ULTRAFINE PARTICLE EMISSIONS ALONG MOTORWAYS

Transcription

1 August 2003 ECN-C ULTRAFINE PARTICLE EMISSIONS ALONG MOTORWAYS Emission factors, concentrations and size distributions E.P. Weijers G.P.A. Kos P.A.C. Jongejan Revisions A B Made by: E.P. Weijers Checked by: Approved: G.J. de Groot Issued: ECN-Clean Fossil Fuels Air Quality & Climate Change A.T. Vermeulen J.W. Erisman

2 Acknowledgements This report summarises the research on ultrafine particulate matter near Dutch motorways executed by the Energy research Centre of the Netherlands (ECN). The study was carried out within the framework of the Netherlands Aerosol Programme (NAP). NAP is an initiative of the Netherlands Ministry of Housing, Spatial Planning and the Environment (VROM), in collaboration with the Ministry of Economic Affairs (EZ and the Ministry of Transport, Public Works and Water Management (V&W). This research project (ECN number ) was funded by the Netherlands Agency for Energy and the Environment (NOVEM). The Municipal Health Institute of Amsterdam is gratefully acknowledged for providing the NO x and CO monitors (J. Visser, H. Helmink), and the inspiring discussions during preparation and execution of the experimental campaign (J. van Wijnen, S. van der Zee). Also, J. Slanina is thanked for his comments. Keywords Particulate matter, ultrafine, emission factor, size distribution, traffic, motorway, CPC, SMPS, NO x, CO 2 ECN-C

3 CONTENTS LIST OF TABLES 4 LIST OF FIGURES 4 SUMMARY 5 1. INTRODUCTION 7 2. METHODS Description of sampling sites Sampling and instrumentation Emissions-factor calculation scheme RESULTS AND DISCUSSION Determination of ultrafine particle number emissions factors Change in concentrations and size distributions of ultrafine particulate matter with increasing distance Wind effects Changes in particle numbers with increasing distance Change in ultrafine particle distribution with increasing distance SUMMARIZING CONCLUSIONS REFERENCES 29 APPENDIX A 31 APPENDIX B 35 APPENDIX C 39 ECN-C

4 LIST OF TABLES Table 1 Sampling dates and instruments used 10 Table 2 Emission factors CO and NO x 13 Table 3 Emission factors for ultrafine particle numbers 15 LIST OF FIGURES Figure 1 Regression analyses for the A4-data set 13 Figure 2 Regression analyses for the A9-data set. 14 Figure 3 Regression analysis for the combined data set. Regression coefficient is now Figure 4 Total particle number (measured by CPC3022) located 70 and 124 m downwind versus wind speeds (November 7th, 13:52-14:26 and 14:40-15:06). Bars indicate 1 standard deviation 17 Figure 5 Traffic intensity on the A9 not far from our experimental site for Thursdays in November Figure 6 Particle number concentrations (CPC3022) along the A9-motorway for each available measurement period collected on September 26, and November 7, 11 and 12, as function of distance to the curb 18 Figure 7 Averaged particle number concentrations (CPC3022) along the A9-motorway corrected for wind speed and direction as function of distance to the curb 19 Figure 8 Particle size distributions at different sampling distances from the A9- motorway. Effective distances are (a) 12 m downwind, (b) 40 m downwind, (c) 62 m downwind, (d) 86 m downwind, (e) 124 m downwind, and (f) 150 m upwind 22 Figure 9 Ultrafine particle size distributions on September 26th 23 Figure 10 Ultrafine particle size distributions on November 7th 23 Figure 11 Ultrafine particle size distributions on November 11th 24 Figure 12 Ultrafine particle size distributions on November 12th 24 Figure 13 Particle number concentrations for different size ranges as a function of distance to the A9-motorway on September 26, ECN-C

5 SUMMARY The aim of this study is the determination of emission factors for ultrafine particles and size distributions along motorways in the Netherlands. In the experimental campaign particle number concentrations and size distribution in the size range from 7 to 220 nm were measured by condensation particle counters (CPC) and a scanning mobility particle sizer (SMPS), respectively. The measurements were taken upwind and at various distances downwind of two Dutch motorways; one site was situated about 20 km north of the city of Amsterdam (A9- motorway) in the open field, and one within the Amsterdam agglomeration (A4, near the junction with the A10. At each sampling day, concentrations of particulate matter, CO and NO x were measured over 20 to 30 min after which the mobile measurement was driven to the next location at another distance. In this study the experimental data collected at 8 days are discussed. Emission factors for the ultrafine range (<100 nm) were determined according to the method proposed by Bloemen and van Putten (1998) using measurements of the vehicular contributions to particulate matter, CO and NO x, thereby circumventing the necessity of having detailed knowledge about traffic intensity. Using the data of 8 measurement days (4 at each location), the average emission factors for particle number at the two motorways were found to be in the range of km -1 for light-duty vehicles and km -1 for high-duty vehicles. The second goal of this work was to build up insight in the changes in distribution of ultrafine particle number concentrations. To this purpose, data obtained near the (undisturbed) A9- motorway was taken into consideration. It was found that particle number concentration (7-220 nm) decayed in an exponential manner with the (downwind) distance from the motorway. On average, number concentrations at 124 m dropped to approximately 50-60% of its original value at 30 m. Ultrafine particle concentration measured at 124 m downwind is still 3 times larger than the number measured at the background location 150 m upwind of the motorway. The maximum number concentration that was observed near the motorway ( cm -3 ) was 5-6 times greater than that at the background location. These results indicate that people who live, work or travel within 100 m downwind of a major traffic source like the A9 will have much higher ultrafine particle exposure that those who live further away from those sources. The data further showed that both atmospheric dispersion (due to higher wind speed) as well as coagulation contributed to the rapid decrease in particle number concentration and change in shape of the particle size distribution. The nuclei mode contributes most to the number concentration but rapidly decreases with increasing distance. Further downwind (>60 m) the shape of the distribution indicates ageing of the aerosol by showing saddle points with time scales in the order of minutes. ECN-C

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7 1. INTRODUCTION Throughout the past decade, epidemiological studies have reported a consistent relationship between increases in particulate matter (PM) exposure and higher mortality and morbidity rates. However, the underlying biological causes of the health effects due to exposure to PM and the most effective measurement metric still remain unclear. An important issue today in this matter is the role of the ultrafine fraction (aerodynamic particle diameter is less than 100 nm, also denoted as PM 0.1 ). Recent toxicological studies indicate that, at the same mass concentration, ultrafine particles are more toxic than larger particles having a comparable chemical composition. Also, recent dosimetry studies have shown that the total deposition fraction of ultrafine particles increases as particle size decreases. However, information about ultrafine particles is still scarce even though ultrafine particles dominate the particle number concentrations in an urban environment (by some 80%). Further knowledge on both number concentration and size distribution is necessary to understand the cause(s) for the observed adverse health effects. In urban environments motor-vehicle activity constitute the most important source of ultrafine particles. It is therefore important to study their behaviour after emission and to quantify their properties along source locations like busy roads and motorways. The first item of this study is the determination of vehicular emissions of ultrafine matter. One way to determine vehicular emissions is by measuring pollutant's concentrations in the emitted gas streams produced by vehicles placed on dynamometers. Operating under standardised driving conditions, "real-world" conditions are simulated. However, doubt remains whether such assessments are accurate enough. For example, emissions from mobile vehicles depend heavily on age and maintenance, which is usually not always included in the dynamometer testing procedures. Also, physical and chemical processes occurring behind the vehicle's outlet in open air may not be representatively caught in direct dynamometer measurements. An 'open-air' method was therefore necessary and ultimately found in the work of Bloemen and van Putten done in Essentially, the study provides another example of the usage of ambient air quality data to estimate vehicular emissions factors. A measurement campaign was designed that provided the necessary data to assess the PM 0.1 -emission factors on two Dutch motorways. These measurements include number and mass concentrations of PM 0.1 as well as NO x and CO concentrations. Other measurements were added to the campaign to collect data for a further study on the size distribution of ultrafine particles at various distances of a motorway, being the second focus of this study. ECN-C

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9 2. METHODS 2.1 Description of sampling sites This study has been conducted in the province of Noord-Holland adjacent to the A9-motorway between September 26 and November 14, The A9 runs generally north and south (actual orientation 350) with no upgrade. The first sampling site is not far from the village of Spaarndam (see Appendix A) between the towns of Haarlem and Alkmaar. In the immediate vicinity of the site, the terrain is flat-mowed lawn with only a few tiny storage buildings not higher than 3 m. The flat meadowlands extend at least 2 km to the east and west of the motorway and 1.5 km along the motorway with no significant local sources of PM emissions other than the motorway. Measurements were made along a small labour road with brick stones at the same height level. In view of the location specifics the site constitutes a regional station. The site lies approximately 15 km east of the North-Sea coast and 8 km northwest of the Amsterdam city area. During the sampling period, a consistent western wind was prerequisite to collect measurements of sufficient quality. The region upwind of the motorway is a residential being part of Haarlem with no industrial or other obvious PM sources. Background measurements were taken ~100 m upwind of the motorway. The only other motorway of major roads nearby is the A5, which runs perpendicular to the A9- motorway not more than 0.9 km to the south. During the sampling periods, the A5 has smaller traffic intensity and with the usual wind direction (form south-west to north-east), traffic on the A5 was more than 2 km away along the wind vector and is expected to have little influence on particle levels. The A9-motorway at the experimental site has 6 lanes, including the two hard-shoulder lanes. It is ~20 m wide, including a 2 m-wide median strip. The location of each measurement site for this study was determined by measuring its distance from the curb of the nearest (right-hand) traffic lane. In contrast to the A9-site, a second comparative experiment was conducted in the southwest part of the city of Amsterdam adjacent to the A4-motorway situated very near the A4-junction with the A10-ring motorway (see Appendix A). This was done in order to investigate the effects of an urban infrastructure on concentration gradient and traffic emissions. The A4 runs west to east (orientation 270 ). This typical urban station is characterised by a complicated infrastructure in the immediate surroundings: relatively high buildings and small trees located north of the motorway and large trees on the southern side. Immediately south of the motorway a secondary road and a lake (Nieuwe Meer) are encountered, which borders the A4 for roughly 1 km; more southwards a large forest (Amsterdamse Bos) exists. During the sampling periods, southern winds were therefore expected to transport relatively clean air as no obvious PM sources are envisaged in this direction. Measurements during westsouthwestern winds are excluded because of the location of the Amsterdam airport in this direction. No other local sources of PM are known apart from the A10-motorway and some busy roads. In case of wind directions a (almost) perpendicular to the A4 traffic emissions on the A10 will have little or no influence on the particle levels. One of the reasons of selecting this site was the lake located just south of the A4. The presence of the lake induces higher wind speeds at the lower heights resulting in cleaner air mixing in from higher altitudes and lower upwind concentrations. Measurements were made on a parking lot between two high business buildings perpendicular to the motorway. At the sampling site, the motorway is elevated ~8 m above the surrounding ECN-C

10 terrain. The A4-motorway has 10 lanes in total at that location. It is ~45 m wide, including a 10- m-wide median strip giving space for a railway line. The location for each of the measurements was determined by measuring the distance from the curb of the road. 2.2 Sampling and instrumentation Wind speed and wind direction were measured 3 m above ground level on top of the mobile measurement van. Wind data were averaged over 1-min intervals and logged into a computerised weather station. Data on average traffic intensity the A9 and A4 motorway were provided by the Ministry of Transport, Public Works and Water Management (V&W). The total traffic intensity was available but not divided in light and heavy-duty vehicles. Particle number concentration and size distribution in the size range from 13 to 583 nm were measured by a condensation particle counter (CPC3022; TSI Inc.) and a scanning mobility particle sizer (SMPS; TSI Inc.) with an exception on November 6 th (CPC3025; range: nm). The sampling flow rate of the SMPS was adjusted to 1 l/min to measure particles as small as 7 nm and to minimise the diffusion losses of ultrafine particles during sampling. A special correction scheme for diffusion losses was applied on the acquired data according to Hinds (1982). All tubing consisted of copper metal with a length of about 1.5 m and a 1/4 inch diameter. A dryer was inserted for maintaining a constant humidity. The sizing accuracy of the SMPS was verified in the laboratory by means of monodisperse polystyrene latex spheres (PSL, Polysciences Inc.). Data reduction and analysis of the SMPS output were performed using TSI programs. Measurements were taken at various distances downwind and upwind from the nearest curb of the motorway. The distances were chosen based on preliminary measurements and previously published literature. At each location, one size distribution sample was taken; scanning time varied between 20 and 45 min per sample. In addition to size distribution and total number concentrations, concentrations of CO and NO x were monitored simultaneously at each sampling location. Before each measurement, all instruments were synchronised. Data were averaged later over the time periods corresponding the scanning intervals of the SMPS. A near-continuous CO monitor measured CO concentrations every minute. The CO monitor was calibrated by means of standard CO gas in the laboratory of the Amsterdam Health Institute and automatically zeroed each time the power was turned on. A continuous NOx monitor measured NOx concentrations every minute. A 4- kw gasoline-powered portable generator supplied electric power for the various instruments. The generator was placed downwind of each of the measuring devices. Table 1 gives the sampling dates and times and summarises the instruments that were used on each date. All the instruments were installed in the measurement van. When measurements were finished at one location, all applicable instruments were moved together by driving the van to the next location. Sampling occurred simultaneously at each location. It took about minutes to complete sampling at each location and about 150 min to complete a data set cycle at all 4 locations. Two, three or four sets were performed on each sampling date. Table 1 Sampling dates and instruments used Day motorway CPC-3022 CPC-3025 SMPS CO/Nox 26-Sep-02 A9 x x x 30-Sep-02 A4 x x x 6-Nov-02 A4 x x x 7-Nov-02 A9 x x x 11-Nov-02 A9 x x x 12-Nov-02 A9 x x x 13-Nov-02 A4 x x x 14-Nov-02 A4 x x x 10 ECN-C

11 2.3 Emissions-factor calculation scheme Measurements presented here have been used to investigate the vehicular emissions of (ultrafine) particulate matter on motorways. The estimation of these factors follows the method proposed by Bloemen and van Putten (1998). A concise description of this scheme is repeated here. First, the assumptions made in this method are summarised. The first assumption is that the background concentration is representative for the composition of pollutants upwind the motorway. Secondly, the composition of the air downwind reflects the composition of the background composition mixed with the different composition of emissions from the various types of vehicles. Thirdly, the composition of emissions from light duty vehicles (LDV) differs significantly from heavy-duty vehicles with respect to particulate matter, carbon monoxide and nitrogen oxides. In this method the determination of the PM-emission factors depend on the emission factors for CO and NO x. Hence, the accuracy of the PM-emission factors rely on the accuracy of the emission factors of these gaseous pollutants. The contribution to the concentration of a pollutant for which traffic is the major source (C t ) is assumed to be the superposition of the background level (C bg ) and the contribution due to the passing cars: C t = C measured - C bg. [1] The contribution to the measured concentrations due to the traffic can also be described by: C t = N E f f disp C t /E f = N f disp, [2] where N is the number of cars passing by in a given period, E f the emission factor and f disp a dispersion factor explaining the dilution of the emitted pollutants. Light-duty (LDV) and heavyduty vehicles (HDV) emit pollutants in different amounts. It is necessary to distinguish between these types of vehicles: C t = N [f h E h + (1 - f h )E 1 ]f disp, [3] where f h is the fraction of HDV, E h the emission factor of HDV and E 1 of LDV. In the case of nitrogen oxides (NO x ) and carbon monoxide (CO) the equations become: NO xt = N [f h E h NOx + (1 - f h ) E 1 NOx ] f disp [4] and CO t = N [f h E h CO + (1 - f h ) E 1 CO ] f disp, [5] where E h NOx and E 1 NOx are the NO x -emission factors for HDV and LDV, and E h CO and E 1 CO those for CO; the two unknown variables in these two equations are f disp and N. Eliminating these unknowns by rearranging [4] and [5] gives the fraction of HDV: f h = (E CO h - E CO ) 1 E NO 1 x (NO xt /CO t (NO xt /CO t ) ) + E NO h x - E NO 1 x [6] By analogy the emission factor of traffic for particulate matter can be described by: ECN-C

12 E t PM = f h E h PM + (1 - f h ) E 1 PM [7] and in relation with the NO x emission factors (Eq.2) by: PM t /E t PM =NO xt /E t NOx E t PM = [f h E h NOx + (1 - f h ) E 1 NOx ] PM t /NO xt. [8] Rearranging [7] and [8] gives another expression for f h : f h = E PM h - E 1 PM 1 E NO x + PM t /NO xt (E NO 1 x - E PM 1 - E NO x h ) PM t /NO xt. [9] Combining the equations [6] and [9] eliminates f h ultimately yields Eq[10] which is the equation of a linear regression line: (PM t /CO t ) = p + q (NO xt /CO t ). [10] The parameters p and q can be calculated using least-square regression using the measured concentrations of PM t, NO xt and CO t. Knowing p and q, the emission factors for particulate matter due to traffic are then extracted as (using Eq[2]): E PM h and E PM l = = p E CO h p E CO l + + q E NOx h q E NOx. l [11] The expression for the number of particles (n) constituting a mass fraction PM can be obtained similarly. It is assumed that the dispersion of particles in air is the same as that of gasses (which is true for particles with sizes less than 1 µm). The expression for the emission factor of number concentrations then becomes: NO E n CO q ' E x h = [p ' E h + ]*10 h 12 and NO E n CO q ' E x l = [p ' E + ]*10 l l 12. [12] The factor is needed to get the units right (number per km per vehicle). In words, these equations provide the possibility of estimating the emission factors for particulate matter by using the vehicular contributions to the measured concentrations of PM, NO x and CO, using the known emission factors for CO and NO x. The effects of actual traffic intensity and specifics are incorporated in the measured concentrations of NO x and CO. 12 ECN-C

13 3. RESULTS AND DISCUSSI ON 3.1 Determination of ultrafine particle number emissions factors Emission factors for both CO and NO x in case of light-duty vehicles (LDV) and heavy-duty vehicles (HDV) are those currently in use in CAR-II. For the year 2002 these factors are summarised (in g/km/vehicle) in table 2. Table 2 Emission factors CO and NO x Velocity NOx CO (km/h) LDV HDV * * The emission factor for HDV here is the weighted average of the emission factors for Medium Duty Vehicles (34%) and Heavy Duty Vehicles (66%) (personal communication Teeuwisse, 2003). For both data sets acquired along the A9- and A4-motorway linear regression analyses have now been performed according to Eq.[10] in the previous section. For all components involved, background concentrations (determined upwind) have first been subtracted in order to obtain the vehicular contributions. Results are given in figures 1 and 2. Data Y = *X 95% Confidence (Data) 95% Confidence (Line) nuf/co NOx/CO Figure 1 Regression analyses for the A4-data set ECN-C

14 Data Y = *X 95% Confidence (Data) 95% Confidence (Line) n_uf/co NOx/CO Figure 2 Regression analyses for the A9-data set. Based on the combined A4 and A9 measurement data the result of the linear regression analysis (Eq.(10])) is given in figure 3. Data Y = *X 95% Confidence (Data) 95% Confidence (Line) nuf/co NOx/CO Figure 3 Regression analysis for the combined data set. Regression coefficient is now 0.95 In all three computations r 2 is around 0.9. By use of the computed regression coefficients (p and q in Eq.[10]) and the CO and NO x emission coefficients in Table 2 the average emission factors for the ultrafine fraction are now estimated. 14 ECN-C

15 For the results see Table 3; the estimations are given for two situations: 1) an average driving speed of 100 km/h for LDV and 80 km/h for HDV, and 2) average driving speeds of 120 km/h (LDV) and 90 km/h (HDV), respectively. The range in these factors (4 th column in Table 2) is determined by considering the maximum and minimum factors computed for the A4- and A9-motorways separately. Table 3 Emission factors for ultrafine particle numbers Emission factors PM0.1 Velocity Average ( km -1 ) Situation 1 Situation 2 Range ( km -1 ) LDV HDV LDV HDV In conclusion, after averaging results for the 8 measurement days, emission factors for particle number were found to be in the range of km -1 for light-duty vehicles and km -1 for high-duty vehicles. Based on considerations given by Bloemen et al. (1998) the overall error in the emission factors is believed to be 40-50%. ECN-C

16 3.2 Change in concentrations and size distributions of ultrafine particulate matter with increasing distance The measurements presented in this section were conducted between September 15 and November 14th, 2002 (see table 1). Information about (average) traffic densities was obtained from the Ministry of Transport, Public Works and Water Management. On the A9- and A4- motorways traffic is primarily is dominated by gasoline-powered cars and light trucks: less than 15% of the vehicles were heavy-duty diesel trucks. The occurrence of traffic jams (diminishing speed and emissions) was registered: periods of congestion have been discarded in the data set. The measurements presented in the next sections include total particle number concentrations as well as ultrafine particle size distributions upwind and at various distances downwind from the motorway Wind effects Wind speed and direction at the various measurement locations were averaged and logged over every 1-min interval of each sampling period. The accuracy of the wind characteristic is important, because it allows data from different days to be averaged together. For example, Hitchins et al. (2000) found a different characterisation of changes in total particle number concentration with increasing distances from a major road when the wind was blowing directly from, parallel to, or away from the sampling location. They observed no trend when the wind was blowing away from the sampling location. In this study it is found that both wind speed and wind direction play an important role in the determination of the characteristics of ultrafine processes near motorways. As an example, in figure 4 is given the averaged total particle number concentrations as measured by the CPC3022 on Thursday November 7th, located 70 and 124 m downwind of the A9-motorway, for various wind speeds. The time frames for these measurements were 13:52-14:26 and 14:40-15:06, respectively; the average wind direction 335 (standard deviation: ±5 ) and 336 (±3 ). A linear regression line, equation and R 2 value for both periods are included in the figure. The CPC was programmed to archive total particle number concentrations at 1-min intervals in synchronisation with the averaging time of the meteorological data. 16 ECN-C

17 A9: 7 November particle number concentration (cm -3 ) y = -8690x R 2 = 0.57 y = x R 2 = m/s 70 m 125 m Lineair (70 m) Lineair (125 m) Figure 4 Total particle number (measured by CPC3022) located 70 and 124 m downwind versus wind speeds (November 7th, 13:52-14:26 and 14:40-15:06). Bars indicate 1 standard deviation As can clearly be observed in the figure, higher-speed winds cause more atmospheric dilution, and thus lead to smaller particle-number concentrations. It can further be observed that total particle number concentration measurements near the A9-motorway are in general dependent on the distance to the motorway, as was expected; this will be discussed in more detail in the next paragraph. veh/h hour Figure 5 Traffic intensity on the A9 not far from our experimental site for Thursdays in November 2002 ECN-C

18 Obviously, a different traffic intensity (as well as meteorological parameters like temperature and humidity) during the periods of measurement may influence the observed differences. Indeed, figure 5 suggests that in the course of hour 14 (14:00-15:00) the intensity will have increased. No correction is made for this effect, as the exact number on traffic intensity per minute or hour was not available Changes in particle numbers with increasing distance Figure 6 shows all available particle number data (CPC3022) as collected on the four measurement days aside the A9-motorway. Clearly, number concentrations are an exponential decaying function of the distance to the motorway, confirming what is already observed in figure 4, and indicating that traffic is the major contributor to the fine and ultrafine particles in air at this location CPC3022 number concentrations A9 y = 76749e x R 2 = 0.48 cm m Figure 6 Particle number concentrations (CPC3022) along the A9-motorway for each available measurement period collected on September 26, and November 7, 11 and 12, as function of distance to the curb As discussed in the previous section in relation to the same figure 4, wind speed affects the particle number concentrations in air. Therefore, in order to give a better comparison, concentrations were corrected for possible effects of a varying wind speed; for example, in the case of November 7 for the period 13:52-14:26, and on the distance of 70 m, the instantaneous measured number concentrations were recalculated according to the following equation: C* = C N, N V where C N * is the corrected particle number concentration, C N is the CPC-measured average particle number concentration, the intercept of the regression line in figure 4, is the particle number concentration at 4 m/s, the slope of the regression line, and V the wind speed in m/s as measured by the meteorological logging device. Similar corrections were performed for each of the other data sets. Another correction is needed for the effect of the prevailing wind direction during the various periods of measurement. To this purpose, an effective wind-dependent distance had to be 18 ECN-C

19 determined for each period that was measured. Instead of the distance from the measurement location perpendicular to the motorway (as was the case in figure 6), the distance is taken in the direction of the wind vector, which is realistic for the distance effectively covered by particulate matter from the motorway to the measurement location. The result of this exercise is given in Figure 7 where also averaging took place over (almost) equal distances. From the picture it becomes clear that influence of the motorway on the total number of particles is still detectable over downwind distances that are in the order of 500 m. The number variability is largest near the motorway; differences are smoothed out with increasing distance. CPC3022 number concentrations after correction for wind speed and wind direction y = 67687e x R 2 = cm Figure m Averaged particle number concentrations (CPC3022) along the A9-motorway corrected for wind speed and direction as function of distance to the curb Change in ultrafine particle distribution with increasing distance In this study of ultrafine particle distributions along the A9-motorway data from four one-day measurement campaigns are discussed: September 26th, November 7th, 11th and 12th (2002). Average wind directions these days were 300, 311, 223 and 185, respectively; on November 12th the wind direction is nearly parallel to the orientation of the motorway (170 ). Results are discussed as function of the effective distance; no attempt is made to correct for wind speed and traffic intensity. In the data collection campaigns two CPC-SMPS combinations were used: a CPC3022-SMPS being capable of measuring particle sizes between 13 and 580 nm, and the CPC3025-SMPS measuring sizes within the range nm. Results are interpreted in more detail for September 26th. For this day, the figures 8a-f depict the ultrafine particle size distributions at the (effective) distances of 12, 40, 62, 86 and 124 m downwind, and at 150 m upwind from the A9-motorway. The horizontal axis represents particle size on a logarithmic scale, while the vertical axis represents number concentrations. When data are plotted in this way, the area under the curve gives the number of particles in that size range when the y-axis is on a linear scale. The distributions are the result of averaging over the available sampled periods at a specific distance. In practice, the averaging procedure involves morning as well as afternoon periods of one day. As can be seen in figures 8a-f, the ultrafine particle-size distributions at larger distances from the motorway have changed markedly with respect to the location closest to the motorway. First, absolute numbers decrease, and second, the shape of the distribution gradually changes going further away from the motorway. ECN-C

20 As shown in figure 8a for the nearest sampling location, 12 m downwind from the motorway, there are two distinct modes. The first one is suggested to be formed on the left side below the range of the SMPS (maximum below the particle size of 13 nm), and the second one having a clear modal peak at 25 nm. The presence of another (weak) mode is only slightly suggested starting at about 40 nm. In literature The mode for the smallest particle sizes, with a peak concentration of at least cm -3 (see figure 8a), is only partially covered by the instrumental measurement range; nevertheless, the shape suggested looks rather similar to that previously reported by Zhu et al. (2002) for vehicle emissions near a major highway (2002), and does not contradict direct laboratory measurements by Kittelson et al (1998). The appearance of the second peak at 25 nm is not definitely understood. It may the same as the one reported by Kittelson et al. (2001) in the case where a majority of diesel trucks where in the vicinity of the measuring apparatus. The maximum number concentration ( cm -3 at 12 m, see figure 8a) at a particle diameter of 13 nm has dropped to cm -3 at 124 m (figure 8e, again at 13 nm). The considerable decrease in particle number concentrations at (and probably below) diameters of 13 nm will be caused by several atmospheric aerosol particle mechanisms that enhanced small-particle loss, like diffusion to surfaces and coagulation. The smaller the particle, the greater its diffusion coefficient and its Brownian motion (particles of 10 nm diffuse about 80 times faster than do particles of 100 nm). In addition, when two small particles collide because of their Brownian motion (coagulate), they form a bigger particle. Thus, coagulation reduces number concentrations and shifts the size distribution to larger sizes. Because of atmospheric dilution, the number concentration for all sizes drops with increased distance from the motorway. On average, number concentrations at 124 m dropped to approximately 50-60% of its original value at 30 m, as shown in figure 8e. This result is in agreement with Hitchins et al. (2000), who found that particle number concentrations decreased by 50% at m. Ultrafine particle concentration measured at 124 m downwind is still 3 times larger than the number measured at the background location 150 m upwind of the motorway: cm -3 (see figure 8f). The maximum number concentration that was observed near the motorway ( cm -3 ) was 5-6 times greater than that at the background location. This indicates that people who live, work or travel within 100 m downwind of a major traffic source like the A9 will have much higher ultrafine particle exposure that those who live further away from those sources. The trend of size distribution and number concentration with increasing distances is shown in figure 9, with a common scale for the vertical axis. According to figure 9, number concentrations for smaller particles (<50 nm) dropped significantly with increasing distances from the motorway, but for larger ones (>100 nm), number decreased only slightly. This again suggests that coagulation is more important than atmospheric dilution for ultrafine particles and the reverse is true for large particles. Researchers who have conducted experimental studies on the transportation of vehicle particle emissions in the atmosphere have concluded that the rapid dilution of the exhaust plume made coagulation unimportant (Shi et al., 1999; Vignati et al., 1999). However, in this study, the observed size distribution changes suggest that coagulation is not negligible. The size distributions measured at the other three days reasonably agree with those of September 26th. Note that the peak value at 25 nm present at November 7th and 11th coinciding with one at September 26th, and absent at November 12th when the wind direction is almost parallel with the motorway s orientation. 20 ECN-C

21 a) 12 m dn/dlogdp (cm-3) particle diameter b) 40 m dn/dlogdp (cm-3) particle diameter c) 62 m dn/dlogdp (cm-3) particle diameter (nm) ECN-C

22 d) 86 m dn/dlogdp (cm-3) particle diameter (nm) e) 124 m dn/dlogdp (cm-3) particle diameter (nm) f) background dn/dlogdp (cm-3) particle diameter Figure 8 Particle size distributions at different sampling distances from the A9-motorway. Effective distances are (a) 12 m downwind, (b) 40 m downwind, (c) 62 m downwind, (d) 86 m downwind, (e) 124 m downwind, and (f) 150 m upwind 22 ECN-C

23 A9: September dn/logd p (cm -3 ) m 40 m 62 m 86 m 124 m background particle diameter (nm) Figure 9 Ultrafine particle size distributions on September 26th A9: November 7 dn/dlogdp (cm -3 ) m 71 m 126 m particle diameter (nm) Figure 10 Ultrafine particle size distributions on November 7th ECN-C

24 A9: November dn/dlogdp (cm -3 ) m 81 m 162 m background particle diameter (nm) Figure 11 Ultrafine particle size distributions on November 11th A9: November dn/dlogdp (cm -3 ) m 236 m 473 m background particle diameter (nm) Figure 12 Ultrafine particle size distributions on November 12th Based on figures 7 to 12, it is clear that vehicle-emitted particles of different size ranges behave differently in the atmosphere. Thus, figure 13 was made to illustrate the decay of particle number concentrations in four size ranges: nm, nm, nm and nm. Selected here for study are the data from September 26. Particle number concentrations in each 24 ECN-C

25 size group were obtained by adding the measured number concentrations in each SMPS size bin within the corresponding size range. As shown in figure 13, total particle number in the size range accounts for approximately 43% of the total ultrafine particle number concentration while for the combined range nm this is some 74% (of the total number) particle number (cm -3 ) nm nm nm nm distance donwind from motorway Figure 13 Particle number concentrations for different size ranges as a function of distance to the A9-motorway on September 26, 2002 Number concentrations in the first two size classes barely change over the first m but further on numbers drop considerably. The "saddles" for the range nm can be explained by particles in the smaller-size classes (<13 nm), present in large numbers (see figure 9), coagulating with these particles increasing size and concentration, thereby partially compensating for the atmospheric dilution effects. This result is consistent with the previous discussion, i.e. that coagulation plays a significant role in vehicle-emitted ultrafine particle atmospheric behaviour. After 40 m the number "decay" in the two smallest size classes becomes apparent while number concentration of particles larger than 100 nm did not change very significantly when the distance from the motorway increased: the change is practically zero after 75 m. Apparently, atmospheric dilution is now dominant. ECN-C

26 26 ECN-C

27 4. SUMMARIZING CONCLUSIONS The first objective was to obtain estimates for emission factors as well as size distributions with an emphasis on the ultrafine particle fraction (PM 0.1 ) of Dutch roadway aerosols. To this purpose, data were collected along two major motorways during daytime conditions in the period September-November Measurements took place upwind at one site and downwind at various distances from the road. The primary instruments used used to characterise on-road particulate matter emissions were CPC's in combination with SMPS. In addition, two gas analysers, CO and NO x monitors, measured gaseous emissions along a motorway. Emission factors for the ultrafine range (<100 nm) were determined along two motorways (A4 and A9). The method used was proposed by Bloemen and van Putten (1998) using synchronised measurements of the vehicular contribution to particulate matter, CO and NO x, and emission factors of the latter two components. In this way, detailed information about mixed-fleet characteristics (traffic intensity) can be circumvented but it has to be stipulated that results rely on the accuracy of the CO and NO x emission factors. After averaging results collected during 8 days, the emission factors for ultrafine particle numbers were calculated to be in the range of km -1 for light-duty vehicles and km -1 for high-duty vehicles. The overall error is expected to be 40-50%. Particulate matter number concentrations directly besides the motorways roughly vary between 10 4 to 10 5 particles per cm 3 ; the majority of particles measured by the SMPS is less than 50 nm in diameter. The absolute number varies for different samples and days that can be explained by meteorology (wind speed and direction) and traffic intensity. In recent literature (Kittelson et al., 2001) an association is found between the speed on the motorway, and the number concentration and size: the higher the speed the greater the particle concentration and the smaller the size. At high vehicular speeds, the particulate matter emissions increase because of enhanced engine load and fuel consumption. In general, two modes are observed here in the size distributions downwind of the motorway. The first one, only partially covered by the SMPS measurement range, shows a maximum below a particle diameter of 12 nm and only partially covered by the instruments. This 'nuclei' mode is usually formed from volatile precursors as the car exhaust cools within seconds (gas-to-particle conversion after leaving the tail pipe). A second mode is found at 25 nm for which the explanation is not known (yet) with certainty but may be due to the passage of diesel trucks (Kittelson et al. 2001). At larger downwind distances (>60 m) the distributions change markedly with respect to the closest location near the motorway. First, the nuclei mode decays rapidly. Second, the shape of the distribution indicates ageing of the aerosol by showing saddle points with time scales in the order of minutes. The considerable decrease in particle numbers for particles smaller than 50 nm can be explained by mechanisms like coagulation and diffusion. When measurements on aerosols in mixed traffic are performed in the way presented here, problems associated with trying to characterise a plume from a single source are eliminated. However, this study was the first of its kind under Dutch circumstances. More data needs to be collected to improve and validate these estimates. Further studies has to be done to provide a more complete set of data by means of which the role of vehicular intensity and car speed, wind speed and distance on the levels of particle numbers and size distributions can be determined and validated. ECN-C

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29 5. REFERENCES Bloemen, H. and E. van Putten (1998): Mobile emission factor determination through ambient air monitoring - MEDAM project, RIVM report no , Bilthoven. Hitchins, J., L. Morawska, R. Wolff, D. Gilbert (2000): Concentrations of submicrometer particles from vehicle emissions near a major road. Atmos. Environ. 34, Hinds, W.C. (1982): Aerosol Technology, Wiley and Sons, pp Kittelson, D.B. (1998): Engines and nanoparticles: a review, J. Aerosol Sci. 29, Kittelson, D.B., W. Watts, J. Johnson (2001): Fine particle (nanoparticle) emissions on Minnesota highways, Final report MN/RC , University of Minnesota, May Shi, J.P., A.A. Khan, R.M. Harrison (1999): Measurements of ultrafine particles concentration and size distribution in the urban atmosphere, Sci. Total Environ. 235, Vignati, E.W., R. Berkowicz, F. Palmgren, E. Lyck, P. Hummelshoj (1999): Transformation of size distributions of emitted particles in streets, Sci. Total Environ. 235, Zhu, Y., W.C. Hinds, S. Kim and C. Sioutas (2002): Concentration and size distribution of ultrafine particles near a major highway, J. Air &Waste manage. Assoc. 52, ECN-C

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31 APPENDIX A Measurement locations in the province of Noord Holland of the Netherlands ECN-C

32 Geographical presentation of the surroundings of the measurement location along the A9-motorway 32 ECN-C

33 Geographical presentation of the surroundings of the measurement location along the A4-motorway ECN-C

34 34 ECN-C

35 APPENDIX B September 26 A9 perpend. effective number mass meas. distance distance CO NO NO2 NOx CPC-3022 total total PM 0.1 PM 0.5 no. meter meter PPM PPM PPM PPM n/cm^3 n/cm^3 ug/m^3 ug/m^3 ug/m^3 averages sep-02 12: sep-02 13: sep-02 14: sep-02 15: sep-02 15: sep-02 16: sep-02 16: sep-02 16: sep-02 17: sep-02 18: st dev sep-02 12: sep-02 13: sep-02 14: sep-02 15: sep-02 15: sep-02 16: sep-02 16: sep-02 16: sep-02 17: sep-02 18: ECN-C

36 November 7 Las-X Las-X CPC-3025 CPC-3022 SMPS SMPS Wind Wind A9 distance serie number Mass average max. total total speed direction code m nr. n/cm^3 ug/m^3 n/cm^3 n/cm^3 n/cm^3 ug/m^3 m/s degrees average stdev ECN-C

37 November 11 A9 Date StartTime StopTime mcpc Stand.Dev WindDir WindSpeed AvgNO2 StdNO2 AvgNO StdNO AvgNOx StdNOx OzonAvg OzonStDev CoAvg CoStDev :00 12: :05 13: :45 14: :25 14: :05 15: :45 16: November 12 A9 Date StartTime StopTime mcpc Stand.Dev WindDir WindSpeed AvgNO2 StdNO2 AvgNO StdNO AvgNOx StdNOx OzonAvg OzonStDev CoAvg CoStDev :57 11: :50 12: :25 12: :45 12: :05 13: :25 13: :45 13: :05 14: :25 14: :45 14: :05 15: :35 15: ECN-C