2004 Progress Report: Relationship Between Ultrafine Particle Size Distribution and Distance From Highways

EPA Grant Number: R827352C006
Subproject: this is subproject number 006 , established and managed by the Center Director under grant R827352
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Southern California Particle Center and Supersite
Center Director: Froines, John R.
Title: Relationship Between Ultrafine Particle Size Distribution and Distance From Highways
Investigators: Hinds, William C. , Sioutas, Constantinos , Zhu, Yifang
Institution: University of California - Los Angeles , University of Southern California
EPA Project Officer: Hunt, Sherri
Project Period: June 1, 1999 through May 31, 2005 (Extended to May 31, 2006)
Project Period Covered by this Report: June 1, 2003 through May 31, 2004
RFA: Airborne Particulate Matter (PM) Centers (1999) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air

Objective:

The objective of this research is to characterize ultrafine particles in the vicinity of major highways, particularly as they are transported downwind from the freeway. Field measurements at various distances just downwind of several major freeways were conducted in the Los Angeles area in an effort to develop a theoretical model to predict ultrafine particle concentration and size distribution close to freeways.

The following sections summarize research objectives, progress accomplishments, and conclusions for each individual field campaign conducted during the reporting period.

Progress Summary:

Ultrafine Particles Near 405 Freeway

Objective: To systematically evaluate ultrafine particles in the vicinity of a gasoline vehicle dominated freeway, Interstate 405 Freeway, particularly as they are transported downwind from the freeway.

Accomplishment: It is now well established that increases in the concentration of fine particulate matter in urban areas are associated with increases in morbidity and mortality. It is not known what properties of fine particulate matter cause these effects, but one candidate is ultrafine particles. These are particles less than 100 nm or 0.1 μm in size and are found near combustion sources, such as motor vehicles. As a first step to modeling the concentration and size distribution of ultrafine particles in the vicinity of freeways, we have made detailed measurements of ultrafine particles near the 405 Freeway, one of the busiest in the country. During the sampling period, traffic density ranged from 140 to 250 vehicles/min passing the sampling site (total for both directions). Traffic was primarily dominated by gasoline-powered cars and light trucks with less than 5% of vehicles being heavy-duty diesel trucks.

For most of the sampling time, the wind was coming directly from the freeway towards the sampling road with a speed of 1 to 2 m/s. The consistency of observed wind direction and speed is a result of a reliable sea breeze in the sampling area. Consistency of the wind is important for this field experiment, because it allows data from different days to be averaged together.

Total Particle Number Concentrations

In this study, we found wind direction and speed, played an important role in determining the characteristics of ultrafine particles near Freeway 405. Figure 1 shows total particle number concentrations as measured by the control condensation particle counter (CPC), located 30 m downwind of the freeway versus wind speed. Data, at 30 m, given by Hitchins, et al. (2000) were also included for comparison (1). It can be seen that total particle number concentration measurements near Freeway 405 are in general 2-3 times greater than those observed by Hitchins, et al., at Tingalpa, Australia. This is mainly due to the much heavier traffic density on Freeway 405. Although, the absolute particle number concentrations are quite different in these two studies, the relative particle number concentration as function of wind speed are quite similar. This indicates that atmospheric dilution of ultrafine particles by the wind is comparable for both cases.

Figure 2 shows the change in measured particle number concentration and the number of cars passing by the sampling site during those sampling periods when the wind was from the southwest. As shown in this figure, normalized particle number concentration tracked the traffic density very well indicating that traffic is the major contributor to fine and ultrafine particles. A traffic slowdown on the north-bound side of Freeway 405 usually developed around 1:30 pm on weekdays as indicated by the sharp drop of the curve. During this traffic slowdown, the average vehicle speed is usually less than 5 mph. The control CPC’s reading during that time period was observed to be much lower than normal, indicating that fewer ultrafine particles are produced during such traffic slow down.

Particle number concentration and size distribution in the size range from 7 nm to 220 nm were measured by a CPC and a scanning mobility particle spectrometer (SMPS). Measurements were taken at 30 m, 60 m, 90 m, 150 m, and 300 m downwind and 300 m upwind from Interstate highway 405 at the Los Angeles National Cemetery. At each sampling point, the concentration of carbon monoxide (CO), black carbon (BC) and particle mass were also measured by a Dasibi CO monitor, an Aethalometer and a DataRam, respectively.

The average concentrations of CO, black carbon, particle number and mass concentration at 30 m was in the range of 1.7 to 2.2 ppm, 3.4 to 10.0 μg/m3, 1.3 × 105 to 2.0 × 105 /cm3 and 30.2 to 64.6 μg/m3, respectively.

As shown in Figures 3 and 4, 30 meters downwind from the freeway, three distinct modes were observed with geometric mean diameters of 12.6 nm, 27.3 nm and 65.3 nm, respectively. The smallest mode, with a peak concentration of 1.6×105 #/cm3, disappeared at distances greater than 90 m from the freeway. The number concentrations for smaller particles, dp < 50 nm, dropped significantly with increasing distances from the freeway, but for larger ones, dp > 100 nm, number concentrations decreased only slightly. Ultrafine particle concentration measured at 300 m downwind of the freeway was indistinguishable from upwind background concentration.

Ultrafine Particle Size Distributions

Figure 5 shows the decay of normalized total particle number and volume concentration, in the size range of 7 nm to 220 nm, respectively, with distance along the wind direction from the freeway. Each data point in the figure represents an averaged value for all measurements with the same wind directions. The solid line was the best fitting exponential decay curve. The primary cause for the decay in concentration is atmospheric dispersion. Since coagulation will only decrease the total particle number concentration, not the volume, if coagulation is occurring then, total number concentration will decay faster than total volume concentration, which appears to be the case as shown in Figures 5a and b.

Figure 5. Ultrafine Particle Concentration Vs. Distance from Freeway 405

Figure 5. Ultrafine Particle Concentration Vs. Distance from Freeway 405

To make this freeway study more comprehensive, the concentrations of CO, black carbon, particle mass, and particle number were also measured at increasing distance from the freeway. Figure 6 shows the decay curves for relative CO, black carbon, total particle number and mass concentration. The mass concentration decreased only by a few percent throughout the measured range, while, CO, black carbon and particle number concentration decreased about 60% in the first 100 m and then leveled off somewhat after 150 m. In fact, CO, black carbon and particle number concentrations tracked each other extremely well. This observed result confirmed the common assumption that vehicular exhaust is the major source for CO, black carbon and ultrafine particles near a busy freeway. In addition, it suggests that the decreasing characteristics of any of these three pollutants could be used interchangeably to estimate the concentration of the other two pollutants near freeways.

Figure 6. Relative Mass, Number, Black Carbon, CO Concentration Near the 405 Freeway

Figure 6. Relative Mass, Number, Black Carbon, CO Concentration Near the 405 Freeway

Conclusions: Wind speed and direction are important in determining the characteristic of ultrafine particles near freeways. The stronger the wind, the lower the total particle number concentration. Total particle number concentration is directly related to traffic density and decreases significantly during a traffic slowdown. The average concentrations of CO, black carbon, particle number and mass concentration at 30 m was in the range of 1.7 to 2.2 ppm, 3.4 to 10.0 μg/m3, 1.3 × 105 to 2.0 × 105 /cm3 and 30.2 to 64.6 μg/m3, respectively. CO, black carbon and particle number concentration track each other extremely well with distance away from a freeway. Exponential decay was found to be a good estimator for the decrease of total particle number concentration with distance along the wind direction.

Ultrafine Particles Near 710 Freeway

Objective: To systematically evaluate ultrafine particles in the vicinity of the 710 Freeway in the Los Angeles basin, a freeway where more than 25% of vehicles are heavy-duty diesel trucks. The results from the current study are compared to these by Zhu, et al. (2002a) which were obtained near the 405 Freeway.

Accomplishments: Figure 1 compares the traffic volume on both the 405 and the 710 freeways. Error bars represent one standard deviation. It was found that the 710 Freeway has about 7 times as many diesel vehicles and 70% of gasoline vehicles as the 405 Freeway. The total vehicle numbers on both freeways are quite similar: 12,180/hour for the 710 Freeway versus 13,900/hour for the 405 Freeway. Similar measurements were taken at 17 m, 20 m, 30 m, 90 m, 150 m, and 300 m downwind and 200 m upwind from Interstate highway 710 as was done near the 405 Freeway.

As shown in Figure 2, ultrafine particle size distribution changed markedly and its number concentration dropped dramatically with increasing distance. At the nearest sampling location, 17 m downwind from the center of the freeway, the dominant mode was around 10 nm with a modal concentration of more than 3.2×105 /cm3. This mode remained at 10 nm for the second sampling location, 20 m downwind from the freeway, but its concentration dropped to 2.4×105 /cm3. In general number concentrations for smaller particles, dp < 50 nm, dropped significantly with increasing distances from the freeway, but for larger ones, dp > 100 nm, number concentrations decreased only slightly. These results are in excellent agreement with what Zhu, et al. (2002a) reported for a freeway impacted mostly by gasoline vehicles, which suggests that coagulation may be important for the smallest ultrafine particles.

Traffic Volume Comparison/Ultrafine Particle Size Distribution

The range of average concentration of CO, black carbon and total particle number concentration at 17 m was 1.9 to 2.6 ppm, 20.3 to 24.8 μg/m3, 1.8 × 105 to 3.5 × 105 /cm3, respectively.

Figure 3 was prepared in the same way as Zhu, et al. (2002a) showing the decay of ultrafine particle number concentrations in four size ranges. The general trends are in excellent agreement with what Zhu, et al. (2002a) observed and can be explained by particles, in smaller size ranges, coagulating with these particles to increase their size.

Figure 4 compares the ultrafine particle size distributions at 30 m downwind from the 710 and the 405 freeways. In Figure 4, both size distributions have three distinct modes. The concentration for the first mode, in between 10 to 20 nm, is slightly higher near the 405 Freeway. This mode is similar to that previously reported for direct laboratory measurement of gasoline vehicle emissions. The concentration for the second mode, around 30 nm, is about 30% higher near the 710 Freeway than that near the 405 Freeway. This is likely due to the much higher diesel emissions on the 710 Freeway.

Ultrafine Particle Number Concentration/Comparison of Ultrafine Particle Size Distribution

Figures 5a to 5c were prepared by comparing the decay characteristic of CO, black carbon and particle number concentrations near the 405, gasoline vehicle dominated, and the 710, diesel vehicle dominated, freeways. It can be seen, in general, all three pollutants decay at a similar rate near both freeways. This implies that atmospheric dilution plays a comparable role in both studies. The discrepancies of the curves were mainly due to the different traffic fleet compositions on these two freeways. The 710 Freeway has more than 25% heavy diesel trucks while the 405 Freeway has less than 5%. It is well known that diesel engines emit less carbon monoxide and more black carbon comparing to spark ignition engines. Figure 5a shows that the concentration of CO near the 710 Freeway is generally half of that near the 405 Freeway. By comparison, Figure 5b shows the black carbon concentration near a diesel vehicle dominated freeway is more than three times greater than that near a gasoline vehicle dominated freeway. As shown in Figure 5c, the total particle number concentration close to the 405 Freeway is somewhat higher than that near the 710 Freeway, but drops faster with downwind distance.

CO Concentration

Black Carbon Concentration

Particle Number Concentration
Particle Number Concentration

Figure 5. Decay Curves of (a) CO (b) Black Carbon and (c) Particle Number Concentration near the 405 and 710 Freeways

Conclusions: Wind speed and direction are important in determining the characteristic of ultrafine particles near freeways. The average concentrations of CO, black carbon and particle number concentration at 17 m was 1.9 to 2.6 ppm, 20.3 to 24.8 μg/m3, 1.8 × 105 to 3.5 × 105 /cm3, respectively. Relative concentration of CO, black carbon and particle number tracked each other well as one moves away from the freeway in the downwind direction. Exponential decay was found to be a good estimator for the decrease of these three pollutants’ concentration with distance along the wind direction starting from the edge of the freeway.

Ultrafine Particles Near Freeways in Winter

Objective: Previously we have conducted systematic measurements of the concentration and size distribution of ultrafine particles in the vicinity of Interstate 405 (mostly gasoline traffic) and Interstate 710 (large proportion of heavy-duty diesel traffic) in Los Angeles during the summer of 2001(2, 3). The present study compares these measurements with those made at the same locations in the winter of 2001-2002.

Accomplishments: Figures 1a and b compare traffic volume in summer and winter on the 405 and 710 freeways. Error bars indicate one standard deviation. Freeway 405 passes through West Los Angeles and is considered one of the busiest freeways in the United States. More than 95% of vehicles on the 405 Freeway were gasoline-powered (3). The average traffic volume per hour during the winter sampling period on the 405 Freeway was: 13,100 cars, 360 light trucks, and 540 heavy-duty trucks for a total of 14,000 vehicles. These values are not statistically different from those reported by Zhu, et al. (2002a) in summer. Freeway 710, passing through the City of Downey, is considered a major truck shipping route in Southern California with about 25-30% of vehicles being heavy-duty diesel trucks (2). The average traffic volume per hour during the winter sampling period on the 710 Freeway was: 8,450 cars, 840 light trucks, and 2,780 heavy-duty trucks, for a total of 12,000 vehicles. These values are also not statistically different from those reported by Zhu, et al. (2002b) in summer near the 710 Freeway.

Figure 1. Traffic Volume Comparison in Summer and Winter for the (a) 405 Freeway and (b) 710 Freeway.

Figure 1. Traffic Volume Comparison in Summer and Winter for the (a) 405 Freeway and (b) 710 Freeway. Bars indicate one standard deviation.

Table 1 summarizes the mean and standard deviation for meteorological sampling conditions, total vehicles, and control CPC readings in summer and winter. Since the raw data did not meet the normality criteria for a t-test, the non-parametric Mann-Whitney rank sum test was used to compare sampling data for summer and winter. Test results show that the traffic density near the 405 and 710 freeways are not statistically different between summer and winter, while, temperature, relative humidity (RH), wind speed, solar zenith angle and CPC readings are all significantly different with P values less than 0.001. The temperature and RH for the 405 and 710 freeways in winter were not significantly different.

Table 1. Comparison of Sampling Conditions in Summer and Winter

Comparison of Sampling Conditions in Summer and Winter
Comparison of Sampling Conditions in Summer and Winter

Figures 2a and b show the ultrafine particle size distribution at different sampling locations near the 405 Freeway in summer and winter, respectively. The horizontal axis represents particle size on a logarithmic scale, while the vertical axis represents normalized particle number concentration. The normalized ultrafine particle size distributions, in the size range of 6-220 nm at each distance from the freeway were averaged for all sampling dates (3). As shown in Figure 2a, three distinct modes were observed during the summer season, 30-m downwind from the freeway. The smallest mode, around 13 nm, shifted to a larger modal value of 16 nm, and the modal number concentration dropped to one third of the maximum concentration at 60 m downwind. Furthermore, this mode was not observed at greater downwind distances. Ultrafine particle concentration measured at 300 m downwind of the freeway was comparable to what was measured at the background location 300 m upwind of the freeway. The maximum number concentration that was observed near the freeway was about 25 times greater than that for the background location in summer (3). These results indicate that the freeway is a very strong local source of ultrafine particles.

Figure 2. Ultrafine Particle Size Distribution at Different Sampling Locations Near the 405 Freeway in (a) Summer and (b) Winter.

Figure 2. Ultrafine Particle Size Distribution at Different Sampling Locations Near the 405 Freeway in (a) Summer and (b) Winter.

Figure 2b illustrates a different situation in the winter season. Only one dominant particle mode was observed for all sampling locations. At 30 m downwind from the 405 Freeway, this mode occurred around 7 nm, rather than the 13 nm found in summer, with a modal number concentration of 1.8×105 /cm3. Contrary to the summer season, this mode also persisted at distances up to 300 m downwind of the freeway and did not shift to larger size range. Furthermore, although the modal concentration decreased significantly with increasing distance, it did not drop as fast as it did in summer. While this mode was not observed at downwind distances greater than 60 m in summer, it was observed at all sampling locations in winter. It is also of particular note that ultrafine particle concentrations measured at 300 m downwind of the 405 Freeway were still distinguishable from background measurement in winter. These results suggest that the effect of atmospheric dilution is weaker in winter.

Figures 3a and b were prepared in the same way as Figures 2a and b, showing the ultrafine particle size distribution at different sampling locations near the 710 Freeway in summer and winter. For the predominant mode (diameter < 10 nm), similar trends were observed. This mode shifted to a larger size range and disappeared after 90 m from the 710 Freeway in summer. By contrast, in wintertime, this mode persisted up to 150 m downwind from the freeway. The decay in modal concentration with distance from the freeway was slower than that observed during the summer experiments. Contrary to what was observed near the 405 Freeway, a second mode around 20 to 30 nm appeared in winter near the 710 Freeway. This is presumed to be due to the much higher diesel emissions on the 710 Freeway. As shown in Figures 1a-b, heavy-duty diesel trucks on the 710 Freeway represent more than 25% of traffic while on the 405 Freeway they represent less than 5%, Zhu, et al. (2002a,b). Average particulate matter emission rates for heavy-duty diesel trucks are about 0.4 g/mi and for passenger cars are about 0.08 g/mi. This gives about 68% of PM emission from heavy-duty diesel trucks on the 710 Freeway. Similar to the predominant mode, the second mode remained around 20 to 30 nm for all sampling locations and did not shift to a larger size significantly. Its modal concentration decreased more slowly in winter than in summer. These results are in excellent agreement with what has been observed near the 405 Freeway and can be explained by weaker atmospheric dilution in the winter season.

Figure 3. Ultrafine Particle Size Distribution at Different Sampling Locations Near the 710 Freeway in (a) Summer and (b) Winter.

Figure 3. Ultrafine Particle Size Distribution at Different Sampling Locations Near the 710 Freeway in (a) Summer and (b) Winter.

It is also noted that for lower ambient temperature, there is a greater particle number concentration in the particle size range of 6 to 25 nm and a smaller number concentration in the 50-200 nm particle size range. This results in a greater total particle number concentration in winter as measured by the CPC, see Table 1. Although number concentration is lower in summer, the greater number of larger particles (50-200 nm range) gives a greater total surface area concentration in summer. In winter, the total particle number concentration is approximately 20% higher than it is in summer (Table 1). It is also observed that the decay rate of CO and BC are slightly greater in summer than in winter for both freeways suggesting a stronger atmospheric dilution effect in summer.

Conclusions: In summary, a slower aging process for freshly emitted particulates from vehicles was observed in winter. Particle number concentration in the size range of 6-12 nm is significantly higher in winter than in summer. The associated modal concentration in that size range decreased at a slower rate in winter than in summer. The mode did not shift significantly to a larger size in winter as it did in summer. The surface area concentrations in the size range of 6-220 nm are consistently higher in summer for all sampling locations.

Ultrafine Particles near 405 Freeway at Nighttime

Objective: By comparing the nighttime measurement results with the studies at the 405 Freeway during daytime in summer (5) and winter (6), we would like to investigate the degree to which differences in atmospheric conditions, such as temperature, humidity and atmospheric mixing affect the characteristics of ultrafine particles.

Accomplishment: Figure 1 shows the spatial profile for total particle number concentration measured by the CPC. Data from the previously published daytime study were summarized in Figure 1a and indicated as open circles (7). In Figure 1b, solid circles represent mean particle number concentrations measured at different distances from the 405 Freeway at night. Error bars represent one standard deviation of measured particle number concentration. The daytime and nighttime dominant wind directions were also included in the figure. The daytime downwind side of the 405 Freeway becomes the upwind side and the daytime upwind side becomes the downwind side at night. The diurnal variations in wind direction suggest a simple distance weighted exposure model is problematic and will lead to an order of magnitude exposure misclassification when the wind switches direction.

No significant concentration gradient was observed for particle number concentrations on the eastern (upwind) side of the 405 Freeway at night. This is consistent with a previous study conducted by researchers in Australia where no trend was observed when the wind was blowing from the sampling location to the roadway (1). Particle number concentrations decreased at a slower rate downwind of the freeway at night than during the daytime. This is mainly due to the lower wind speed and less atmospheric dispersion and dilution effect at night. The number concentration at 300 m dropped only to approximately 60% of its original value (at 30 m). To see how far it takes for the downwind particle number concentration to drop to upwind background levels, we extended our downwind measurements to 500 m on two nights. In contrast to what happened during daytime, where particle number concentrations at 300 m downwind of the freeway was comparable to what was measured at 300 m upwind of the freeway, nighttime downwind particle number concentrations at 500 m were still significantly greater than those measured at the upwind site. This implies that freeway emissions have a much broader effect on local air quality at night. The daytime upwind background particle number concentrations were higher than those at night. This may be due to the contribution from nearby vehicle emissions on local streets during the daytime. Although traffic volume on the 405 Freeway at night was only about 25% of the daytime traffic density, the measured particle number concentration at 30 m downwind of the freeway at night was about 80% of the daytime value. Besides a weaker atmospheric dispersion at night, it also suggests a higher emission factor in terms of particle number concentration per vehicle at night.

Figure 1. Spatial Variations in Total Particle Number Concentration Near Freeway 405 (a) During the Day and (b) at Night.

Figure 1. Spatial Variations in Total Particle Number Concentration Near Freeway 405 (a) During the Day and (b) at Night.

It was noted in discussion of Figure 1 that nighttime traffic volume was reduced to 25% of daytime volumes, but it generated about 80% of the daytime particle number concentration. Lower wind speed and weaker atmospheric dilution alone could not explain this observed discrepancy because carbon monoxide (CO) concentrations at 30 m downwind of the freeway at night (0.5 ppm) was about 25% of what we observed during the daytime (2.0 ppm). CO concentrations at further downwind and upwind locations are constantly below the detection limit of Q-Trak (0.1 ppm). Besides the dilution effect, the lower temperature and higher relative humidity at night may also contribute to this observed discrepancy.

Temperature and relative humidity were closely related with a negative correlation (correlation coefficient R2 = 0.78). It is therefore not immediately clear whether humidity alone, without a change in ambient temperature, had an effect on particle number concentration and size distribution. To investigate this question, size distributions at the site 30 m downwind of the freeway were averaged into groups with similar temperature and humidity conditions. Figure 2 shows these averaged size distributions. The number size distribution with the lowest concentrations corresponds to the highest temperatures and lowest relative humidities (average temperature of 15.3°C ± standard deviation of 1.2°C, average relative humidity of 25% ± 2%, group denoted low RH, high T). Intermediate relative humidities occurred at a wider temperature range that was divided at 10°C into two groups: one, denoted med RH, med T, with average temperature of 11.4°C (± 1.0°C) for which relative humidity averaged at 38% (± 3%); the other, denoted med RH, low T, with averages of 8.4°C (± 0.9°C) and 53% (± 6%), respectively. The highest humidities (> 70%) occurred at similar temperature ranges, therefore, size distributions were again averaged separately for temperatures above and below 10 °C ambient temperature in two groups: high RH, med T with averages of 11.2°C (± 1.0°C) and 81% (± 5%); and high RH, low T with 9.4°C (± 0.3°C) and 86% (± 2%). Figure 2 clearly illustrates that number concentration increased with both decreasing temperature and increasing humidity. The grouping in different temperature and humidity conditions seems to indicate that humidity changes encountered during our study had a greater effect on particle number concentration than temperature changes. In particular it should be noted that between the two cases of “med RH, low T” and “high RH, low T” there is a large difference in relative humidity while the temperature is similar. The large difference of the mode concentration associated with the two cases (factor of about two) can therefore be most likely attributed directly to the increased relative humidity. This is supported by the same trend between the two cases “med RH, med T” and “high RH, med T.” This corroborates findings from dynamometer tests that studied separately the effects of temperature and humidity changes in the dilution air and showed that increasing humidity increased the number concentration of the nucleation mode (8). In this study two dilution air temperatures were tested, 34 and 45°C, of which the lower always caused a higher nucleation mode at similar relative humidities. While both temperatures showed the mentioned effect with humidity increase, it was stronger for the higher temperature that showed almost no nucleation mode at a low relative humidity of 41%. Its concentration increased by about an order of magnitude when the humidity was increased from 41% to 84%.

The size distribution was affected by temperature and humidity changes in two ways: first by increased number concentrations as described above; and second by an apparent growth of the aerosol. As can be seen in Figure 2, the modal diameter increased from 13 to 16 nm from the case of relative humidity below 30% with temperature of 15.3°C to the case of humidity between 30 and 70% with temperatures around 10°C. For further increased humidity the mode remained at about 16 nm. Dilution tunnel measurements that showed that the total number concentration increased when the temperature was decreased from 25°C to 15°C also showed that the nuclei mode diameter increased slightly with the temperature decrease (9). In addition to increased modal diameter and concentration, we observed an increased concentration at approximately 80 nm, which was evident in the cases of relative humidity above 70% in form of a shoulder next to the main mode. This shoulder was probably caused by relatively high contributions from background aerosol during the two nights with humidities above 70%. The averaged size distribution at 300 m upwind (Los Angeles National cemetery), our background site, for those two nights is unimodal with mode diameter at 64 nm and maximum concentration of 12,600 particle/cm3, which is more than twice the average for all nights at 64 nm. The high humidity during the two nights might be responsible for this relatively high contribution at larger particle sizes, since at similar temperatures and medium relative humidity it was not observed.

Figure 2. Ultrafine Particle Size Distribution at 30 m Downwind From Freeway 405 at Different Temperature and Relative Humidities

Figure 2. Ultrafine Particle Size Distribution at 30 m Downwind From Freeway 405 at Different Temperature and Relative Humidities

Conclusion: In summary, similar to what we observed during the daytime, ultrafine particle size distributions changed and particle number concentration decreased with downwind distance from the freeway at night. Traffic was only 25% at night but particle number concentration measured at 30 m downwind from the freeway was 80% of previous daytime measurements. This discrepancy between changes in traffic counts and particle number concentrations is apparently due to the decreased temperature, increased humidity, and lower wind speed at night. Particle number concentration decays exponentially downwind from the freeway similar to what was observed during the day, but at a slower rate. Ultrafine particle number concentration measured at 500 m downwind from the freeway was still distinguishably higher than upwind background concentration at night. This implies that freeway emissions spatially have a much broader effect on local air quality at night.

Predict Ultrafine Particle Concentration near Freeways

Objective: To determine particle number emission factors based on vertical concentration profiles near the Interstate 405 Freeway and to develop a simple atmospheric dispersion models that predict ultrafine particle number concentration as these particles are transported away from a major emission source—a freeway. The goal of this research is to quantitatively predict particle number concentration at any specified distance from a freeway based on traffic and atmospheric conditions.

Accomplishments:Vertical concentration profile of total particle number concentration measured by the CPC is shown in Figure 1. The highest total particle number concentration occurred around 3 to 7 m above the ground. There is a weak dimple effect at 10 m for total particle number concentrations. Error bars in Figure 1 were considerably larger than those in previously reported horizontal profiles (3). In horizontal profiles, dispersion was the dominant process in determining pollutants’ concentrations at a given downwind location from the source and is governed mainly by wind velocity. Due to a reliable sea breeze in the sampling area, relatively stable wind direction and speed were achieved during the sampling time. While, in vertical profiles, diffusion process is more important and is controlled by turbulence, which is much less predictable. These may partially explain the observed large error bars in Figure 1. Nevertheless, the general shape for vertical concentration profiles indicates we have captured most of the plume.

Figure 1. Total Particle Number Concentration Vertical Profile 50 m Downwind from the 405 Freeway.

Figure 1. Total Particle Number Concentration Vertical Profile 50 m Downwind from the 405 Freeway. Error bars indicate one standard deviation.

The atmospheric diffusion equation was found to provide a more general approach than the Gaussian models and was used in this study. Assuming incompressible flow and the absence of chemical reaction, atmospheric diffusion equation based on the Gradient-transport theory (K-theory) is (10)

Atmospheric diffusion equation

where C is the mean concentration of a pollutant; (u, v, w) and (Kxx, Kyy, Kzz) are the components of wind and eddy diffusivities vectors in x, y and z direction, respectively, in an Eulerian frame of reference.

For a continuous, crosswind line source ( ∂C / ∂y = 0 ), at a height h emitting at a rate ql (particle m-1s-1), with following assumptions:

(a) Steady state conditions, i.e. ∂C / ∂t = 0.

(b) The vertical velocity is much smaller than the horizontal velocity so the term w(∂C / ∂z) can be neglected.

(c) The x-axis is oriented in the direction of the mean wind, i.e., u = U and v = 0, where U is the wind velocity (U > 0).

(d) The diffusion in the direction of the mean wind can be neglected, i.e., Kxx = 0.

Equation (1) reduces to

Reduced Equation.

The source term ql is introduced through the following boundary conditions;

Boundary Conditions

Where δ (z-h) is Dirac’s delta function.
Far away from the line source, the concentration decreases to zero after subtracting the background concentration, i.e.,

Background Concentration

Ground surface is assumed impermeable to the pollutants, i.e.,

Ground Surface

For near source diffusion, Sharan, et al. (1996) showed that Kzz, the vertical eddy diffusivity, can be specified as linear functions of downwind distance based on Taylor's statistical theory of diffusion for small travel times (11). Thus,

Equation

where γ represents the turbulence parameter in the z direction. This is based on the fact that near a point source, the surface containing the standard deviations of the pollutants from a horizontal straight line to leeward of the source is a cone, not a paraboloid as the classical Gaussian model assumes. Now equation (2) becomes

Equation

Equation (7) with boundary conditions (3), (4) and (5) can be solved analytically using Fourier’s transforms or similarity method (12) to obtain

Equation

For practical application, the turbulence parameter γ, can be identified as the square of turbulence intensity using Taylor’s statistical theory of diffusion, i.e.,

Equation

When measurements of turbulence intensities are available, γ should be computed directly by Equation (9). In the absence of direct measurement, mixed-layer similarity scaling and empirical turbulence data suggest that σ w = bw*, where w* is the convective velocity scale. This scale is the magnitude of the vertical velocity fluctuations in thermals and is usually on the order of 1.0 – 2.0 m/s (13). Depending on the dimensionless height z/zi, where zi is the convective mixing height, the constant b can be from 0.4 to 0.6. It is a good approximation to take b=0.4 for modelling dispersion in the surface layer and b=0.6 in the mixed layer (14, 15). Thus, turbulence parameter can be expressed as

Turbulence Parameter

In order to use the model to predict particle number concentration at given downwind distance from the freeway, the line source strength for particle number concentration; ql (particle m-1s-1) has to be determined. This can be done by integrating both side of Equation (8) from 0 to infinity with respect to vertical height.

Equation

Comparing the right hand side of Equation (11) to an error function defined as

Equation

Equation (11) can be rewritten as

Equation

where z goes to infinity. Since erf()=1, Equation (13) is reduced to

Equation

where C(x,z) is the concentration of particles as a function of sampling height z and U(x,z) is the average wind speed at the sampling height z. ql can be also viewed as the unit length flux F through the plane on the downwind side of the freeway (16) which could be obtained by integrating the product of wind speed and particle number concentration with respect to increment of vertical height as were done in Equation (14).

During the measurement period, more than 85% of the time, wind was blowing perpendicular from the freeway to the sampling site. Average wind speed and particle number concentration measured by the CPC at each sampling height as well as particle number concentration measured upwind of the freeway are summarized in Table 1. As shown in Table 1, average wind speeds were approximately constant with sampling height and had relatively small and similar standard deviations. This result is consistent with previous studies (16, 17).

Table 1. Average Wind Speed and Particle Number Concentration as a Function of Sampling Height

Sampling Height, h (m)

Average Wind Speed, U(h) (m/s)

Average Particle Number Concentration, C(h) (particle/cm3)

0.6

1.3 ±0.5

1.0×105

3.0

1.3 ±0.4

1.2×105

5.5

1.3 ±0.4

1.2×105

8.0

1.3 ±0.5

1.0×105

10.4

1.4 ±0.5

0.9×105

12.8

1.5 ±0.5

0.8×105

15.3

1.3 ±0.4

0.7×105

17.7

1.4 ±0.4

0.5×105

Upwind

N/A

3.5×104

Previously Gramotnev, et al., has shown that at the height of ~15 m, the vertical concentration profile decreases to the background level by means of CALINE4 model. As shown in Table 1, in the current study vertical concentration at 17.7 m was still higher than the background concentration, which is usually on the order of 3.5×104 particle/cm3 as reported in Zhu, et al., (2002a). After subtracting the background concentration of 3.5×104 particle/cm3, Equation (14) was rewritten in terms of discrete sampling heights within our sampling range to get a measured source strength, ql*.

Equation

Based on data summarized in Table 1, ql* was calculated to be 1.44×1012 (particle m-1s-1). It should be noted ql* is not the real source strength. It is just a measured value based on data within our sampling height. Since our sampling height has only captured most of the plume, not the complete one, a scale factor must be introduced to achieve the real line source strength.

The scale factor, ql/ql* could be determined by dividing the integrals of Equation (8) with respect to our sampling height to that of infinity.

Equation

The highest sampling height in the current study was 17.7 m (z =17.7 m). The emission source was at 4.5 m above the ground (h = 4.5 m). Vertical sampling was conducted at a horizontal distance of 35 m from the edge of the freeway (x = 35 m). The turbulence parameter γ, was not directly measured in the current study. It was determined by using Equation 12 in which the mean value of convective velocity scale found in the literature, 1.5 m/s, was used with average wind speed measured during vertical study, 1.3 m/s. The turbulence parameter γ was determined to be 0.21. For the highest sampling height, z=17.7

Equation

From error function table a value of 0.588 and 0.830 was obtained for these two terms respectively. Since

Equation

the real line source strength was estimated to be 1.44×1012/0.71 = 2.0×1012 (particle m-1s-1). Source strength ql was related to emission factors through traffic density by

Equation

where E is the average particle number emission factor from vehicles (particle/vehicle/mile) on the freeway; V is average traffic volume (vehicle/sec). Average traffic was determined to be 3.9 vehicles per second based on traffic data recorded on the videotapes. Thus the average particle number emission factor was calculated to be 8.3×1014 (particle/vehicle/mile) or 5.2×1011 (particle/vehicle/m).

The emission factor 8.3e14 (particle/vehicle/mile), was then plugged into Equation (8) to predict horizontal particle number concentration profiles under conditions in which previous horizontal measurements were conducted (3). The emission height was set to the height of the freeway, 4.5 m; sampling height was 1.6 m. Wind speed was the average wind speed during horizontal sampling, 1.5 m/s, perpendicular to the freeway. Background particle number concentration was 3.5e4 particle/cm3. Average traffic density was 3.8 vehicle/sec, a little bit lower than during vertical sampling. Since no direct measurement was available for turbulence parameter data, γ was determined by using Equation 12. Three γ values were used representing the low, medium and high convective velocity scale in the literature, namely, 1.0 m/s, 1.5 m/s and 2.0 m/s. These three conditions result in values of 0.07, 0.16 and 0.28 for turbulence parameter.

Model prediction was then compared to previous measurement and presented in Figure 2. Horizontal axis is distance from the edge of the freeway in this figure whereas experimental data was previously reported as from the center of the freeway (3). Half width of the freeway, ~15 m, was subtracted from previously published results. The model with all three γ values predict a sharp increase close to the source due to the difference between the source and receptor height and an exponential decay with increasing downwind distance. In generally, the model developed in this study fits very well to the experimental data. It is noted that the model is moderately sensitive to γ values. All three curves give reasonable prediction to experimental data although the mean value of convective velocity scale seems to yield a better fit to experimental data. These results imply that atmospheric dispersion is by far the most important mechanisms in determining particle number concentration near freeways.

Figure 2. Comparison of Model Predicted Particle Number Concentration with Experiment Data Near the 405 Freeway

Figure 2. Comparison of Model Predicted Particle Number Concentration with Experiment Data Near the 405 Freeway

Other aerosol or chemical processes may have an effect on the particle size distribution but not much on total particle number concentrations. Thus, the model developed in this study provides epidemiologists and toxicologists a simple tool to estimate ultrafine particle number concentrations near freeways for health related studies.

Conclusions: A simple analytical solution has been presented in this study to estimate particle number concentration near freeways based on measured vertical concentration profile. The model predicts particle number concentration near freeways very well. Atmospheric dispersion was found to be the dominant mechanisms in determining the particle number concentration near freeways. The analytical solution provides a reasonable estimation of the dispersion process in near field situations. Average particle number emission factor, 8.3×1014 particle/vehicle/mile, was determined based on vertical concentration profile and a scale factor obtained from the dispersion model. With proper particle number emission factors, traffic compositions, and meteorological data, particle number concentrations downwind of freeways can be quantitatively determined by the atmospheric dispersion model developed in this study.

Modeling Ultrafine Particle Size Distributions Near Freeways

Objective: To analyze data collected near the 405 and 710 freeways in Los Angeles, CA and simulate the aerosol dynamics.

Accomplishments: The dispersion of exhaust near highway can be described by two steps, i.e., “tailpipe-to-­road” and “road-to-ambient”. The data collected near the Interstate 405 Freeway clearly represent the “road-to-ambient” process. Next we analyzed the data, elucidating the aerosol dynamics which led to the dramatic changes in particle size distributions from 30 m to 300 m, and then introduced a multi-component sectional aerosol dynamic model to simulate this process. Finally we derived receptor-dependent, road and grid-level particulate emissions in size-resolved emission factor distributions near the roadway and at the 1 km distance scale.

During the tailpipe-to-road process, the sharp drop in temperature and relatively high concentrations lead to significant condensation of vapor emissions, making particle composition a complex mixture, too. The relative amount of condensed materials usually depends on particle size. As exhaust disperses from roadways, the gas-phase concentration decreases. Then during the road-to-ambient process, some compounds may continue condensing while others may begin evaporating, depending on the relative magnitude of their partial and vapor pressure. In addition, the vapor pressure is further modified by the molar fraction of each component in the particle phase according to Raoult’s law and by the Kelvin effect, which has profound impact on the dynamics of small particles. This competition between partial pressure and Raoult- and Kelvin-adjusted vapor pressure coupled with dilution is the main reason why some particles grew and others shrank at the same time. Volatile gases may evaporate from particles to achieve gas-particle equilibrium, while small particles have to grow fast enough to minimize their Kelvin effect before the concentration of the condensing materials drops to a level making their growth unfeasible.

Figure 1 demonstrated the simulation results. We choose 7 size bins for model validation against measurement with size cuts at 6, 10, 20, 40, 60, 80, 120 and 220 nm. The selection takes into account the modal shapes for all measured size distributions. Except for the 6 – 10 nm range affected by the tail effect, our model closely matched the measurement data for all other size bins at all distances. The relative roles of condensation/evaporation and dilution can be clearly illustrated by turning off the condensation / evaporation operator. Figure 2 depicts such a comparison for 405S at 150 m. Since the dilution-only model always preserves the initial shape of the size distribution, it led to serious discrepancies from measurement data at later steps of the evolution process, while the dynamic model was able to reproduce the size distributions at all distances even when the relative differences were considered.

Figure 1. Simulation of Highway 405 Summer Study (405S) at (a) 60 m, (b) 90 m (c) 150 m and (d) 300 m.

Figure 1. Simulation of Highway 405 Summer Study (405S) at (a) 60 m, (b) 90 m (c) 150 m and (d) 300 m.

    Figure 1. Simulation of Highway 405 Summer Study (405S) at (a) 60 m, (b) 90 m (c) 150 m and (d) 300 m. The number over each size bin indicates relative difference between measurement and prediction with positive for over-prediction and negative for under-prediction.

Figure 2. Comparison Between (a) the Dilution-Only Model and (b) the Dynamic Model for Highway 405 Summer Study (405S) at 150 m

    Figure 2. Comparison Between (a) the Dilution-Only Model and (b) the Dynamic Model for Highway 405 Summer Study (405S) at 150 m

Aerosol dynamics is driven toward gas-particle equilibrium. Particle compositions could change dramatically in this process as components adapt to decreasing gas-phase concentration due to dilution. Thus evolution of number distribution is also an evolution of compositions. The optimal carbon numbers for the condensing volatile organics were 21 and 24, respectively. Those numbers fall into the range where the largest fuel molecules and smallest lubrication oil molecules reside.

Figure 3 depicts the particle number and mass emission factors, which were obtained by inverse-modeling a chemically inert, concurrently-measured, gaseous co-pollutant (carbon monoxide), and then correlating its concentrations to particle concentrations. The total number emission factors derived from this study agree well with previous on-road investigations. Road-level emissions are generally multi-modal, while grid-level emissions are distinctly mono-modal. The common modes shift from 40 to 100 nm in summer season to 10 to 20 nm in winter season. On-road fine-mode mass emission rates for trucks and non-trucks in around 133 and 21 mg km-1 vehicle-1, respectively. We also observed distinct contrasts in the shapes of particle number and mass emission factor distributions that indicate that the effects of plume processing on particle number near roadways are much more profound than on particle mass.

Figure 3. Distance (left axis) and Fuel-Based (Right Axis) Particle Number and Mass Emission Factors in Highway 405 Summer and Winter Studies.

    Figure 3. Distance (left axis) and Fuel-Based (Right Axis) Particle Number and Mass Emission Factors in Highway 405 Summer and Winter Studies. The error bars only denote those of distanced-based emission factors. Zero EF values indicate measured concentration below the background level.

Conclusions: We successfully simulated the evolution using a multi-component aerosol dynamic model. The model was fitted to measurements to identify the physicochemical properties of the volatile species. Compared with a dilution-only model, it is better able to capture the aerosol dynamics during this evolution process.

Penetration of Freeway Ultrafine Particles into Indoor Environment

Objective: Previously we have reported high concentrations of ultrafine particles near major freeways (2, 3). These results imply increased exposure to harmful pollutants in areas close to such hot spots. Many urban residences and business are located in close proximity to busy roadways. Consequently, indoor environments in urban areas may experience significant concentrations of outdoor ultrafine particles, exposing tenants to potentially toxic pollutants. People spend over 80% of their time indoors, therefore characterization of indoor ultrafine particles of outdoor origin and assessment of their penetration efficiencies are important factors in determining human exposure to outdoor contaminants. The present study determines penetration behavior of outdoor ultrafine particles into indoor environments in areas close to freeways. Results from this research have important implications concerning personal exposure to freeway related ultrafine particles and possible health consequences.

Accomplishments: Four two-bedroom apartments in the vicinity of the I-405 Freeway in Los Angeles, CA were selected for this study. These apartments are in identical buildings with the same interior layouts. Three of the four apartments (Apt 1, 2, and 3) are on the eastern side of the 405 Freeway. These three apartments are on the third floor with windows 3 m above a sound barrier wall. The distances between apartments 1-3 and the wall range from 15 m to 40 m. All three apartments are separated by no more than 50 m. The fourth apartment (Apt 4) is on the opposite, western, side of the 405 Freeway, 15 m from the sound barrier wall. Apt 4 is on the second floor with windows 0.5 m above a similar sound barrier wall. All the apartments are about 8 years old with central mechanical ventilation systems that can be turned on or off. Arrangements with occupants allowed for sample collection during periods with no cooking or cleaning activities.

Both indoor and outdoor ultrafine particle size distributions in the size range of 6 to 220 nm were measured simultaneously by two Scanning Mobility Particle Sizers (SMPS). Sampling flow was checked on site on a daily basis to ensure absence of flow leakage. Data reduction and analysis of the SMPS output were done by the Aerosol Instrument Manager software (version 4.0, TSI Inc., St. Paul., MN). Carbon monoxide (CO), carbon dioxide (CO2) concentrations, ambient temperature, relative humidity and air exchange rates were also monitored during the particle size distribution measurements.

Figure 1 shows averaged particle size distributions and indoor/outdoor ratios for studied apartment 1. Figure 1a shows day time (10 am – 5 pm) and Figure 1b illustrates night time indoor and outdoor particle size distributions. Each graph indicates the number of 3-minute observations averaged to obtain indoor and outdoor particle size distribution curves. Figure 1c shows size dependent indoor/outdoor ratios during day and night times. For this apartment, particle number concentrations were lower than those observed previously near the 405 Freeway (3). A sound barrier wall and closely spaced trees separating the freeway from sampling locations may have contributed to the loss of particles. Sites used for this study were 5 miles south of the previous sampling site and measurements were performed from an elevation of 5 m above ground level. The vertical profile may have contributed to lower number concentrations. Meteorological conditions, specifically higher wind speeds, also favored lower particle concentrations.

Figure 1. Averaged (a) Day Time, (b) Night Time Outdoor and Indoor Particle Size Distributions and (c) Size Dependant I/O Ratios in Apt 1. Number of observations is given in parentheses.

    Figure 1. Averaged (a) Day Time, (b) Night Time Outdoor and Indoor Particle Size Distributions and (c) Size Dependant I /O Ratios in Apt 1. Number of observations is given in parentheses.

A daytime outdoor particle size mode near 20 nm was observed outside of apartment 1, consistent with previous reports (3). No such mode exists for indoor observations, and the indoor particle number concentrations are much lower and more stable than outdoors. Nighttime particle number concentrations, shown in Figure 1b, are comparable to their daytime values. Although traffic densities are lower during the night, vehicle speeds on the freeway are much faster. It has been shown previously that faster vehicles generate more particles (3). Lower nighttime temperatures, may also result in higher emission factors for particle numbers, as shown previously (18). Yet another reason for higher particle number concentrations during the night may be lower wind speeds and a lower atmospheric mixing height at night, thus weaker atmospheric dilution effects.

As Figure 1c shows, I/O ratios during day and night times exhibit similar trends and shapes. The difference between day and night I/O is due to higher air exchange rates during daytime. Day and night I/O profiles for particles above 20 nm are consistent with theoretical curve shapes. Curves for particles below 20 nm do not correspond to the accepted theory, as no downward trend is observed for both day and night time observations. These results may be affected by low instrument detection limits, and thus have less statistical confidence. Particle volatility may also explain why the I/O ratio increases for very small particles. Freeway ultrafine particles are known to have a large fraction of volatile components, especially for particles below 50 nm (18). For example, some of the particles in the 20-40 nm size range may lose their volatile components and become particles of 20 nm or less. Such loss of volatile components has been reported previously (19).

Measurements were made under three ventilation conditions: closed window with the fan off (A), closed window with the fan on (B), and open window with the fan off (C). Averaged I/O size-dependent particle concentration ratios and their standard deviations for the three ventilation conditions are shown in Figure 2. Under natural conditions (2a), the curve exhibits a similar shape as I/O ratio curves in Figure 1, with an expected downward trend for particles down to 20 nm, and an uncharacteristic upward trend for smaller particles. Under mechanical ventilation (2b), much lower I/O ratios are observed. This may be due to some partial filtering of the air entering the building by the ventilation system, which would decrease indoor particle concentration. The fan effect also diminishes the increase in I/O ratio for particles below 20 nm, as observed under natural ventilation conditions. With an open window (2c), the I/O ratio is very close to 1.0 across all particle sizes.

Conclusions: In summary, particle number concentration I/O ratios showed strong dependence on particle sizes and were influenced by different ventilation mechanisms. Under natural ventilation, the highest I/O ratios (0.6–0.9) were usually observed for larger ultrafine particles (70-100 nm), while the lowest I/O ratios (0.1–0.4) occurred typically around 10­20 nm. The size distributions of indoor aerosols showed less variability than those of outdoor freeway aerosols.

Figure 2. Averaged Size Dependant I/O Ratios and Standard Deviations Under Different Ventilation Conditions in Apt. 1. (a) Natural Ventilation (b) Fan On and (c) Window Open.

    Figure 2. Averaged Size Dependant I/O Ratios and Standard Deviations Under Different Ventilation Conditions in Apt. 1. (a) Natural Ventilation (b) Fan On and (c) Window Open.

Overall Conclusions

We found wind speed and direction, temperature, and relative humidity are important in determining the characteristic of ultrafine particles near freeways. The stronger the wind, the lower the total particle number concentration. The lower the temperature, or the higher the humidity, the higher the emission factor for ultrafine particles. The maximum ultrafine particle number concentration observed near freeways was about 25 times greater than background concentrations and exponentially decay with distance along the wind direction. Strong seasonal and diurnal variations were found near freeways. Dispersion model and aerosol dynamic model can be developed to simulate ultrafine particle behaviors near freeways.

References:

  1. Hitchins J, Morawska L, Wolff R, Gilbert D. Concentrations of submicrometre particles from vehicle emissions near a major road. Atmospheric Environment 2000;34:51-59.
  2. Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmospheric Environment 2002a;36:4323-4335.
  3. Zhu Y, Hinds WC, Kim S, Sioutas C. Concentration and size distribution of ultrafine particles near a major highway. Journal of Air and Waste Management Association 2002b;52:174-185.
  4. Finlayson-Pitts BJ, Pitts JNJ. Chemistry of the upper and lower atmosphere. San Diego: Academic Press, 2000.
  5. Zhu YF, Hinds WC, Kim S, Shen S, Sioutas C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmospheric Environment 2002c;36:4323-4335.
  6. Zhu YF, Hinds WC, Shen S, Sioutas C. Seasonal trends of concentration and size distribution of ultrafine particles near major highways in Los Angeles. Aerosol Science and Technology 2004;38:5-13.
  7. Zhu YF, Hinds WC, Kim S, Sioutas C. Concentration and size distribution of ultrafine particles near a major highway. Journal of the Air & Waste Management Association 2002d;52:1032-1042.
  8. Mathis U, Mohr M, Zenobi R. Effect of organic compounds on nanoparticle formation in diluted diesel exhaust. Atmospheric Chemistry and Physics 2004;4:609-620.
  9. Wei Q, Kittelson DB, Watts WF. Single-stage dilution tunnel performance. SAE Technical paper series 2001-01-0201(2001).
  10. Seinfeld JH, Pandis SN. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. New York: Wiley, 1998.
  11. Taylor GI. Diffusion by continuous movements. In: Proceedings of the London Mathematical Society Ser, 1921, 2XX:196-212.
  12. Kevorkian J. Partial differential equations analytical solution techniques. Pacific Grove, CA: Chapman & Hall, 1990.
  13. Stull RB. An introduction to boundary layer meteorology. Dordrecht, The Netherlands: Kluwer Academic, 1988.
  14. Sharan M, Yadav AK, Singh MP, Agarwal P, Nigam S. A mathematical model for the dispersion of air pollutants in low wind conditions. Atmospheric Environment 1996;30:1209-1220.
  15. Sharan M, Singh MP, Yadav AK. Mathematical model for atmospheric dispersion in low winds with eddy diffusivities as linear functions of downwind distance. Atmospheric Environment 1996;30:1137-1145.
  16. Gramotnev G, Brown R, Ristovski Z, Hitchins J, Morawska L. Determination of average emission factors for vehicles on a busy road. Atmospheric Environment 2003,37:465-474.
  17. Benson PE. CALINE-4. A dispersion model for predicting air pollutant concentrations near roadway. Final Report, FHWA/CA/TL.-84/15. Sacramento, CA: California Department of Transportation, 1989.
  18. Kittelson DB. Engines and nanoparticles: a review. Journal of Aerosol Science 1998;29:575-588.
  19. Lunden MM, Revzan KL, Fischer ML, Thatcher TL, Littlejohn D, Hering SV, Brown NJ. The transformation of outdoor ammonium nitrate aerosols in the indoor environment. Atmospheric Environment 2003;37:5633-5644.

Journal Articles:

No journal articles submitted with this report: View all 13 publications for this subproject

Supplemental Keywords:

RFA, Health, Scientific Discipline, PHYSICAL ASPECTS, Air, Geographic Area, particulate matter, Environmental Chemistry, Health Risk Assessment, State, Risk Assessments, mobile sources, Biochemistry, Physical Processes, urban air, engine exhaust, atmospheric particulate matter, atmospheric particles, motor vehicle emissions, airway disease, exposure, automobile exhaust, particulate emissions, automotive emissions, air pollution, automobiles, automotive exhaust, diesel exhaust, air sampling, human exposure, ultrafine particulate matter, PM, diesel exhaust particles, frreway study, California (CA), PM characteristics, human health risk

Relevant Websites:

http://www.scpcs.ucla.edu Exit

Progress and Final Reports:

Original Abstract
  • 1999
  • 2000
  • 2001 Progress Report
  • 2002 Progress Report
  • 2003 Progress Report
  • Final Report

  • Main Center Abstract and Reports:

    R827352    Southern California Particle Center and Supersite

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R827352C001 The Chemical Toxicology of Particulate Matter
    R827352C002 Pro-inflammatory and the Pro-oxidative Effects of Diesel Exhaust Particulate in Vivo and in Vitro
    R827352C003 Measurement of the “Effective” Surface Area of Ultrafine and Accumulation Mode PM (Pilot Project)
    R827352C004 Effect of Exposure to Freeways with Heavy Diesel Traffic and Gasoline Traffic on Asthma Mouse Model
    R827352C005 Effects of Exposure to Fine and Ultrafine Concentrated Ambient Particles near a Heavily Trafficked Freeway in Geriatric Rats (Pilot Project)
    R827352C006 Relationship Between Ultrafine Particle Size Distribution and Distance From Highways
    R827352C007 Exposure to Vehicular Pollutants and Respiratory Health
    R827352C008 Traffic Density and Human Reproductive Health
    R827352C009 The Role of Quinones, Aldehydes, Polycyclic Aromatic Hydrocarbons, and other Atmospheric Transformation Products on Chronic Health Effects in Children
    R827352C010 Novel Method for Measurement of Acrolein in Aerosols
    R827352C011 Off-Line Sampling of Exhaled Nitric Oxide in Respiratory Health Surveys
    R827352C012 Controlled Human Exposure Studies with Concentrated PM
    R827352C013 Particle Size Distributions of Polycyclic Aromatic Hydrocarbons in the LAB
    R827352C014 Physical and Chemical Characteristics of PM in the LAB (Source Receptor Study)
    R827352C015 Exposure Assessment and Airshed Modeling Applications in Support of SCPC and CHS Projects
    R827352C016 Particle Dosimetry
    R827352C017 Conduct Research and Monitoring That Contributes to a Better Understanding of the Measurement, Sources, Size Distribution, Chemical Composition, Physical State, Spatial and Temporal Variability, and Health Effects of Suspended PM in the Los Angeles Basin (LAB)