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Diesel Particle Size Distribution Estimation from DigitalImage AnalysisMagín Lapuerta a , Octavio Armas a & Arántzazu Gómez aa Escuela Técnica Superior de Ingenieros Industriales , University of Castilla-La , Mancha,SpainPublished online: 30 Nov 2010.

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Aerosol Science and Technology 37: 369–381 (2003)c© 2003 American Association for Aerosol ResearchPublished by Taylor and Francis0278-6826/03/$12.00+ .00DOI: 10.1080/02786820390125223

Diesel Particle Size Distribution Estimation from DigitalImage Analysis

Magın Lapuerta, Octavio Armas, and Arantzazu GomezEscuela Tecnica Superior de Ingenieros Industriales, University of Castilla–La Mancha, Spain

One of the most serious problems associated with Diesel en-gines is pollutant emissions, specially nitrogen oxides and partic-ulate matter. However, although current emissions standards inEurope and America with regard to light vehicles and heavy dutyengines refer to the particulate limit in mass units, there has beenincreasing concern of late to know the size and number of parti-cles emitted by engines. This interest has been promoted by thelatest studies about the harmful effects of particles on health andis enhanced by recent changes in internal combustion engine tech-nology. This study is focused on the implementation of a methodto determine the particle size distribution that could be appropri-ate for the current methodology of vehicle certification in Europe.This method uses an automated Digital Image Analysis Algorithm(DIAA) to determine particle size trends from Scanning ElectronMicroscope (SEM) images of filters charged in a partial dilutionsystem used for measuring specific particulate emissions. The ex-perimental work was performed on a stationary electric generationdirect injection Diesel engine with 0.5 MW (671 hp) rated power,which is considered as a typical engine in middle power industries.Particulate size distributions obtained using DIAA were comparedwith distributions obtained using an Optical Particle Counter (OC)and a Scanning Mobility Particle Sizer (SMPS), the latter currentlyconsidered as the most reliable technique. Although the numberconcentration detected by this method does not represent the realflowing particle concentration, the algorithm gives a fair reproduc-tion of the trends observed with on-line techniques (SMPS and OC)when the engine load is varied.

INTRODUCTIONAccording to requirements with regard to air quality, Di-

rective 98/69/EC (Emission Standards1998), together with the

Received 27 June 2002; accepted 18 October 2002.Financial support from Spanish Education Ministry (Project 1FD97-

1605) and collaboration of Cer´amicas de Mira S.L. and CIEMAT aregratefully acknowledged.

Address correspondence to Mag´ın Lapuerta, Escuela T´ecnica Su-perior de Ingenieros Industriales, University of Castilla–La Mancha,Edificio Politecnico, Avda. Camilo Jos´e Cela, s/n. 13071 Ciudad Real,Spain. E-mail: Magin.Lapuerta@uclm.es

foreseeable limits demanded of Diesel engine manufacturers bycertification standards and the harmful effects of particles onhealth, encourage the development of techniques to improve de-tailed knowledge of the number and size of emitted particles(Goodfellow 1998).

Usually particle size distributions of a Diesel engine aerosolare log-normal and three modal in structure. These three modesare, respectively, nuclei, accumulation, and coarse. The nucleimode consists of nanoparticles, that is to say, primary parti-cles formed during combustion and exhaust dilution, that areless than 50 nm in diameter. Due to the large number of par-ticles, the nuclei mode dominates the particle number distribu-tion. The accumulation mode consists of particles in the 50 nm to1µm diameter range. This is where agglomerates with adsorbedhydrocarbons reside. Their larger size in comparison to nucleimode particles makes this mode dominate the mass distribution.Finally, the particles which are more than 1µm in diametercompose the coarse mode, but they have no effect on numberdistribution (Graskow et al. 1999). Therefore, if the coarse modeis not considered, particle size distributions can be defined asbimodal.

Although bimodal distributions have been reported by sev-eral authors (Graskow et al. 1999; Abdul-Khalek et al. 1998;Baumgard and Johnson 1992, 1996; Pagan 1999; Hawker et al.1998), others have found only a single mode within the samediameter range (Greenwood et al. 1996; Rickeard et al. 1996;Hammerle et al. 1994; Lepperhoff et al. 1994; Gruber andKlawatsch 1999). However, it still has not been proved whethersuch disagreement is due to different tested engines and opera-tion conditions or to different sampling methodologies.

Scanning Mobility Particle Sizer (SMPS) is considerednowadays as the most reliable technique to determine particlesize distributions in the mentioned size range. The combineduse of this technique with dilution tunnels or minitunnels guar-anties the precise control of particle concentration, exhausttemperature, and exhaust flowrate, permits correspondence be-tween specific particulate emission and particle size distribu-tion measurements, and provides complete information about

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all the implications of particulate emissions (Armas et al.2001).

However, like other sizers such as cascade impactors, opti-cal analyzers, etc., the SMPS technique requires complex andexpensive equipment and careful maintenance, which makesits use difficult to generalize in some circumstances. Becauseof this, although the particle morphology of Diesel engine ex-haust has previously been studied with the aid of scanning andtransmission electronic microscopy (with the objective of dis-playing agglomerates or identifying primary particles but not asa quantitative method—see Lipkea et al. 1978; Schraml et al.1999), it is the intention of this work to establish whether ornot an alternative technique using digital image analysis fromparticulate filters charged in dilution systems could distinguishsignificant particle size trends, even though it is known thatthe particle number obtained in this way is never represen-tative of actual particle exhaust population (Armas et al.2001).

With this objective, an experimental study comparing particlesize distributions obtained using different methods is presented.On the one hand, on-line techniques such as a SMPS and an op-tical counter (OC) are used and, on the other, a heuristic DigitalImage Analysis Algorithm (DIAA) determines the particle sizedistribution from Scanning Electron Microscope (SEM) imagesof filters.

DIGITAL IMAGE ANALYSIS ALGORITHMIMPLEMENTATION

Structure of the Filters Used in Particle SamplingAs stated by the current European standards (Emission

Standards 1994), Diesel particulate matter must be sampledwith dilution systems at a temperature lower than 52◦C, eitherwith partial stream tunnel (minitunnel) or with full flow tun-nel, through glass fiber filters covered by Teflon, although othertypes of material are allowed for special applications.

Glass fiber filters are composed of a thin mesh of interwovenfibers. Many of the nanoparticles settle in the hollows betweenthe fibers (Figure 1). This could be the disadvantage of this typeof depth filters for optical surface treatments when compared toother more regular supports, such as those commonly used in

Figure 1. Image of a clean glass fiber filter taken with SEM(500×).

microscopy. These latter supports are flat surfaces which supplya uniform background when images are taken. However, fiberfilters were selected for this study because they are currently usedin the measurement of Diesel particulate emissions for vehiclecertification in Europe.

In order to limit the inaccuracies caused by the inability toaccount for particles which are settled inside the filter hollowsand in hidden cloaks, filters were charged with 1.3 mg. Thisis the lowest valid mass required by current standard. More-over, this type of minimum-mass sampling with the minitun-nel excludes both the possibility of nanoparticles covering largeparticles (thus making the image treatment more difficult) andadditional agglomeration due to increased sampling time andsampled mass.

Therefore, the main objective of this simple algorithm is toobtain the particle size distributions from a set of different mag-nification images taken by SEM. In this way, if low magnificationimages are used (500×), it is possible to capture a large num-ber of particles. Additionally, 8000× images are used to countnanoparticles because they are too small to be adequately as-sessed in big magnification, lower resolution images (Figure 2).This latter magnification was selected as a consequence of acompromise between the outline definition of particles and theimage resolution.

In 500× images it is possible to distinguish between macro-particle zones, big agglomerated particles represented as whitespots, filter fibers, smaller nonagglomerated particles represent-ed as a gray cloak, and finally, the image background. On theother hand, in 8000× images taken from the gray nanoparticlecloak, only the nonagglomerated particles and the correspondingimage background can be distinguished.

Although the authors are aware that digital image treatmentscan provoke certain loss or modification of information, the factthat assessment of the charged filter components varies depend-ing on the magnification chosen makes it necessary to carry outdifferent image treatments. In this way, 500× images must bepretreated to distinguish between macroparticles and the restof the image components, such as the filter fibers whose graylevels are very similar to those of macroparticles. However, in8000× images this distinction is not necessary, as these lattercomponents do not appear.

Image Analysis MethodologyTo achieve the proposed objective, and being aware that a dis-

tribution is only representative when a large number of imagesare taken from different parts of the filter, a set of eight mi-crophotos were taken in this study—four 500× images for theanalysis of macroparticles and four 8000× images for the anal-ysis of nanoparticles. This number of microphotos was selectedas a compromise between achieving accurate results and theassociated processing time. The schedule of the image analysisprocedure is shown in Figure 3.

According to this figure, the algorithm makes a neighborhoodaveraging from 500× images, a contrast modification and finally

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DIESEL PARTICLE SIZE DISTRIBUTION 371

Figure 2. Images of a charged glass fiber filter taken with SEM (1.3 mg of particles collected): (a) 500×, (b) 8000×.

Figure 3. Schedule of the image analysis procedure.

Figure 4. Large particles detection, 500× images.

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Figure 5. Nanoparticles detection, 8000× images.

an image thresholding. When 8000× images were analyzed,thresholding was made from the original image without previoustreatments.

Figures 4 and 5 show the schedule for the macro- and nanopar-ticle detection procedure.

As shown in Figure 4, the neighborhood averaging consistsof the application of a unitary mask to the original image matrix.This smoothing filter is used not only to remove small detailsfrom the image prior to large objects extraction and to bridgesmall gaps in lines or curves but also to reduce white noise(Gonzalez and Woods 1993).

In the same way, with contrast modification or histogramequalization it is possible to alter the overall image gray lev-els, enhancing the highest ones (those belonging to macropar-

Figure 6. Gray level histogram and threshold election.

ticles). This alteration is made by transforming each pixel graylevel (r ) by a transfer function, which brings an appropriateappearance to the final image. In this case, the transformationwas

s= T(r ) = r 2. [1]

The result is a general but not proportional decrease of imagegray levels. Such a decrease is more intense for low gray levelpixels, while those corresponding to macroparticles remain veryclose to unity.

Once the image has been treated, in the case of 500× im-ages, it must be thresholded as a function of its original graylevels histogram (Figure 6). The simplest of all segmentation

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techniques is the partition of the image histogram by using asingle threshold,T , adopted as the gray level of the mean valueplus the standard deviation of histogram values (which wouldcorrespond to 84% of the data if the histogram was adjusted toa Gaussian distribution). In this way, the objects of interest arelighter than the background, so any pixel with a gray level>T islabeled white (1) and any pixel with a gray level≤T is labeledblack (0).

Once the particles are identified, they are counted and theirsize is determined in pixels by means of a connectivity rela-tionship. To establish whether two pixels are connected, it mustbe determined if they are adjacent in some sense (if they are4-neighbors) and if their gray levels satisfy a specified criterionof similarity (if they are equal).

As neither fibers nor macroparticles appear in 8000× images,the amount of particles detected in these images must then beextrapolated to those which would be detected in the area corre-sponding to 500× images in order to obtain a joint distributiontogether with particles directly detected from 500× images.

Finally, the particle diameter in microns is determined ac-cording to each image resolution and particles with the samesize are grouped, leading to the total distribution as well as somerelated statistics.

EXPERIMENTAL VALIDATION OF DIAA

Experimental SetupThe experimental work was performed on a turbocharged

DI Diesel engine, model Guascor E-318 with 0.5 MW electricpower, considered as a typical engine in middle power industries,

Figure 7. Experimental setup schedule.

Table 1Engine specifications

Engine type ID/TurbochargedInjection system On line Bosch pumpBore/Stroke (m) 0.15/0.15Cylinders 12 VCompression ratio 14Swept Volume (l) 31.8

At 1500 min−1:Mechanic rated power (kW) 525Electric rated power (kW) 500Rated Torque (Nm) 3300

coupled to an electric generator. The main engine characteristicsare shown in Table 1. A scheme of the experimental procedureto validate DIAA is shown in Figure 7.

As mentioned above, particulate matter was collected throughglass fiber filters covered with Teflon (Pallflex T60T20) in apartial dilution minitunnel (Nova Microtroll). Before and afterthe collection, the filters were conditioned in a climatic chamberMinitest CCM-0/81 to keep constant temperature and humiditybefore being weighted in an analytical balance HA-202M ofA&D Company Limited, U.K.

In order to obtain the particle size distributions in the wholediameter range to be analyzed, two different techniques wereused: firstly a Scanning Mobility Particle Sizer (SMPS), whichmeasures the electric mobility of particles, and secondly an Op-tical Counter (OC) based on the light scattering of particles in-side a defined measuring volume. The equipment used for these

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measurements was supplied by CIEMAT (Centro de Investiga-ciones Energ´eticas, Medioambientales y Tecnol´ogicas). The setof parameters used in this study permitted us to obtain distri-butions with SMPS in the 0.01 to 0.35µm diameter range andwith OC in the 0.25 to 20µm diameter range. Finally, partic-ulate mass concentrations in different size ranges to a maxi-mum diameter of 25µm were obtained with a 9-stage cascadeimpactor.

Test ScheduleThe test schedule was defined according to the following

restrictions:

• The engine generated electric power at a steady speed so thetest schedule was performed during the productive cycle ofthe plant where the engine was installed.

• The variations of engine load were adapted to the character-istics of the load regulation system of the engine-alternator.

Four steady operating conditions were scheduled with a con-stant speed of 1500 rpm but with different engine loads, as shownin Table 2. Likewise, the sampling time to collect the lowest valid

Figure 8. Particle size distribution and associated statistics obtained with DIAA (150 kW of electric power—30% of engineload).

Table 2Test schedule and sampling time with the minitunnel as a

function of engine load

Sampling timeSpeed Engine load Electric power with the(min−1) (% of full load) (kW) minitunnel

1500 30 150 6 min50 250 4 min, 45 s70 350 4 min, 20 s

90 (usual engine 450 4 minoperating condition)

mass demanded by current standard with the minitunnel is alsolisted in Table 2.

Experimental ResultsParticle Size Distributions. The particle size distributions

obtained with DIAA for each engine operating condition as wellas related statistics are shown in Figures 8 to 11. Two images

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DIESEL PARTICLE SIZE DISTRIBUTION 375

Figure 9. Particle size distribution and associated statistics obtained with DIAA (250 kW of electric power—50% of engineload).

among the set that was used to obtain the distribution, one of500× and one of 8000×, are also shown.

The particle size distributions obtained with on-line tech-niques—SMPS and optical counter (OC)—as a function of en-gine load are shown in Figure 12, although the OC result for 50%engine load was rejected due to concentrations below the detec-tion limit (100 part/cm3) for particle diameters over 0.75µm.

As shown in Figure 12b, the OC distributions coincide withthe tail of each SMPS distribution (diameter>0.25µm). More-over, the particle size distributions obtained with on-line tech-niques within the whole diameter range appear to be quite similarto those obtained with DIAA, although not in absolute values asexpected.

Total Concentration Results.The integrated particle con-centration obtained with on-line techniques is shown inFigure 13. It can be observed that particle concentration in theSMPS range increases with engine load but decreases for thehighest load (Patschull and Roth 1992, 1995). However, in theOC range, the particle concentration increase becomes more in-tense as load increases. This suggests that the detected decreasein the SMPS range for high load provokes an agglomeration

effect with a corresponding diameter growth, moving the parti-cles to the OC diameter range.

The following figures show the particle concentration de-tected in the filter by DIAA, per unit surface (Figure 14a) andper unit volume flowing in the exhaust under normal conditions(Figure 14b). This latter graph was obtained dividing the particleconcentration per unit surface data (Figure 14a) by the samplingtime and flowrate and then by multiplying by the dilution ratio.Moreover, from these previous data it could be possible to dis-tinguish the particle concentration corresponding to SMPS orOC diameter range, that is to say, to identify particle concen-tration on the one hand in the 10 to 350 nm diameter range andon the other hand in the 250 nm to 20µm diameter range. Theaddition of both particle concentrations is not exactly equiva-lent to the total particle concentration because the lower limitof the OC range is slightly lower than the upper limit of theSMPS range.

The particle number per unit filter surface decreases withengine load because the sampling time in the minitunnel is re-duced in order to constantly obtain the same sampled particleweight. However, when particle concentration is analyzed either

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Figure 10. Particle size distribution and associated statistics obtained with DIAA (350 kW of electric power—70% of engineload).

per unit sampling time or per sampled volume (leading to propor-tional results), DIAA trends are consistent with those observedwith the SMPS. Moreover, the total concentration trends followthose corresponding to SMPS diameter range, as the majorityof particles belong to this range. In the OC diameter range, incontrast to the optical counter, no significant increase with loadis found.

From the results of particle concentration in the filter per unitsampling time, particulate mass concentration in the filter in g/hcan be obtained if density values are assigned to particles as afunction of their size.

Particulate Mass Results.Approximations to different val-ues of primary soot particle density can be found in severalstudies. Kyriakides and Dent (1986) assume a density for sootparticles of 2 g/cm3 in their phenomenological model of Dieselcombustion, as do Pontikakis et al. (2001) or Saathoff et al.(2002), although this latter reference assumes this value onlyfor spark ignition soot, while a density of 1.7 g/cm3 is assumedfor Diesel engine soot. Other works propose values for soot den-sity between 1.8 g/cm3 (Kronholm and Howard 2000; Corcioneet al. 2002) and 1.5 g/cm3 (Yu and Xu 1986).

However, it is more unusual to find studies which givedensity values for agglomerates, for which an effective den-sity, defined as the ratio between their mass and their appar-ent volume, would be more appropriate. This effective densityvaries as a function of soot density and particle morphologyand porosity (Burtscher 2001). Moreover, phenomena like ad-sorption and condensation of gaseous hydrocarbons on particlesurface make predictions more difficult (Ahlvik et al. 1998).In any case, the reviewed literature agrees that effective parti-cle density decreases with the agglomerate size. For instance,Fuchs (1964) infers that this effective density can vary between0.1 and 0.7 times the primary particles density forming theaggregate, while Pontikakis et al. (2001), in their study aboutmodelling of Diesel particle retention filters, assume effectivedensity values as low as 0.05 g/cm3 for particles prior to im-paction with filters, although affirming that particle porosity de-creases as they accumulate in the filter, increasing their densityconsistently.

In Ahlvik et al. (1998), effective density values were calcu-lated by fitting the particle size distributions from a Diesel en-gine obtained with an Electrical Low Pressure Impactor (ELPI)

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Figure 11. Particle size distribution and associated statistics obtained with DIAA (450 kW of electric power—90% of engineload).

to those obtained with a Differential Mobility Particle Sizer(DMPS). With these effective density values calculated for dif-ferent diameters, and adopting as minimum density for bigagglomerates (as inferred by Fuchs) 0.1 times the primary par-

Figure 12. Particle size distributions obtained with on-line techniques for various engine loads. Data provided by CIEMAT.(a) SMPS, (b) optical counter.

ticles density identified by Ahlvik et al. (1.55 g/cm3), an expo-nential fitting was made, as shown in Figure 15.

Equation (2) shows the resulting exponential function. Withthis function, an effective density value can be associated with

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Figure 13. Particle concentration as a function of engine load.Results obtained from data provided by CIEMAT.

each particle diameter found with DIAA:

ρ = ρo − (ρo − ρag)

[1− exp

(C

(log D − log Do

log Dag− log Do

)m)][2]

whereρ is the effective particle density;ρo is the maximumeffective density, corresponding to primary soot particles, withconsidered value= 1.55 g/cm3; ρag is the minimum effectivedensity, corresponding to big agglomerates, with consideredvalue= 0.155 g/cm3; D is the equivalent particle diameter;Do is the minimum equivalent diameter, corresponding to pri-mary soot particles, with considered value= 0.03µm; Dag isthe maximum equivalent diameter corresponding to the mini-mum density, fitting value= 1 µm; m is the shape exponent,fitting value= 4.28; andC is the coefficient which guaranties acomplete sweeping of the whole density range with 0.1% error,value= −6.908.

Table 3Effective particle density comparison

Engine load (%) 30% 50% 70%Impactor diameter range (µm) 0.011–0.6805 0.011–0.695 0.011–0.7847Impactor concentration (g/l) 1.75145E-5 2.4187E-5 2.9659E-5SMPS concentration (cm3/l) 3.1322E-5 4.7466E-5 7.3751E-5CO concentration (cm3/l) 1.9319E-7 1.9984E-8 3.3469E-7Measured effective density (g/cm3)∗ 0.555 0.509 0.401Estimated effective density (g/cm3)∗∗ 0.484 0.488 0.449

∗ρ Measured= Impactor concentration/(SMPS concentration+ CO concentration).∗∗ρ Estimated from Equation (2) and averaging throughout the indicated range.

In order to check the validity of this fitting for the presentexperimental results, the particle volume distributions obtainedwith SMPS were combined with the particle mass distributionsobtained with a cascade impactor. Dividing the mass concentra-tion for each impactor stage by the corresponding volume con-centration, a mean effective density of particles was estimatedfor the considered diameter range. However, big macroparti-cles were not detected by OC because their concentrations werelower than the detection limit (100 part/cm3). Consequently, thisestimation was only possible for the smallest size impactor stage,corresponding to the nanometer diameter range. This validationwas made for three engine loads. The resulting mean effectivedensity values, together with the density values obtained fromEquation (2) in the considered diameter range, are shown inTable 3.

As observed in the table, mean density values obtained withthe impactor, SMPS, and OC data correspond with fitting re-sults from Ahlvik et al. (1998). Therefore, effective density ofagglomerates was assumed to follow the proposed exponen-tial function of particle diameter, this being identified fromDIAA.

With this assumption, particle mass detected by SEM canbe calculated if particle spherical shape is assumed. Through500× images and filter area ponderation, particle mass identi-fied by DIAA in the filter can be obtained. Finally, consideringthe diluted exhaust flow in the minitunnel and sampled flow,the fraction of the engine particulate emissions in g/h whichcould be detected from microscopy was estimated, as shown inFigure 16a. It can be observed that the trend with engine loadagrees with that of particulate emissions directly measured withthe minitunnel (Figure 16b), although DIAA results are lowerdue to the surface treatment of the filter images.

Mean Diameter Results.Mean diameters obtained with on-line techniques and with DIAA in the same diameter ranges areplotted versus engine load in Figure 17. As in the case of concen-trations, DIAA trends also agree with those obtained with SMPS,even though absolute values differ slightly. In the OC range, thetrends obtained with DIAA are again the same as those obtainedwith the optical counter. An increase of the mean diameter is ob-served at the highest load due to the above-mentioned agglom-eration effect. However, differences between the optical counterD

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Figure 14. (a) Identified particle number per unit filter surface (DIAA). (b) Particle concentration in the exhaust obtained withDIAA.

Figure 15. Diesel particle effective density as a function of diameter. Data from Ahlvik et al. (1998).

Figure 16. (a) Estimated particulate emissions with DIAA as a function of engine load. (b) Particulate emissions measured withthe minitunnel.

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Figure 17. (a) Mean diameters obtained with on-line techniques as a function of engine load. Results obtained from data providedby CIEMAT. (b) Mean diameters obtained with DIAA.

and DIAA results become more marked mainly because of thehigher macroparticle identification with DIAA than with theoptical analyzer. Macroparticles with more than 2µm diametercould not be detected by this latter technique because their con-centrations were lower than the detection limit (100 part/cm3).This fact also causes the on-line mean diameters within thewhole diameter range to be lower than DIAA ones.

CONCLUSIONS• A digital image analysis algorithm was implemented to de-

termine particle size trends from Scanning Electron Mi-croscope (SEM) images from filters charged in a dilutionsystem used for measuring specific particulate emissions.

• When the engine operating mode is modified, the mean di-ameter trends obtained from SMPS and optical counter mea-surements are consistent with those obtained from DIAAapplied to filters. A diameter increase is noticed in thelarge particle range when the engine load is varied from70% to 90%, perhaps due to an agglomeration effect on theparticles.

• Moreover, when the engine operating mode is modified from70% to 90%, a decrease in the SMPS concentration dueto the particle growth was also observed with DIAA. Thiscan be explained because the engine operating conditionsaffect the whole range of particle sizes. However, the particleconcentration increase within the optical analyzer range isnot as well-marked with DIAA as within the SMPS range.

• Assuming that the effective density of particles follows anexponential function of particle diameter, the fraction of theengine particulate emissions which could be identified withDIAA was calculated. The observed trends with engine loadwere similar to those obtained from direct measurementswith minitunnel, although absolute values were lower dueto surface treatment of filter images.

• Due to high consistency of trends, and despite the differ-ences in absolute values, a procedure based on DIAA fromparticulate emissions filters can be appropriate for Dieselengine research and could even be the basis for a future par-ticle size certification procedure, with the consequent savingin specific and sophisticated techniques.

REFERENCESAbdul-Khalek, I. S., Kittelson, D. B., Graskow, B. R., Wei, Q., and Brear, F.

(1998). Diesel Exhaust Particle Size: Measurement Issues and Trends. Societyof Automotive Engineers,SAE paper980525.

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