Determination of mean particle size using the electrical aerosol detector and the condensation particle counter: Comparison with the scanning mobility particle sizer

Aerosol Science 39 (2008) 19 – 29 www.elsevier.com/locate/jaerosci

Determination of mean particle size using the electrical aerosol detector and the condensation particle counter: Comparison with the scanning mobility particle sizer Brian P. Franka,∗ , Seth Saltiela , Olga Hogrefeb , Jillian Grygasa , G. Garland Lalab a NYS Department of Environmental Conservation, Bureau of Mobile Sources and Technology Development, Division of Air Resources,

625 Broadway, Albany, NY, 12233, USA b Atmospheric Sciences Research Center, University at Albany, State University of New York, 251 Fuller Rd., Albany, NY 12203, USA

Received 13 April 2007; received in revised form 4 September 2007; accepted 26 September 2007

Abstract Two methods for determining mean particle size of an ultrafine particle size distribution were compared using a combustion aerosol source. The first utilized the scanning mobility particle sizer (SMPS), while the second combined measurements from the electrical aerosol detector (EAD), which measures total aerosol length (mm/cm3 ), and the condensation particle counter (CPC). Poor agreement was found between EAD/CPC and SMPS data when mean particle size was used as a basis for comparison but good agreement was found between EAD and SMPS data when total aerosol length was used as a basis for comparison. The largest contribution to the poor correlation between the two methods was the difference in concentration measured by the SMPS and the CPC. This paper examines the influence of both diffusion losses and nanoparticle aggregate mobility on these results, which also suggest that morphology, charging and composition may be contributing factors. 䉷 2007 Elsevier Ltd. All rights reserved. Keywords: Ultrafine; Electrical aerosol detector; Scanning mobility particle sizer; Diffusion losses; Nanoparticle aggregate mobility

1. Introduction Determining the particle size distribution for ultrafine particle sources with transiently varying behavior, such as a mobile source being subjected to a dynamometer testing cycle, presents a technical challenge due to the rapidly changing particle behavior of the source, typically in the range of 1 Hz. The scanning mobility particle sizer (SMPS), which is widely used to determine particle size distributions in the ultrafine range, requires several minutes to perform a full scan of particle sizes and is thus too slow for such sources (Russell, Flagan, & Seinfeld, 1995; Wang & Flagan, 1990). The engine exhaust particulate spectrometer (EEPS; Johnson, Pöcher, Mirme, & Kittleson, 2003) is capable of measuring particle distributions over this size range at a sampling rate of 1 Hz. However, the EEPS is a relatively new instrument, which has only been deployed for measurement of a limited number of particle sources to date (Kittelson, Watts, Johnson, Rowntree et al., 2005; Liu, Thurow, Caldow, & Johnson, 2005). ∗ Corresponding author. Tel.: +1 518 402 8355; fax: +1 518 402 9035.

E-mail address: [email protected] (B.P. Frank). 0021-8502/$ - see front matter 䉷 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaerosci.2007.09.008

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Another instrument which has the potential for performing such measurements is the electrical aerosol detector (EAD). The EAD employs diffusion charging of aerosol particles and their subsequent detection by sensitive electrometer (Johnson, Kaufman, & Medved, 2002). The inlet flow is split into a sample flow and a second flow which is filtered and passed through a corona charger to create an ion flow. These flows are recombined in a counter-flow mixing chamber where diffusion charging of the aerosol particles occurs (Medved, Dorman, Kaufman, & Pöcher, 2000). Excess charges are removed in an ion trap, and the remaining charge carried by the aerosol particles is then measured in an electrometer in units of pA. This diffusion charging produces a nearly linear relationship between particle diameter and the number of units of charge acquired by particles in the 10 nm–1 m range. The net current measured by the electrometer (taking internal particle losses into account) is proportional to the 1.133 power of the particle diameter (Fissan, Neumann, Trampe, Pui, & Shin, 2007; Johnson et al., 2002). The EAD reports this value as the aerosol diameter concentration, or total aerosol length, in units of mm/cm3 . The total aerosol length is defined as the total length of a chain consisting of all particles detected within a 1 cm3 volume, and is equivalent to d 1 weighting, falling between number concentration (d 0 ) and surface area (d 2 ). As a relatively new parameter, the relationship of total aerosol length to other particle measurement metrics is not yet well understood and it does not yet fit in well with our conceptual understanding of particle behavior. A good correlation has been found between total aerosol length and model predictions of the penetration of aerosol particles into the respiratory system (Fissan et al., 2007; Wilson et al., 2004). One may conceptualize total aerosol length as the product of particle number concentration and mean particle diameter. Therefore, if the total aerosol length is divided by the particle number concentration (such as that determined by a condensation particle counter, CPC), the resulting value should be the mean of the particle size distribution. This assumes, of course, that the particle size distribution is unimodal and completely captured. If such a relationship holds true, then simultaneous EAD and CPC measurement could determine the mean of a particle size distribution without the need for an SMPS. Further, since both instruments have a sampling rate of 1 Hz, they should be able to make such measurements for transiently varying particle sources which cannot be measured by the SMPS. The EAD and CPC have low sample flowrates (2.5 and 1.5 lpm, respectively) relative to the EEPS (10 lpm), as well as significantly lower weights and smaller dimensions. This makes them promising candidates for use in applications such as portable emissions monitoring or in chase vehicles (Kittelson, Watts, Johnson, Remerowki et al., 2005). While benchtop CPC models were used in the experiments described here, the Model 3007 isopropanol portable CPC (TSI Inc., Shoreview, MN) could potentially also be used for such measurements. Simultaneous measurements of ambient aerosol using the EAD, CPC, and SMPS were performed during the PM2.5 Technology Assessment and Characterization Study—New York (PMTACS—NY). PMTACS—NY is one of several US Environmental Protection Agency “Supersites” intended to provide enhanced measurement data on the chemical and physical composition of PM and its associated precursors. A data set of ca. 1200 simultaneous measurements for these instruments was obtained over a period of four weeks during the PMTACS—NY Winter 2004 Intensive Campaign. Evaluation of this data showed that the EAD and CPC were capable of responding to transients in ambient particle concentration, but that the mean determined by the EAD/CPC method did not correlate well with that measured by the SMPS (Frank et al., 2005; Hogrefe, Lala, Frank, Schwab, & Demerjian, 2006). This paper describes the results of laboratory experiments conducted with a combustion aerosol particle source in order to further investigate the relationship between these two methods of determining the mean particle size. 2. Experimental method Since the initial objective of the experiments described here was to further understand the results obtained during the PMTACS—NY Winter 2004 Intensive Campaign, all particle measurement instruments used in these experiments employed the same sampling conditions and operating parameters that were also employed during PMTACS—NY. In particular, the acquisition periods for the EAD and CPC, the scan rate for the SMPS, and the sheath and sample flowrates were chosen to be the same as those employed during ambient measurement. 2.1. Electrical aerosol detector The EAD employed in this study is a Model 3070A manufactured by TSI Inc. (Shoreview, MN). The inlet flow is fixed at 2.5 lpm, which is split into a 1.5 lpm sample flow and a 1.0 lpm flow which is filtered and passed through an

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Fig. 1. Experimental setup for the CAST generator experiments.

ionizer. As described above, these flows are recombined in a mixing chamber where charging of the aerosol particles occurs. Excess charges are removed in an ion trap, and the remaining charges carried by the aerosol particles are then measured in a sensitive electrometer. Charges are measured in pA and reported by the EAD as total aerosol length in units of mm/cm3 . Data was acquired by the EAD at 1 Hz and averaged every 59 s in order to duplicate the parameters employed during PMTACS—NY. 2.2. SMPS and CPC The SMPS used in these experiments was comprised of an electrostatic classifier (ESC, Model 3080, TSI Inc., Shoreview, MN), a long differential mobility analyzer (LDMA, Model 3081, TSI Inc., Shoreview, MN) and a CPC (Model 3025A, TSI Inc., Shoreview, MN). Sample and sheath flowrates were set at 0.6 and 6.0 lpm (except where noted otherwise), and the 0.457 cm impactor was used. Each sample consisted of two scans, each 2.5 min in duration, from 10.2 to 414 nm. While these conditions were chosen to duplicate the parameters employed during the PMTACS—NY study, some smearing of the SMPS number distributions may result due to the finite response time of the CPC (Collins, Flagan, & Seinfeld, 2002; Rodrigue, Ranjan, Hopke, & Dhaniyala, 2007) and is reflected in the variability of the SMPS mean particle sizes which are reported below. Multiple charge correction was also applied to all SMPS scans. The CPC used was the Model 3022A (TSI Inc., Shoreview, MN) with sample flow adjusted to 0.6 lpm through the addition of a flow compensator with an HEPA filter. Data was acquired by the CPC at 1 Hz and averaged every 59 s in order to also duplicate the parameters employed during PMTACS—NY. 2.3. Combustion aerosol generation Combustion aerosol was generated by means of the combustion aerosol standard system (CAST, Matter Engineering AG, Wohlen, Switzerland). Propane was used as the fuel and combined with flows of N2 and oxidation air regulated by mass flow controllers (MKS Instruments, Wilmington, MA) as described below and fed to a burner. Above the burner, additional air and N2 flows dilute the particles generated by the burner and isolate them from the walls of the generator outlet. In these experiments, gas flows to the burner were varied while all gas flows after the burner were kept constant at 7.5 lpm for the quench N2 and 20 lpm for the dilution air. As shown in Fig. 1, samples were drawn from the outlet of the CAST via a valveless sample pump (Model QD, FMI, Syosset, NY) and into a custom-built minidiluter, where they were diluted with dried and filtered dilution air at a ratio of 100:1. Diluted samples for the EAD, CPC and SMPS were then drawn from the minidiluter at the sample flowrate for each individual instrument. In controlling the gas flows to the CAST burner, it should be noted that the combustion air flow consists of both O2 and N2 . The factors that control the characteristics of particle formation in the CAST are the fuel/O2 and fuel/N2 ratios, which must be calculated by taking the mixed composition of oxidation air into account. By varying these two ratios, the CAST can produce particle size distributions with varying mean particle diameters as measured by the SMPS and a calibration curve can be generated for each set of conditions. For each fuel/O2 ratio there is a minimum and maximum fuel/N2 ratio beyond which combustion cannot be sustained. Particle number concentration also changes as a function of these ratios. A fuel/O2 ratio of 0.213 with fuel/N2 ratios ranging from 0.038 to 0.053 was chosen as most suitable for

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these experiments since this combination of flows encompassed the widest range of particle sizes (from 13 to 183 nm) that also included particles < 50 nm. The distributions along this curve were all found to be unimodal and reproducible. 2.4. Other aerosols In addition to the combustion aerosols generated by the CAST and the ambient aerosols measured during the PMTACS—NY 2004 Winter Intensive Campaign, aerosols from three other sources were examined in this study: (1) Na2 SO4 and sucrose particles generated in the laboratory by spray atomization in a slow-flow chamber (Hogrefe, Drewnick, Lala, Schwab, & Demerjian, 2004) and (2) particles sampled from a diesel generator exhaust stream and diluted by minidiluter in the same manner as the CAST particles, and laboratory-generated aerosols of water soluble primary organic acids that are of environmental significance (pinonic, malonic, glutaric and palmitic acids, and levoglucosan) (Hogrefe et al., 2007). 3. Results and discussion Simultaneous measurements were taken by the SMPS, EAD, and CPC for each set of fuel/O2 and fuel/N2 ratios as described above; five sets of data were collected at each condition. The EAD and CPC measurements were combined to yield the mean particle size (106 * EAD total aerosol length (mm/cm3 )/CPC concentration (cm3 )) at each condition and then correlated to the SMPS number-weighted mean particle size to yield Fig. 2. The relationship between these two methods of determining the mean particle size is nonlinear, with consistently larger means yielded by the SMPS than by the EAD/CPC, except at 13 nm. The behavior of the aerosol at 13 nm may be due to the morphological nature of the aerosol at this size (discussed further below), or may simply be due to measurement uncertainties. The mean particle diameter of 13 nm is close to the lower size limit of 10 nm for both the LDMA and the EAD (Medved et al., 2000), and thus the lower portion of the size distribution may have been both excluded by the SMPS and incorrectly measured by the EAD at this point. The variability in the SMPS mean values is due in part to smearing of the distribution due to scan speed effects as discussed above. The variability in the EAD/CPC mean values is due in part to the nonlinearity of the charger efficiency curve (Medved et al., 2000), resulting in different EAD/CPC means for different widths of the size distribution at the same SMPS mean. However, the magnitude of both effects is small compared to the overall discrepancy between the EAD/CPC and SMPS methods. Since good agreement should be expected in theory when applying these methods for particle size distributions of this nature, some other difference between instrumentation must account for the behavior in Fig. 2. However, there are

Fig. 2. Correlation curve for EAD/CPC calculated mean particle size and SMPS mean particle size, with corrections for SMPS diffusion losses and aggregate mobility. Error bars correspond to the standard deviation of the mean values.

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Fig. 3. NaCl aerosol penetration through electrostatic classifier impactor and neutralizer as a function of particle diameter for sample flow rates from 0.3 to 1.0 lpm.

two factors which can significantly affect the performance of the SMPS which must first be accounted for. The first is diffusion losses in the SMPS. These losses will have a greater impact on the measured concentration (discussed in further detail below) than on the mean particle size, but can also cause the SMPS mean to shift towards larger particle sizes. Such diffusion losses in the SMPS have also been observed in independent experiments conducted with polydisperse NaCl aerosol. For these experiments, NaCl aerosol was generated in a range of particle sizes by means of an atomizer and dilution system (Hogrefe et al., 2004) and measurements were made at sample flowrates of 0.3, 0.6, and 1.0 lpm. Measurements were also made with the sample inlet both through the entire classifier system flow path, i.e., the impactor, neutralizer and DMA, and through the DMA alone. Evaluation of the percentage penetration through the impactor and neutralizer for various particle diameters (Fig. 3) indicates that losses occur in the impactor–neutralizer portion of the classifier. The variability in the data for each flow rate is the result of directly dividing the upstream and downstream SMPS concentrations for the different size channels. This is consistent with the flow path within this portion of the classifier compared to the flow path in the DMA. Further, such losses only occur for the slower flowrates and increase with decreasing particle size, as is expected for diffusion losses. Other investigators have also found significant diffusion losses occurring within the DMA portion of the SMPS (Reineking & Porstendörfer, 1986; Rodrigue, Ranjan, Hopke, & Dhaniyala). A diffusion loss correction for the TSI SMPS has recently been determined (TSI, 2006a), which is intended to compensate for diffusion losses in the SMPS for the size regime less than 100 nm. These diffusion losses are characterized in terms of total penetration, Ptotal , which is the product of the penetration for five composite flow paths through the SMPS, i.e., Ptotal = P1 P2 P3 P4 P5 ,

(1)

where P1 is the penetration through the impactor inlet; P2 is the penetration through the bi-polar neutralizer and the internal plumbing; P3 is the penetration through the tubing to the DMA and CPC; P4 is the penetration through the DMA; and P5 is the CPC counting efficiency including penetration inside the CPC, activation efficiency, optical detection efficiency, any losses incurred at the inlet, etc. (Stolzenburg, 1988). The diffusion loss correction also compensates for the effect of diffusion losses on the SMPS mean, causing it to shift towards smaller particle sizes. As shown in Fig. 2, the correction decreases the SMPS mean at all particle sizes by ca. 3 nm, producing a better correlation between the SMPS mean and the EAD/CPC mean. However, correcting the SMPS mean diameter for diffusion losses does not fully compensate for the difference in mean diameter determined by the EAD/CPC and the SMPS. The second factor which can significantly affect the SMPS measurement of mean size for the combustion aerosol produced by the CAST system is the interpretation of electrical mobility data based on a spherical particle model

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Fig. 4. Correlation curve for EAD total aerosol length and calculated SMPS total aerosol length based on CPC concentration and SMPS concentration.

rather than a more realistic aggregate model. Combustion aerosol generators have been found to generate aggregate clusters, and particles generated by the CAST have a highly fractal structure as revealed by SEM imaging (di Stasio, 2001; Samson, Mulholland, & Gentry, 1987). The problem of the response of SMPS to aggregates, including the effect of the fractal morphology on both charging probability and electrical mobility, has been considered in detail, both theoretically (Lall & Friedlander, 2006a) and experimentally (Lall, Seipenbusch, Rong, & Friedlander, 2006b). In this approach, the migration velocity of an aggregate is equated to that of a sphere, so that the number and size of primary particles which compose the aggregate can be related to the diameter of a sphere with the same migration velocity. This analysis employs a two module approach: one for calculating the drag on the aggregates and the other for calculating aggregate charging efficiency. Good agreement with theory was found both for silver aggregates created by evaporation–condensation and for aggregate particles from diesel exhaust (Lall et al., 2006b). The method of Lall et al. has recently been incorporated into a software module for the TSI SMPS (TSI, 2006b) which allows particle size distributions for SMPS data to be recalculated to include both the diffusion loss correction and the Lall et al. correction for aggregates rather than spheres. This method requires an a priori knowledge of the primary particle diameter for the aggregates being measured. For the CAST data, a primary particle diameter of 33 nm was chosen as a basis for the aggregate analysis for the following reasons: (i) it is the smallest particle size observed in these experiments and is at worst an overestimation of primary particle size (the 13 nm data was disregarded due to the uncertainties discussed above), (ii) the behavior of the CAST calibration curve for these conditions that was generated as discussed above (data not shown) suggests that primary particle growth is occurring between 13 and 33 nm, with aggregate formation occurring at larger observed particle sizes, and (iii) this value is realistic based on observations of other combustion systems (di Stasio, 2001) and limited data we have obtained for the CAST system based on transmission electron microscopy images. As shown in Fig. 2, adding the correction for nanoparticle aggregate mobility increases the SMPS mean by up to 26 nm at the largest particle size measured. This result indicates that the correlation between these two methods is worse when the aggregate nature of the aerosol is taken into account, and further suggests that the aggregate nature of the aerosol may be a contributor to the discrepancy between these two methods. The correlation between mean particle sizes as measured by the SMPS and EAD/CPC can also be expressed in terms of total aerosol length as shown in Fig. 4. In this case, the calculation used for Fig. 2 is simply reversed, i.e., the SMPS number-weighted mean is multiplied by the CPC concentration and corrected for dimensional units. Both SMPS mean and concentration were determined from distributions that had been corrected for both diffusion loss and aggregate mobility as described above. As expected, the correlation between total aerosol length determined by the EAD and total aerosol length calculated from the SMPS mean and CPC concentration is also poor. However, if the SMPS concentration rather than the CPC concentration is used for the calculation of total aerosol length, a greatly

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Fig. 5. CPC and SMPS concentration as a function of SMPS mean particle size with corrections for SMPS diffusion losses and aggregate mobility.

improved correlation emerges as shown in Fig. 4. Previous measurements of ambient aerosol comparing EAD current to SMPS concentration using surface area as a metric also showed a linear correlation between the instruments (Woo, Chen, Pui, & Wilson, 2001). The good agreement between EAD and SMPS data when total aerosol length is used as a basis, and the poor agreement between EAD/CPC and SMPS data when mean particle size is used as a basis, indicates that the difference in instrument measurements is between the SMPS and CPC rather than between the SMPS and EAD. As stated above, varying the fuel/N2 ratio for a given fuel/O2 ratio also changes the total concentration as well as the mean particle size. The total concentration measurements for the CPC and SMPS that correspond to the SMPS mean particle sizes in Fig. 2 are shown in Fig. 5. CPC concentrations are consistently higher than those measured by the SMPS, and it is this difference in measured concentration between the CPC and the SMPS that is the largest contributor to the poor correlation between the EAD/CPC and SMPS mean particle sizes in Fig. 2. It should be noted that these concentrations are near the upper limit of the Model 3022 CPC photometric range, which may contribute to the discrepancy between CPC and SMPS measurements. However, the difference is also a function of particle size, with the greatest differences occurring at the smaller particle sizes and decreasing as the particle size increases (the results at 13 nm may be anomalous as discussed above). This dependency on particle size suggests that diffusion losses in the SMPS may be a factor in these measurements. As shown in Fig. 5, correcting the SMPS concentration for diffusion losses reduces the difference in measured concentrations between the CPC and SMPS, although this effect is masked when the effect of aggregate mobility is also included. However, diffusion losses alone cannot fully account for the discrepancy between these two methods, indicating that additional factors must also contribute to this discrepancy. Further evidence that diffusion losses alone cannot fully account for the differences in mean particle diameters determined by these two methods is given in Fig. 6, which shows the correlation between EAD/CPC and SMPS means determined for fuel/O2 ratios in the CAST system other than 0.213. The relationship between the two methods clearly varies as a function of fuel/O2 and is different even for similar particle sizes, which are expected to experience the same diffusion losses in the classifier and thus the same impact on the EAD/CPC versus SMPS correlation. The magnitude of the differences in concentration also vary with fuel/O2 ratio, and the correlation between the EAD/CPC mean and the SMPS mean improves as the difference in SMPS and CPC concentration decreases (data not shown). Particles generated from sources other than the CAST demonstrate behavior that varies even more widely. Fig. 7 shows correlations between EAD/CPC means and SMPS means for a several different aerosol sources in addition to the CAST: ambient particles measured during the PMTACS—NY Winter 2004 Intensive Campaign, Na2 SO4 and sucrose

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Fig. 6. Correlation curve for EAD/CPC calculated mean size and SMPS mean size for various fuel/O2 ratios from 0.180 to 0.217.

Fig. 7. Correlation curve for EAD/CPC calculated mean size and SMPS mean size for various aerosol sources.

particles generated in the laboratory by spray atomization, particles sampled from a diesel generator exhaust stream and diluted by the same means as the CAST particles, and laboratory-generated aerosols of water soluble primary organic acids (pinonic, malonic, glutaric and palmitic acids, and levoglucosan). The widely varying behavior demonstrated in both Figs. 6 and 7 indicates that factors other than diffusion losses are contributing to the difference between the SMPS and EAD/CPC methods. Similar behavior is observed for the two combustion aerosols (CAST and diesel) and for the two “ambient” aerosols (PMTACS—NY and the primary organic acids), suggesting that characteristic differences between these two types of aerosols are contributing factors to the degree of correlation between the SMPS and EAD/CPC methods. The most likely factor is morphology, especially due to the aggregate nature of combustion particles. In addition to the effect of aggregate structure on particle mobility, the particle morphology may also impact these results in several other areas. In particular, the diffusion loss correction employed above assumes spherical particles; a correction based instead on the diffusion coefficients of aggregates may change the magnitude of the contribution of diffusion losses

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to the observed discrepancy in methods. Also, the response of the photometric mode of the CPC to highly fractal aggregates such as the CAST particles is questionable. Complex aggregate morphology may also contribute to the nonlinear shape of the correlation curve in Fig. 2. For example, the behavior of the curve may potentially be due to changes in the fractal dimension of the CAST aggregates with changing size. At small sizes and low fractal dimension the CAST particles would approximate spheres and fall on the 1:1 line as observed. As both size and fractal dimension increases and the particles assume the form of open, chain aggregates (fractal dimension < 2), their behavior would depart from that expected for spheres. At large sizes the particles may become more compact (fractal dimension approaching 3) and thus once again more closely approximate the behavior of spheres and approach the 1:1 line. Such an increase in fractal dimensions would also be consistent with the decrease in concentration for large particles shown in Fig. 5. Clearly, the effect of particle morphology on these measurements warrants further investigation. Future work to investigate morphology should include measurements performed using aerosol particles of defined morphology, such as polystyrene latex spheres. Morphology may also have a strong relationship with a second possible factor, which is charging. Since the EAD employs diffusion-limited charging, it is expected to have more sensitivity to particle morphology. The fractal nature of CAST particles may vary as a function of fuel/O2 and fuel/N2 ratios, and the diffusion-limited charging employed by the EAD is more likely to be sensitive to this characteristic of the particles than other methods of charging. Electrometerbased methods have been found to have a greater sensitivity to the initial charging state of the aerosol (Winklmayr, Reischl, Lindner, & Berner, 1990), and the charging state of the aerosol can also affect the relative measurement of concentration by the CPC and SMPS (Liu & Deshler, 2003). Particles generated from a combustion source are also more likely to carry significant charges than the other species shown in Fig. 7 (Kim, Woo, Liu, & Zachariah, 2005). Further, particle charging efficiency as a function of particle size may also be different for the aerosols studied; while not expected to be sensitive to composition, this behavior can clearly be affected by differing particle morphology. Future work to investigate charging should also include the effect of additional charge neutralization of the aerosol sample, and the possible employment of a Polonium rather than a Krypton source for charge neutralization (Liu & Deshler, 2003). A third possible factor is composition, as the different particle sources shown in Fig. 7 clearly have different compositions. Also, varying the fuel/O2 ratio in the CAST system, as was done in Fig. 6, is known to affect the relative fractions of elemental and organic carbon in the resulting particles (CAST, 2002). While composition has not been thought to strongly impact particle number measurements, there has been little direct investigation into such possible effects. Composition has previously been shown to directly impact diffusion charging measurements, in particular by the presence or absence of volatile material (Kittelson, Watts, Savstrom, & Johnson, 2004). Future work to investigate the effect of composition should include the use of a thermodenuder to remove volatiles in the aerosol sample, and the generation of aerosols of varying composition but similar morphology. 4. Conclusions For the combustion aerosol discussed in detail here, EAD and SMPS measurements are consistent with each other, and the total aerosol length is a useful parameter for comparing measurements from these two instruments. However, CPC and SMPS measurements were not found to be consistent. The SMPS measurements are affected both by diffusion losses and by the aggregate nature of the aerosol, and the agreement between SMPS and EAD suggests that both instruments may be similarly affected by these factors. The difference in mean particle size determined by the SMPS method versus the EAD/CPC method is solely due to the difference in concentrations measured by the CPC and SMPS rather than any characteristics of the EAD. However, diffusion losses and the effect of nanoparticle aggregates are insufficient to fully explain this behavior, since varying relationships were found between the two methods of determining mean particle diameter for different aerosol sources. Therefore, additional factors such as morphology, charging and composition may also play a significant role. Further research is warranted to investigate the role of these factors and others in determining the relationship between these two methods of determining mean particle size. In particular, future investigations into these phenomena should be conducted at conditions intended to optimize the performance of these instruments for individual sample aerosols (e.g., slower SMPS scan times), rather than conditions intended to duplicate those of the PMTACS—NY ambient study which was the initial impetus for this work. Overall, differences between these two methods make it unlikely that the EAD in concert with the CPC could be used as a fully independent method for determining mean particle sizes for sources with

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transient behavior such as mobile sources (diesel vehicles, etc.). However, this work does raise the possibility—if the factors determining the relationship between these methods can be more fully understood—that the combination of the EAD/CPC and SMPS methods could be used to provide information regarding particle characteristics other than mean size, such as morphology, etc. Given the difficulty in probing such characteristics for particles in the ultrafine range, even limited additional information could prove to be a valuable tool in furthering our knowledge of such particles. Acknowledgments This work was supported in part by the New York State Energy Research and Development Authority (NYSERDA) Contract # 4918ERTERES99 and Agreement # 8643, the US Environmental Protection Agency (EPA) cooperative agreement # R828060010 and the New York State Department of Environmental Conservation (NYSDEC) Contract # C004210. Although the research described in this article has been funded in part by the US EPA, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. The authors would like to thank Robert Praisner of NYSDEC for assistance in performing these experiments, and Walter Pienta of NYSDEC for helpful discussions. References Collins, D. R., Flagan, R. C., & Seinfeld, J. H. (2002). Improved inversion of scanning DMA data. Aerosol Science and Technology, 36, 1–9. Combustion Aerosol Standard Burner Operation Handbook. (2002). JING-CAST Technology GmbH, Berne, Switzerland. di Stasio, S. (2001). Observation of restructuring of nanoparticle soot aggregates in a diffusion flame by static light scattering. Journal of Aerosol Science, 32, 509–524. Fissan, H., Neumann, S., Trampe, A., Pui, D. Y. H., & Shin, W. G. (2007). 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