel-specific emissions in particles kg1 of fuel. Emissions expressed in this form are especially useful if total fuel consumption by a vehicle fleet is known. These results may also be used to estimate particle emissions in particles km1, assuming vehicle fleet average fuel consumption. An average fuel consumption rate of 8.5 km l1 and an average fuel density of 0.74 kg l1 for gasoline were assumed (Owen and Coley, 1995). Table 4 shows the fuel-specific particle emissions derived from our data corrected for the background contribution. Estimates based upon the CPC range from 1.9–9.9 1014 part km1 and 2.2 1015 to 1.1 1016 part kg1 fuel. The particle km1 estimates shown in Table 4 are higher for the CPC than for the SMPS. This is likely due to the difference in the lower particle size detection limits of the instruments (3 versus 8 nm); and it suggests that a large number of particles lie between 3 and 8 nm. Similar observations have been reported elsewhere (Kittelson et al., 2001; Shi et al., 2001). The SMPS provides estimates of the number, surface area, and volume particle size distributions, each corrected for any background contribution. The volume distribution is equivalent to the mass distribution if particles are spherical and a constant particle density is assumed. For the reasons described above a density of 1 g cm3 has been assumed. The mass fuel-specific emission estimates derived from the SMPS are shown in Table 5 and range from 6.2 to 11.2 mg km1 and 71 to 325 mg kg1 of fuel. Results reported here are not dissimilar from data reported in other technical literature. In a European study (Rickeard et al., 1996) that evaluated Diesel and SI emissions in the laboratory, it was reported that, at 50 km h1 emissions from Diesel vehicles varied con, siderably. The lowest emitting vehicle produced less than half the particle emissions of the highest emitting vehicle (5.0 1013 compared to 1.24 1014 particles km1); SI vehicles produced much lower emissions. However, at high speed (120 km h1), the pattern was different and both types of vehicles emitted large quantities of particles (1.2 1014 to 1.8 1014 particles km1). Mass emission estimates from 24 properly functioning and six high CO emitting light-duty SI vehicles derived from the US Federal Test Procedure hot start Unified Cycle were reported for a study done in Denver, CO (Cadle et al., 2001). The range of emissions varied from 2.2 to 7.9 mg km1 for the properly functioning vehicles and 14.9 to 32.3 mg km1 for the high emitters. In an Australian study, the average emission factor for vehicles was calculated to be 4.5 1014 particles km1 vehicle1 with a standard deviation of approxi-
mately 10–15% (Gramotnev et al., 2003). In another Australian study, the average emission factor obtained from a box model and roadside measurements was 1.75 1014 particles km1 vehicle1, with a standard error of 67.6% (Jamriska and Morawska, 2001). The authors compared their estimates to those obtained from chassis dynamometer experiments and suggested that laboratory studies may underpredict realworld emission estimates. Laboratory studies evaluate a limited number of vehicles and test conditions and may not include high emitting conditions such as acceleration or evaluation of high emitting vehicles. A variety of light-duty Diesel and SI automobiles were evaluated on a chassis dynamometer using the electrical low-pressure impactor (ELPI) (F.arnlund et al., 2001). The ELPI is capable of sizing particles from 10 nm to 10 mm, however, its calibration below 30 nm is subject to question. Particle km1 emissions for gasoline-fueled, naturally aspirated SI vehicles ranged from about 3.7 1011 to 5.6 1013 when evaluated on a moderate driving cycle. Still, the worst naturally aspirated SI engine emitted 1000 times more particles than the best engine of the same type. Particle km1 emissions for turbocharged SI engines were found to emit between 8.7 1012 and 8.1 1013 particles km1, with less variation between engines. Emissions from Diesel engines ranged from about 6.8 1013 to 3.1 1014 particle km1. The number emissions that we report are on the high end of those reported in the literature. This is likely due to several factors. The 3025A CPC used in this study can detect smaller particles than the instruments used in other studies. Furthermore, the relatively cold ambient temperatures and on-road dilution conditions favor nanoparticle formation (Kittelson et al., 2002); studies conducted on chassis dynamometers, using a constant volume sampling dilution tunnel, may suppress nanoparticle formation (Kittelson et al., 2002, 2003).
5. Conclusion This study differs from previous studies because data were collected on-road, under varying traffic conditions, as opposed to collecting data from stationary sampling points near the highway. On-road size distributions and fuel-specific emissions corrected for background were obtained. Data were corrected using background concentrations measured in residential areas located upwind of the highway. When sampling in residential areas, data were collected upwind and downwind from the highway to determine the impact of the highway on residential air quality and to determine the effects of aerosol aging on the plume aerosol concentration and size distribution.
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Particulate matter concentrations on-road ranged between 104 and 106 particles cm3, with the majority of the particles being less than 50 nm in diameter. An association between the on-road speed of the MEL and particle number concentration and particle size was observed: The higher the speed, the greater the particle concentration, and the smaller the particle size. This is a reasonable finding because at high vehicular speeds, particulate number emissions, especially from SI engines, increase with increased engine load, exhaust temperatures and exhaust flow. Diesel traffic was observed to further increase particle number concentrations. Measurements made in traffic jams, with speeds o32 km h1 showed lower concentrations and larger particles. Less variation was observed in particle volume compared to particle number size distributions. In our tests, volume was influenced more by background concentrations than by local on-road conditions. Measurements made in residential areas demonstrated that aerosol concentrations increased as the distance from the roadway decreased. The size distributions from these areas were similar to on-road aerosol with high concentrations of very small (o20 nm) particles. Much lower concentrations and much larger particles were observed in residential areas located 500–700 m from the highway. Fuel specific and particle km1 emission rates were estimated from data collected on two different days; the estimates were similar to results from other studies. Estimates based upon the CPC range from 1.9– 9.9 1014 part km1 and 2.2 1015 to 1.1 1016 part kg1 fuel. Our results are limited because the study was conducted over a limited range of ambient temperature (1–13 C). Colder ambient temperatures increase particle number concentrations by favoring nucleation. To determine the full range of emission factors, data need to be collected for a wide variety of environmental and engine operating conditions under real-world dilution conditions.
Acknowledgements This work was funded in part by the University of Minnesota Center for Transportation Studies under a contract from the Minnesota Department of Transportation (MnDOT). The Coordinating Research Council (CRC) and the sponsors of the CRC E-43 project provided funding for the MEL and instrumentation. The Volvo Truck Corporation provided the tractor platform for the MEL. We would like to thank Mr. Bill Bunde and Ms. Marilyn Jordahl-Larson from MnDOT and Mr. Marcus Drayton from the University of Minnesota for their assistance in making this project possible.
17
References Abdul-Khalek, I.S., Kittelson, D.B., 1995. Real Time Measurement of Volatile and Solid Exhaust Particles Using a Catalytic Stripper. Society of Automotive Engineers, Warrendale, PA, 950236. Andersson, J., Wedekind, B., 2001. DETR/SMMT/CONCAWE particulate research program 1998–2001. Summary Report, Ricardo Consulting Engineers, 26pp. Ayala, A., Olson, B., Cantrell, B., 2002. Working quality assurance project plan for diesel aerosol sampling methodology. CRC Project E-43. Final Report, April 2002, 93pp. (Available from CRC, Alpharetta, GA). Ayala, A., Olson, B., Cantrell, B., Drayton, M., Barsic, N., 2003. Estimation of Diffusion Losses when Sampling Diesel Aerosol: A Quality Assurance Measure. Society of Automotive Engineers, Warrendale, PA, 2003-01-1896. Booker, D.R., 1997. Urban pollution monitoring: Oxford city centre, Research Report, AEA Technology, Aerosol Science Centre, Oxfordshire, UK. Cadle, S.H., Mulawa, P., Hunsanger, E.C., Nelson, K., Ragazzi, R.A., Barrett, R., Gallagher, G.L., Lawson, D.R., Knapp K. T., Snow, R., 1998. Measurement of exhaust particulate matter emissions from in-use light-duty Motor vehicles in the Denver, Colorado Area. Coordinating Research Council Project E-24-1 Final Report, CRC, Atlanta, GA, pp. 142. Cadle, S.H., Mulawa, P., Groblicki, P., Laroo, C., Ragazzi, R.A., Nelson, K., Gallagher, G., Zielinska, B., 2001. In-use light-duty gasoline vehicle particulate matter emissions on three driving cycles. Environmental Science and Technology 35, 26–32. Chevron, 1996. Motor Gasolines Technical Review. FTR-1 Chevron Products Company, 66pp. CONCAWE, 1998. A study of the number, size & mass of exhaust particles emitted from European diesel and gasoline vehicles under steady-state and European driving cycle conditions. Prepared for the CONCAWE Automotive Emissions Management Group by its Special Task Force AE/STF-10 (Brussels, Belgium) CONCAWE Report No. 98/51, 56pp. Dockery, D.W., Pope III, A., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris Jr., B.G., Speizer, F.E., 1993. An association between air pollution and mortality in six US cities. New England Journal of Medicine 329 (24), 1753–1759. F.arnlund, J., Homan, C., K(ageson, P., 2001. Emissions of ultrafine particles from different types of light duty vehicles. Swedish National Road Administration Publication, Vol. 10, 16pp. Gramotnev, G., Brown, R., Ristovski, Z., Hitchins, J., Morawska, L., 2003. Determination of average emission factors for vehicles on a busy road. Atmospheric Environment 37, 465–467. Graskow, B.R., Kittelson, D.B., Abdul-Khalek, I.S., Ahmadi M.R., Morris, J.E., 1998. Characterization of Exhaust Particulate Emissions from a Spark Ignition Engine. Society of Automotive Engineers, Warrendale, PA, 980528. Greenwood, S., Coxon, J.E., Biddulph, T., Bennett, J., 1996. An Investigation to Determine the Exhaust Particulate Size Distributions for Diesel, Petrol, and Compressed Natural
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McCawley, M.A., 1990. Should dust samples mimic human lung deposition? Counterpoint. Applied Occupational and Environmental Hygiene 5 (12), 829–835. . Oberdorster, G., Finkelstein, J.N., Johnston, C., Gelein, R., Cox, C., Baggs, R., Elder, A.C.P., 2000. Acute pulmonary effects of ultrafine particles in rats and mice. Health Effects Institute Report No. 96. Owen, K., Coley, T., 1995. Automotive Fuels Reference Book. 2nd Edition. Society of Automotive Engineers, Inc., Warrendale, PA, 963pp. Park, K., Cao, F., Kittelson, D.B., McMurry, P.H., 2003. Relationship between particle mass and mobility for diesel exhaust particles. Environmental Science and Technology 37, 577–583. Pope III, C.A., Thun, M.J., Namboodiri, M.M., Dockery, D.W., Evans, J.S., Speizer, F.E., Heath Jr., C.W., 1995. Particulate air pollution as a predictor of mortality in a prospective study of US adults. American Journal of Respiratory and Critical Care Medicine 151, 669–674. Rickeard, D.J., Bateman, J.R., Kwon, Y.K., McAughey, J.J., Dickens, C.J., 1996. Exhaust Particulate Size Distribution: Vehicle and Fuel Influences in Light Duty Vehicles. Society of Automotive Engineers, Warrendale, PA, 961980. Sakurai, H., Tobais, H.J., Park, K., Zarling, D., Docherty, K.S., Kittelson, D.B., McMurry, P.H., Ziemann, P.J., 2003a. On-line measurements of diesel nanoparticle composition, volatility, and hygroscopicity. Atmospheric Environment 37, 1199–1210. Sakurai, H., Park, K., McMurry, P.H., Zarling, D.D., Kittelson D.B., Ziemann, P.J., 2003b. Size-dependent mixing characteristics of volatile and non-volatile components in diesel exhaust aerosols. Environmental Science and Technology, submitted for publication. Schaberg, P., Kittelson, D.B., Zarling D.D., Waytulonis, R.W., 2002. Exhaust Particle Number and Size Distributions with Conventional and Fischer–Tropsch Diesel Fuels. Society of Automotive Engineers, Warrendale, PA, 2002-01-2727, 13pp. Shi, J.P., Evans, D.E., Khan, A.A., Harrison, R.M., 2001. Sources and concentration of nanoparticles (o10 nm diameter) in the urban atmosphere. Atmospheric Environment 35, 1193–1202. Watson, J.G., Fukita, E.M., Chow, J.C., Zielinska, B., Richards, I.W., Neff, W., Dietrich, D., 1998. Northern Front Range Air Quality Study Final Report, Document No. 6580-685-8750-IF2: Desert Research Institute, Reno, NC. Wei, Q., Kittelson, D.B., Watts, W.F., 2001. Single-Stage Dilution Tunnel Performance. Society of Automotive Engineers, Warrendale, PA, 2001-01-0201, 12pp. Whitby, K.T., Cantrell, B.K., 1976. Atmospheric aerosols: characteristics and measurement. In: International Conference on Environmental Sensing and Assessment (ICESA), Institute of Electrical and Electronic Engineers (IEEE), IEEE 75-CH 1004-1, September 14–19, 1975, Paper 29-1, ICESA, Las Vegas, NV, 6pp. . Wichmann, H.-E., Spix, C., Tuch, T., Wolke, G., Peters, A., Heinrich, J., Kreyling, W.G., Heyder, J., 2000. Daily mortality and fine and ultrafine particles in Erfurt, Germany. Part 1: role of particle number and particle mass. Health Effects Institute Report No. 98.
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highway. Journal of Air and Waste Management Association 52 (9), 1032–1042. Zhu, Y., Hind, W.C., Kim, S., Shen, S., Sioutas, C., 2002b. Study of ultrafine particles near a major highway with heavy duty diesel traffic. Atmospheric Environment 36, 4323–4335.
Nanoparticle emissions on Minnesota highways D.B. Kittelson*, W.F. Watts, J.P. Johnson Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA Received 30 April 2003; received in revised form 9 August 2003; accepted 15 September 2003
Abstract The objective of this project was to characterize on-road aerosol on highways surrounding the Minneapolis area. Data were collected under varying on-road traffic conditions and in residential areas to determine the impact of highway traffic on air quality. The study was focused on determining on-road nanoparticle concentrations, and estimating fuel-specific and particle emissions km1. On-road aerosol number concentrations ranged from 104 to 106 particles cm3. The highest nanoparticle concentrations were associated with high-speed traffic. At high vehicular speeds engine load, exhaust temperature, and exhaust flow all increase resulting in higher emissions. Less variation was observed in particle volume, a surrogate measure of particle mass. Most of the particles added by the on-road fleet were below 50 nm in diameter. Particles in this size range may dominate particle number, but contribute little to particle volume or mass. Furthermore, particle number is strongly influenced by nucleation and coagulation, which have little or no effect on particle volume. Measurements made in heavy traffic, speedso32 km h1, produced lower number concentrations and larger particles. Number concentrations measured in residential areas, 10–20 m from the highway, were considerably lower than onroad concentrations, but the size distributions were similar to on-road aerosol with high concentrations of very small (o20 nm) particles. Much lower number concentrations and larger particles were observed in residential areas located 500–700 m from the highway. Estimated emissions of total particle number larger than 3 nm ranged from 1.9 to 9.9 1014 particles km1 and 2.2–11 1015 particles (kg fuel)1 for a gasoline-dominated vehicle fleet. r 2003 Elsevier Ltd. All rights reserved. Keywords: Nanoparticles; Nuclei mode; Particulate matter; On-road measurements; Particle size
1. Introduction Fig. 1 illustrates relationships between idealized trimodal Diesel aerosol number, and mass weighted size distributions (Whitby and Cantrell, 1976), and an alveolar deposition curve (ICRP, 1994). A sparkignition (SI) size distribution is similar, but has a mass median diameter that is typically smaller. For Diesel aerosol, the nuclei mode typically contains o10% of the particle mass but >90% of the particle number. Most of the mass is composed of carbonaceous agglomerates and adsorbed materials; it is found in the accumulation *Corresponding author. E-mail address: [email protected] (D.B. Kittelson).
mode. The coarse mode contains 5–20% of the mass. Nuclei mode particles emitted by either Diesel or SI engines are usually composed of nearly all-volatile material (Abdul-Khalek and Kittelson, 1995; Graskow et al., 1998; Mayer et al., 1998; Kittelson et al., 2002). Epidemiological and laboratory studies have linked environmental exposure to particles less than 2.5 mm in size with adverse health effects (Dockery et al., 1993; . Pope et al., 1995; Lippmann et al., 2000; Oberdorster et al., 2000; Wichmann et al., 2000). These studies have elucidated a range of causal mechanisms, but have not developed a quantitative understanding of their relative importance. These studies have not, for the most part, focused on determining the physical characteristics of the aerosol to which people are exposed. Although
1352-2310/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2003.09.037
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10 0.25
1
Ultrafine Particles Dp < 100 nm
0.8 PM10 Dp <10 µm
0.15
0.6 Deposition Accumulation Mode
0.1
0.4
Deposition
Normalized Concentration (Ctotal-1)dC dlogDp-1
Fine Particles Dp < 2.5 µm
Nanoparticles Dp < 50 nm
0.2
Nuclei Mode Coarse Mode 0.05
0.2
0 1
10
100
1,000
0 10,000
Diameter (nm)
Number
Surface
Mass
Deposition (Alveolar + Tracheo-Bronchial, ICRP 1994)
Fig. 1. Typical Diesel mass and number weighted size distributions shown with alveolar deposition.
regulatory agencies such as the US Environmental Protection Agency (EPA) have adopted mass-based air pollution regulations for particulate matter, other metrics, such as particle number or surface area, may be important in characterizing the physical properties of aerosol related to health effects (McCawley, 1990). Laboratory studies (Andersson and Wedekind, 2001; Booker, 1997; CONCAWE, 1998; Graskow et al., 1998; Greenwood et al., 1996; Hall et al., 1998; McAughey et al., 1996; Maricq et al., 1999a, b) have shown that nanoparticle emissions from SI engines are much more speed- and load-dependent than Diesel engines, and that SI engines emit a higher proportion of smaller particles than do Diesels. Our on-road studies have shown that high speed and load conditions, such as freeway cruise and hard acceleration, produce SI number emissions nearly as high as those produced by Diesel engines. Under less severe conditions, SI emissions are considerably lower (Kittelson et al., 2002, 2003). Measurements from recent model SI vehicles, over the FTP and ECE driving cycles, showed tailpipe particulate matter emissions to be very low (Maricq et al., 1999a). Mass emission rates from a light duty truck ranged from 4.3, during the cold-start phase of the FTP drive cycle, to o0.062 mg km1, during phase 2, for nearly half the vehicles reported. All of the conventional gasoline vehicles in this study emitted o1.24 mg km1 particulate matter, compared to the current 50 mg km1 particulate matter standard. The mean particle size for all vehicles tested ranged from 35 to 65 nm; by mass it ranged from 100 to 300 nm.Minor changes in distribution were observed between vehicles. The number of particles emitted over the transient test conditions ranged from
3 1010 to 7.9 1013 particles phase1. Peak particle emissions occur principally during short periods of heavy acceleration and coincide with peak hydrocarbon, CO, and NOx emissions. The Northern Front Range Air Quality Study (Watson et al., 1998; Cadle et al., 1998) found that vehicle exhaust was the largest PM2.5 total carbon (TC) contributor, constituting about 85% of PM2.5 carbon at sites in the Denver metropolitan area. Sources with emissions similar to light-duty-gasoline vehicles contributed about 60% of PM2.5 TC at urban Denver sites; these contributions were 2.5–3 times the Diesel exhaust contributions. Emissions were particularly high during cold, cold starts and from vehicles identified as ‘‘smokers’’. Still, Diesels contributed the largest share of the ‘‘elemental’’ carbon (EC) at these locations. Uncertainty exists regarding the relative contribution of atmospheric aerosol from SI- and Diesel-exhaust in real-world conditions. Diesels emit more particulate matter per vehicle; but, because SI vehicles, at least in the US, account for most of the vehicles operating onroad, the direct PM emissions from SI engines may be more important (Cadle et al., 1998).
2. Mobile emissions laboratory and instrumentation The University of Minnesota mobile emissions laboratory (MEL) was used to collect gaseous and aerosol data while operating on Minnesota roadways around Minneapolis. The sample air intake was located 69 cm above the highway and 43 cm in front of the MEL to simulate an automobile’s air inlet. Total
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sample-line length was less than 11 m with a flow rate of 400 l min1 through a 10.2 cm diameter tube; the calculated penetration efficiency for 10 nm size particles was greater than 95%. A manifold distributed the sample air to the instruments. The TSI 3071 scanning mobility particle sizer (SMPS) was used to determine the number, surface area, and volume size distributions. The SMPS classified particles from 8 to 300 nm and was operated with a TSI 3025A CPC. A standalone TSI 3025A CPC was used to determine the total number concentration for particles ranging in size from about 3 to 1000 nm (Kesten et al., 1991; Wiedensohler et al., 1990). This CPC has a maximum concentration of 9.99 104 particles cm3 so it was used with a leaky-filter dilutor, which had a dilution ratio of 220:1. The leaky filter dilutor is a passive dilutor, and is dependent upon the instrument flow rate. A glass capillary tube was placed inside an absolute capsule filter to create a leak through the filter. The diameter of the capillary tube determined the dilution ratio. The dilution ratio was established using a polydisperse ammonium sulfate aerosol, both with and without the leaky filter dilutor in line. A nebulizer was used to spray the polydisperse ammonium sulfate aerosol to the particle instruments on a daily basis to check the consistency of the size distributions and number concentrations. The total particle number concentrations measured by the 3025A CPC and calculated from the SMPS were different because the 3025A CPC counts particles as small as 3 nm while the SMPS goes down to only 8 nm. This situation is analogous to PM10 and PM2.5, where the metric being measured is the same but the cutoff size is different. Three gas analyzers were also used: a non-dispersive infrared (NDIR) CO2 analyzer, a NDIR CO analyzer, and a chemiluminescence NOx analyzer. These instruments have a response time of about 1 s; they were used to determine on-road and background ambient gas concentrations. Each instrument was zeroed and spanned daily according to the manufacturers’ instructions and 10 point calibrations were done every 6 months. The quality assurance protocol was developed during the Coordinating Research Council E-43 project and is reported elsewhere (Ayala et al., 2002, 2003).
3. Methods and approach The MEL speed, location, traffic conditions, weather, presence of Diesel traffic, and other pertinent information were recorded while on-road. These logs were electronically transcribed, coded, and associated with the raw data obtained by the instruments. Date and time
11
stamps in the electronically recorded instrument data allowed logged information to be associated with electronically recorded data. Routes with high daily traffic volumes and speeds, representative of the Twin Cities area, comprised the sampling region. Stationary sampling points located upwind or downwind of the roadways, in residential areas, and with low-traffic volumes were sampled to obtain residential background concentrations. The MEL covered about 800 km over 5 days of sampling; a variety of traffic conditions were encountered and sampled. A conscious effort was made to avoid directly sampling exhaust plumes from on-road vehicles. The driver of the MEL was asked to maintain a distance of at least 10 m between the MEL and the vehicle directly in front of the MEL. This distance was established based upon safety and the desire not to impede traffic flow.
4. Results and discussion Fig. 2 shows data covering a 3 h sampling period. The calculated SMPS integrated number concentration tracks with the total CPC number concentration, but the SMPS has a lower concentration. This is consistent with the CPC having a wider counting range, especially on the low-end of the particle size spectrum. Fig. 2 also shows the impact of the interstate highway on surrounding neighborhoods located about 700 m upwind (14:30) and downwind (15:10) of the roadway, where the MEL was parked to collect residential area background data. Concentrations increased dramatically as the MEL got closer to and finally crossed over the interstate highway around 14:45. Fig. 2 and Tables 1 and 2 show the relationship between particle number concentration and MEL speed. Table 1 shows data obtained from the CPC and Table 2 shows data from the SMPS. On-road particle number concentrations ranged from 104 to >106 particles cm3; higher speeds were associated with higher concentrations. The MEL moved with traffic and at a low speed when vehicle density was high; traffic was generally bumper to bumper. At high speeds vehicle density was low, allowing the MEL to move at posted speed levels. The traffic pattern was consistent with a large metropolitan area where rush hours congest main arteries in the morning and late afternoon. Our sampling generally took place between 1 and 5 PM allowing us to experience both free flowing and congested traffic conditions. The average weekday traffic data collected from the Minnesota Department of Transportation showed that the Diesel to SI ratio for specific observation points on the routes taken in this study was about 0.09. We presume that ambient weather conditions may have influenced the number concentrations. This study
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Fig. 2. Strip chart for 15 November 2000.
Table 1 On-road averages by condition and speed Condition
Speed range (km h1)
CO2 (ppm)
CPC (particles cm3)
NOx (ppb)
Avg
Std
Avg
Std
Avg
Std
Median
Diesel No Diesel Diesel No Diesel Diesel No Diesel
>0o32 >0o32 X32p80 X32p80 >80 >80
416 395 437 406 403 396
40 38 50 43 32 25
147 98 271 160 261 129
135 139 250 196 225 133
9.21E+04 5.75E+04 5.85E+05 4.01E+05 1.28E+06 3.74E+05
8.82E+04 2.97E+05 2.35E+06 2.13E+06 3.89E+06 1.80E+06
7.33E+04 1.88E+04 1.88E+05 6.85E+04 4.79E+05 1.12E+05
All on-road
>0
404
38
155
179
4.03E+05
2.03E+06
9.12E+04
was conducted in November 2000, when ambient daily temperatures ranged between 1 C and 13 C, with relative humidity between 40% and 60%. Our results show that colder ambient temperatures contribute to increased nanoparticle formation (Wei et al., 2001; Kittelson et al., 2003). Tables 1 and 2 show data classified by speed and the presence or absence of neighboring Diesel vehicles. When a heavy-duty Diesel passed the MEL or entered the highway in front of the MEL, a sampling window of 60 s was defined, because of the possible impact that the exhaust plume may have had on our measurements. The collected data were coded to reflect that a heavy-duty
vehicle was present within the 60 s window of time. The tables illustrate that Diesel traffic increased the particle number concentration in all speed groups. Table 2 also shows the average integrated number-to-volume ratio ðN=V Þ (Kittelson, 1998) and geometric mean particle size (DGN) derived from the SMPS data. The particle size decreased with increasing speed and did not appear to be affected by the presence of heavy-duty Diesel trucks. The N=V ratio increased with increasing speed and was larger in the presence of heavy-duty Diesel trucks. An N=V ratio >104 indicates the presence of a large nuclei mode (Kittelson, 1998; Kittelson et al., 2002).
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Table 2 Integrated SMPS averages by condition and speed Condition
Speed range (km h1)
Sample (N)
N total (particles cm3)
N=V (part mm3)
DGN (nm)
Avg
Std
Avg
Std
Avg
Std
Diesel No Diesel Diesel No Diesel Diesel No Diesel
>0o32 >0o32 X32p80 X32p80 >80 >80
31 33 32 66 40 111
1.02E+05 4.72E+04 1.46E+05 9.68E+04 1.80E+05 8.27E+04
7.94E+04 5.14E+04 1.00E+05 1.36E+05 2.94E+05 9.90E+04
1.70E+04 1.07E+04 2.18E+04 1.92E+04 3.32E+04 3.07E+04
1.34E+04 1.06E+04 1.37E+04 2.36E+04 3.11E+04 3.72E+04
25 26 20 20 14 16
8 13 7 7 3 5
All on-road
>0
313
1.03E+05
1.46E+05
2.42E+04
2.88E+04
19
8
Table 3 Average residential area data by location Location
CPC (particles cm3) Avg
Std
Median
SMPS N/V (part mm3)
SMPS DGN (nm)
CO2 (ppm)
NOx (ppb)
Avg
Std
Avg
Std
Avg
Std
Avg
Std
9.16E+03
23
9
370
14
31
49
1.17E+03
30
8
364
6
15
27
10 m from 3.80E+04 3.57E+04 2.45E+04 8.05E+03 highway 700 m from 9.44E+03 8.70E+03 8.37E+03 3.17E+03 highway
Table 3 shows the average CO2, NOx, and CPC particle concentrations and the average SMPS N=V and DGN for data collected in residential areas 10 and 700 m from the highway. Particle number, CO2, and NOx concentrations decreased with increasing distance from the highway. The N=V also decreased, but the DGN increased indicating that the nuclei mode was less pronounced farther away from the highway. The N=V ratios shown in Tables 2 and 3 give the number of particles formed per-unit-volume of emitted particulate matter (particles mm3). Since particle mass is proportional to particle volume, the N=V ratio gives a measure of the particles formed per unit-mass emitted. For spherical particles with an effective density of 1 g cm3, an N=V ratio of 1 particle mm3 corresponds to 1012 particles g1. On-road samples are more likely to have N=V >104, as shown in Table 2, while samples collected away from major highways nearly always have an N=V o104, as shown in Table 3. The higher the vehicle speed the greater the N=V ratio and the smaller the particle size. Park et al. (2003) have shown that Diesel particles exhibit a fractal like behavior where the effective density decreases with increasing particle size. This decrease ranges from about 1.1 to 0.3 g cm3 as particle size increases from 50 to 300 nm. Schaberg et al. (2002) measured the ratio of filter mass to SMPS volume for two different Diesel engines and found the effective density ranged from 0.74 to 0.87. These results are for Diesel engines and, although similar work has not been done for SI engine particles, we have assumed an
effective density of 1.0 for our calculations. Our assumption is supported by the fact that the nuclei mode for a Diesel engine is mainly composed of volatile material from lubrication oil, which has a density of 0.9 g cm3 (Sakurai et al., 2003a, b). 4.1. SMPS size distributions SMPS data are shown as average size distributions grouped by condition. The number (or volume) concentration of particles, per log-diameter interval of the size distribution (dN d log D1 or dV d log D1 p p ), are shown on the y-axis; N is the particle number (or V is the volume) and Dp is the particle diameter. When data are plotted in this way, the area under the curve gives the number (or volume) concentration of particles in that size range, given that the y-axis is on a linear scale and the x-axis is on a log scale. Fig. 3 shows the average number and volume size distributions for the residential and on-road samples with the standard deviation of the mean (SDOM). There is a significant difference between the nuclei modes of the three number size distributions; as the distance from the highway increases, the magnitude of the nuclei mode decreases. There is less difference in the volume distributions. Since the SMPS was operated to size particles from 8 to 300 nm, the complete accumulation mode (where most of the volume resides) was not captured. This is indicated by the upward trend of the volume-size distributions.
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14 2.5E+05
2.0E+01
Error bars are standard deviation of the mean.
1.8E+01 1.6E+01 1.4E+01
1.5E+05
1.2E+01 1.0E+01 8.0E+00
1.0E+05
6.0E+00 5.0E+04
dV dlogDp-1, µm3 cm-3
dN dlogDp-1, particles cm-3
2.0E+05
4.0E+00 2.0E+00
0.0E+00 1
10
0.0E+00 1000
100
Midpoint diameter, nm Onroad (Number)
Residential (Number) 10 m from Hwy 62
Residential (Number) 700 m from I-494
On road (Volume)
Residential (Volume) 10 m from Hwy 62
Resdential (V) 700 m from I-494
Fig. 3. Average number and volume SMPS size distributions grouped by location.
Number, 151 scans, Speed >= 80 Number, 131 scans, Speed >=8<=48 Volume, 51 scans, Speed > 48 < 80
3.5E+05
Number, 51 scans, Speed >48 < 80 Volume, 151 scans, Speed >= 80 Volume, 131 scans, Speed >=8<=48
25
Error bars are standard deviation of the mean 3.0E+05
2.5E+05 15
2.0E+05
1.5E+05
10
1.0E+05 5
5.0E+04
0.0E+00 1
10
100
Midpoint diameter, nm Fig. 4. On-highway SMPS number and volume distributions grouped by speed.
0 1000
dV dlogDp-1, µm3 cm-3
dN dlogDp-1, particles cm-3
20
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on particle number than volume. The increase in particle volume or mass, at lower vehicle speeds, was consistent with expectations of higher concentrations under congested conditions. Less variation was observed in particle volume compared to particle number size distributions.
Fig. 4 shows the relationship between speed, aerosol number, and volume size distributions. The magnitude of the nuclei mode increases with vehicle speed, while particle size decreases. The error bars on the nuclei mode portion of the size distributions do not overlap; this suggests that the increase in magnitude is statistically significant. DGN and N/V data from Table 2 show the magnitude of the particle size decrease, which is about 10 nm, and the corresponding increase in the N/V ratio. The magnitude of the increase in particle number concentration is even greater for the CPC data shown in Table 1. At high vehicular speeds, particulate matter emissions increase because of higher engine load, exhaust temperature, and exhaust flow. This observation has been reported in other studies (Zhu et al., 2002a, b), but in these studies samples were collected near the roadway, as opposed to traveling on-road. Some of the particles observed at higher speeds are likely due to transient release of particle-associated materials stored in exhaust systems during lower speed operation (Hall and Dickens, 2000). Diesel engine particle emissions are relatively independent of speed and load (Kittelson et al., 2002); conversely, SI engine emissions are strongly dependent upon speed and load, as previously discussed. Slower speeds produced larger particles and larger aerosol volumes, as illustrated by the volume distributions in Fig. 4. Particle volume is a surrogate measure of particle mass and, unlike particle number, it is not influenced by coagulation and is less influenced by nucleation. Furthermore, particles added by the on-road fleet are much smaller than residential background particles, therefore, they have a much stronger influence
4.2. Fuel-specific particulate matter emissions Emission-factor estimates can be derived from our data. The estimates in Tables 4 and 5 were calculated for the 10th and 15th of November and are representative of a predominantly gasoline-vehicle dominated scenario. As mentioned previously, the Diesel to SI ratio for specific observation points on the routes taken in this study was about 0.09 during weekday periods. The highway concentrations were obtained from data collected on a section of roadway parallel to and between the residential locations; residential location data were used to correct the on-road data for the background contribution. The calculation was based on the ratio of particles to carbon added by the fleet. It was assumed that all of the carbon added by highway traffic was in the form of either CO2 or CO. This was a reasonable assumption since gasoline engine combustion efficiencies are better than 90% (Heywood, 1988); thus, more than 90% of the carbon in the fuel was converted to CO2 and most of the incompletely burned carbon is emitted as CO. The latter is illustrated by the US Federal tailpipe emission standards for new vehicles that allow emissions of more than 10 times the amount of CO compared to non-methane organic gases (2.112 versus 0.255 g km1). The particles per mass of carbon may be
Table 4 On-road particle number emission estimates derived from the CPC and SMPS Date
11/10/00 11/10/00 11/10/00 11/15/00
Time From
To
15:38 15:51 16:39 15:09
15:51 16:39 16:48 15:29
Location
CPC (part km1)
SMPS (part km1)
CPC (part kg1 fuel)
SMPS (part kg1 fuel)
I-494S I-494S traffic jam I-494E I-494S
1.93E+14
8.70E+13
2.92E+14 9.94E+14
2.73E+14 2.24E+14
2.2E+15 2.8E+15 3.4E+15 1.1E+16
1.0E+15 3.0E+15 3.1E+15 2.6E+15
Table 5 On-road fuel specific mass emission estimates derived from the SMPS Date
11/10/00 11/10/00 11/10/00 11/15/00
Time
Location
From
To
15:38 15:51 16:39 15:09
15:51 16:39 16:48 15:29
I-494 I-494 I-494 I-494
going going going going
SMPS (mg km1) S S traffic jam E S
7.8 11.6 6.2
SMPS (mg kg1 fuel) 89 325 132 71
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converted to particles per mass of fuel if the carbon content of the fuel is known. The carbon content of gasoline ranges from 0.83 to 0.86 as a mass fraction (Chevron, 1996); for our calculations, 0.85 was used. These calculations yield the fuel-specific emissions in particles kg1 of fuel. Emissions expressed in this form are especially useful if total fuel consumption by a vehicle fleet is known. These results may also be used to estimate particle emissions in particles km1, assuming vehicle fleet average fuel consumption. An average fuel consumption rate of 8.5 km l1 and an average fuel density of 0.74 kg l1 for gasoline were assumed (Owen and Coley, 1995). Table 4 shows the fuel-specific particle emissions derived from our data corrected for the background contribution. Estimates based upon the CPC range from 1.9–9.9 1014 part km1 and 2.2 1015 to 1.1 1016 part kg1 fuel. The particle km1 estimates shown in Table 4 are higher for the CPC than for the SMPS. This is likely due to the difference in the lower particle size detection limits of the instruments (3 versus 8 nm); and it suggests that a large number of particles lie between 3 and 8 nm. Similar observations have been reported elsewhere (Kittelson et al., 2001; Shi et al., 2001). The SMPS provides estimates of the number, surface area, and volume particle size distributions, each corrected for any background contribution. The volume distribution is equivalent to the mass distribution if particles are spherical and a constant particle density is assumed. For the reasons described above a density of 1 g cm3 has been assumed. The mass fuel-specific emission estimates derived from the SMPS are shown in Table 5 and range from 6.2 to 11.2 mg km1 and 71 to 325 mg kg1 of fuel. Results reported here are not dissimilar from data reported in other technical literature. In a European study (Rickeard et al., 1996) that evaluated Diesel and SI emissions in the laboratory, it was reported that, at 50 km h1 emissions from Diesel vehicles varied con, siderably. The lowest emitting vehicle produced less than half the particle emissions of the highest emitting vehicle (5.0 1013 compared to 1.24 1014 particles km1); SI vehicles produced much lower emissions. However, at high speed (120 km h1), the pattern was different and both types of vehicles emitted large quantities of particles (1.2 1014 to 1.8 1014 particles km1). Mass emission estimates from 24 properly functioning and six high CO emitting light-duty SI vehicles derived from the US Federal Test Procedure hot start Unified Cycle were reported for a study done in Denver, CO (Cadle et al., 2001). The range of emissions varied from 2.2 to 7.9 mg km1 for the properly functioning vehicles and 14.9 to 32.3 mg km1 for the high emitters. In an Australian study, the average emission factor for vehicles was calculated to be 4.5 1014 particles km1 vehicle1 with a standard deviation of approxi-
mately 10–15% (Gramotnev et al., 2003). In another Australian study, the average emission factor obtained from a box model and roadside measurements was 1.75 1014 particles km1 vehicle1, with a standard error of 67.6% (Jamriska and Morawska, 2001). The authors compared their estimates to those obtained from chassis dynamometer experiments and suggested that laboratory studies may underpredict realworld emission estimates. Laboratory studies evaluate a limited number of vehicles and test conditions and may not include high emitting conditions such as acceleration or evaluation of high emitting vehicles. A variety of light-duty Diesel and SI automobiles were evaluated on a chassis dynamometer using the electrical low-pressure impactor (ELPI) (F.arnlund et al., 2001). The ELPI is capable of sizing particles from 10 nm to 10 mm, however, its calibration below 30 nm is subject to question. Particle km1 emissions for gasoline-fueled, naturally aspirated SI vehicles ranged from about 3.7 1011 to 5.6 1013 when evaluated on a moderate driving cycle. Still, the worst naturally aspirated SI engine emitted 1000 times more particles than the best engine of the same type. Particle km1 emissions for turbocharged SI engines were found to emit between 8.7 1012 and 8.1 1013 particles km1, with less variation between engines. Emissions from Diesel engines ranged from about 6.8 1013 to 3.1 1014 particle km1. The number emissions that we report are on the high end of those reported in the literature. This is likely due to several factors. The 3025A CPC used in this study can detect smaller particles than the instruments used in other studies. Furthermore, the relatively cold ambient temperatures and on-road dilution conditions favor nanoparticle formation (Kittelson et al., 2002); studies conducted on chassis dynamometers, using a constant volume sampling dilution tunnel, may suppress nanoparticle formation (Kittelson et al., 2002, 2003).
5. Conclusion This study differs from previous studies because data were collected on-road, under varying traffic conditions, as opposed to collecting data from stationary sampling points near the highway. On-road size distributions and fuel-specific emissions corrected for background were obtained. Data were corrected using background concentrations measured in residential areas located upwind of the highway. When sampling in residential areas, data were collected upwind and downwind from the highway to determine the impact of the highway on residential air quality and to determine the effects of aerosol aging on the plume aerosol concentration and size distribution.
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Particulate matter concentrations on-road ranged between 104 and 106 particles cm3, with the majority of the particles being less than 50 nm in diameter. An association between the on-road speed of the MEL and particle number concentration and particle size was observed: The higher the speed, the greater the particle concentration, and the smaller the particle size. This is a reasonable finding because at high vehicular speeds, particulate number emissions, especially from SI engines, increase with increased engine load, exhaust temperatures and exhaust flow. Diesel traffic was observed to further increase particle number concentrations. Measurements made in traffic jams, with speeds o32 km h1 showed lower concentrations and larger particles. Less variation was observed in particle volume compared to particle number size distributions. In our tests, volume was influenced more by background concentrations than by local on-road conditions. Measurements made in residential areas demonstrated that aerosol concentrations increased as the distance from the roadway decreased. The size distributions from these areas were similar to on-road aerosol with high concentrations of very small (o20 nm) particles. Much lower concentrations and much larger particles were observed in residential areas located 500–700 m from the highway. Fuel specific and particle km1 emission rates were estimated from data collected on two different days; the estimates were similar to results from other studies. Estimates based upon the CPC range from 1.9– 9.9 1014 part km1 and 2.2 1015 to 1.1 1016 part kg1 fuel. Our results are limited because the study was conducted over a limited range of ambient temperature (1–13 C). Colder ambient temperatures increase particle number concentrations by favoring nucleation. To determine the full range of emission factors, data need to be collected for a wide variety of environmental and engine operating conditions under real-world dilution conditions.
Acknowledgements This work was funded in part by the University of Minnesota Center for Transportation Studies under a contract from the Minnesota Department of Transportation (MnDOT). The Coordinating Research Council (CRC) and the sponsors of the CRC E-43 project provided funding for the MEL and instrumentation. The Volvo Truck Corporation provided the tractor platform for the MEL. We would like to thank Mr. Bill Bunde and Ms. Marilyn Jordahl-Larson from MnDOT and Mr. Marcus Drayton from the University of Minnesota for their assistance in making this project possible.
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