PMF Analysis of Wide-Range Particle Size Spectra ... - ACS Publications

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PMF Analysis of Wide-Range Particle Size Spectra Collected on a Major Highway Roy M. Harrison,* David C. S. Beddows, and Manuel Dall’Osto† National Centre for Atmospheric Science, Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom

bS Supporting Information ABSTRACT: Particle number concentration data have been collected on a very busy road in central London (Marylebone Road). Continuous size distributions from 15 nm to 10 μm diameter, collected over 21 days, were analyzed using positive matrix factorization which identified 10 factors, five of which were observed to make major contributions (greater than 8%) to either the total number or volume of particulate matter. The sources associated with each factor were identified from the size distribution, directional association, diurnal variation and their relationship to meteorological pollution and traffic volume variables. The factors related to the emissions on Marylebone Road accounted for 40.5% of particle volume and 71.9% of particle number. These comprised nucleation mode exhaust particles (3.6% of total volume and 27.4% of total number), solid mode exhaust particles (18.8% of total volume and 38.0% of total number), brake dust (13.7% of total volume and 1.7% of total number and resuspension (4.4% of total volume and 4.8% of total number). Another six factors were associated with the urban background accounting for 59.5% of total volume and 28.2% of total particle number count. The method is extremely successful at separating the components of on-road emissions including brake wear and resuspension.

1. INTRODUCTION The adverse effects of exposure to airborne particulate matter on human health are now well established.1 This has led governments around the world to establish air quality guidelines and standards and to implement policies to reduce concentrations of particulate matter in polluted air. Despite such measures, progress in reducing concentrations of PM10 and PM2.5 across western Europe has been very slow in many places since the year 2000.2 Current knowledge of which components by source, composition and size have the greatest impact on public health has long been weak,3 and although progress is being made, the results remain inconclusive. Nonetheless, there remains a very strong imperative to gain a better understanding of the source attribution of particles in the urban atmosphere in order that effective measures can be applied to the major contributing sources. Major hotspots with respect to particle pollution are commonly observed alongside major highways. The contributors to elevated concentrations include regionally transported pollutants, pollutants from within the city itself and emissions on the adjacent highway.4 By a careful data analysis of roadside and nearby urban background sites, including measurements of particle chemistry, it was possible to distinguish these contributions.4 However, the roadside incremental concentration is made up not only of particles from the vehicle exhaust but also from tire, brake, and road surface wear and from resuspension of road dust by the action of passing traffic.5 While progress is being r 2011 American Chemical Society

made in distinguishing these contributions6 it remains very difficult to make a quantitative attribution on the basis of roadside measurements, and data for nonexhaust emissions in emissions inventories are very uncertain. Consequently, receptor modeling methods provide the best alternative to disaggregate roadside particles according to their sources. Our studies7 have recently shown through the clustering of urban particle size distribution data that very different size distributions are found in different locations and at different times, which can be interpreted in terms of the known sources and meteorological processes affecting the particles. This gives confidence that an analysis of particle size spectral data should be able to determine the sources of the constituent particles. Positive matrix factorization (PMF) has been widely applied for the source apportionment of atmospheric pollutants for a variety of metrics, and it is suited to physical and chemical measurements in that it has the constraint that all factors must have positive elements. Charron and Harrison, Janhall et al., Costabile et al., and Pey et al.8
11 have all applied cluster analysis or principal component analysis (PCA) to roadside SMPS data in order to characterize the source contributions to the measured spectra and even to estimate emission factors. As an extension to the Received: March 2, 2011 Accepted: May 26, 2011 Revised: May 20, 2011 Published: June 08, 2011 5522

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Environmental Science & Technology aforementioned studies, we have determined the main factors contributing to the particle size data collected at the Marylebone Road roadside supersite by applying PMF to a particle number data set (diameter 14
10 000 nm) in combination with CO and NOx concentrations, traffic flow and meteorological data.

2. EXPERIMENTAL SECTION 2.1. The Marylebone Road Supersite. The Marylebone Road supersite (lat = 52.81 °N and long = 1.06 °W) is part of the London Air Quality Network. Sampling instruments were sited in a cabin placed at kerbside, with an inlet at about 3 m above groundlevel. Further details are given in the Supporting Information (SI). 2.2. London Marylebone Road Data Set. An intensive observation period ran from 19th October to 8th November 2007. The data set considered for multivariate analysis was a 438  120 matrix of values where each column represented a particle size bin or an auxiliary metric and each row represented an hourly measurement. Particle counts and size distributions (14.9
626.4 nm) were determined using a scanning mobility particle sizer (SMPS) comprising model 3080 electrostatic classifier and a model TSI 3776 condensation particle counter (CPC), complemented by a TSI aerosol particle sizer (APS) 3321 which measured particle diameters within the range 0.542
19.81 μm. The data collected from these two instruments were averaged into hourly spectra and merged into one particle size spectrum matrix (diameter 14.9
10 000 nm) according to the method of Beddows et al.12 Data from carbon monoxide and oxides of nitrogen instruments, installed at the Marylebone Road monitoring station as part of the London Air Quality Network (see SI Table S2), were included as the auxiliary metrics in the last 17 columns. The data are quality-assured to national network standards and reported to the UK National Air Quality Data Archive. 2.3. Meteorology at Marylebone Road. SI Table S3 summarizes the meteorological data collected over the measurement period at Heathrow Airport on the outskirts of London, which represent winds above the street canyon, rather than those within. Measurements at the Marylebone Road measurement site reflect the fact that it is a traffic line source within an urban canyon. As described by Vardoulakis et al.13 when the wind blows across the roof tops perpendicular to the canyon, urban background air is driven down the windward side of the canyon, across the lines of traffic toward the leeward side before rising up out of the canyon. If the measurement is taken on the windward side of the canyon the measurement is reflective of the local background air, whereas if the measurement is on the leeward side of the canyon, the measurement is reflective of the background plus traffic emissions. For the Marylebone Road the latter case occurs when the wind direction is in a southerly sector. 3.3. Positive Matrix Factorization. Details of this method appear in the SI. The diurnal patterns of the factors were averaged for all days (included in Figure 1) and for weekday and weekend days separately (shown in SI Figure S1). The time series of the factors were also correlated with the aerosol mass spectrometer (AMS) factors for organic matter (total, oxidized, cooking, combustion, and primary), sulfate and nitrate at the nearby Regents Park site.14 However, these correlations are regarded as indicative only, as the sites have significant spatial separation (about 1 km) and the Regents Park site, unlike Marylebone Road, is not affected by a street canyon vortex.

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4. RESULTS The results of the PMF analysis appear in Figure 1, which shows the number and volume spectra associated with each factor, together with its average wind direction and time dependence, and association with gaseous pollutant and traffic variables. Table 1 and SI Table S4 shows the contribution of each factor to the total number or volume of particles measured, and the suggested source. Factor 1 shows a mode in the number distribution at 0.1 μm and the volume distribution of around 0.35 μm (Figure 1). It is not strongly related to any of the meteorological, pollutant, or traffic variables but shows a weak association with NOx. Consistent with that association with NOx, its diurnal pattern shows the possibility of some traffic influence, but this is not entirely clear. The size distribution and lack of obvious directionality suggests that this is background accumulation mode aerosol containing also a small coarse particle component. It correlates most strongly with the AMS factor for total organic matter and contributes more to particle volume (12.8%) than to particle number (6.3%). Factor 2 shows marked directionality in comparison to Factor 1 with a strong association with wind sectors from the south (as opposed to north) which generate a vortex within the canyon which maximizes the impact of traffic emissions on Marylebone Road upon measured pollutant concentrations at the sampling site on the southern side of the road. The strongest directional association is with winds during rush hour periods from a west southwesterly direction which corresponds very closely to the orientation of Marylebone Road itself. The factor also shows a modest association with articulated HDV and PCV (buses and coaches), consistent with a traffic source (Figure 1). This factor makes a much stronger contribution to particle volume (13.7%) than to particle number (1.7%) and from the size of the mode in the volume distribution at 3 μm, it is tentatively inferred that this factor is associated primarily with brake dust.15 Factor 3 is the largest contributor to particle number (38%) and second to particle volume (18.8%). It also shows an association with winds with a southerly component, associations with traffic-generated primary pollutants (NOx) and a diurnal pattern likely to be traffic-associated. From the position of the modes at around 50 nm in the number distribution and 200 nm in the volume distribution, this factor corresponds to solid carbonaceous particles from diesel exhaust as characterized by Shi et al.16 in exhaust sampled on a dynamometer and by Zhu et al.17 in a highway tunnel. Casati et al.18 report this mode as occurring centered on around 50 nm (number distribution) as in this study. A minor mode at 3 μm in the volume distribution is probably associated with a minor contribution from brake dust particles. Factor 4 shows a strong association with morning rush hour traffic and winds in the southwesterly sector consistent with a vortex bringing traffic-generated pollutants to the sampler. The very sharp mode in the number distribution at around 20 nm associates this factor strongly with nucleation mode particles generated during dilution of diesel exhaust emissions. This was observed by Ntziachristos et al.19 in sampling from engine exhaust and by Charron and Harrison and Janhall et al.8,9 who observed the mode to be strongest in the morning probably due to low temperatures at this time of day. This factor is also associated positively with traffic-generated primary pollutants. Casati et al.18 report this mode as occurring in the range 10
30 nm diameter. This factor makes the second largest contribution to 5523

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Environmental Science & Technology particle number (27.4%) but only a small contribution to particle volume (3.6%). Factor 5 is more difficult to attribute. Its directionality associates it predominantly with background air arising from northerly directions when the street canyon vortex leads to sampling of background air little influenced by emissions from traffic within the street canyon. It is consequently surprising that this factor shows a strong association to traffic volume (LDV) but only modest associations with primary pollutants and wind speed. Its strongest characteristic is the diurnal pattern which shows a strong peak throughout the working day with a nocturnal

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minimum. The factor does not make a large contribution to either particle number (7.5%) or to particle volume (2.3%) and from its size association with a mode in the number distribution at just below 30 nm, this seems most likely to be a reflection of suburban traffic emissions within the background air of London. Consistent with this, it rises later in the morning on weekends than weekdays (see SI Figure S1). Factor 6 is also associated predominantly with wind with a northerly component suggesting that it is primarily within the urban background rather than generated by traffic on Marylebone Road. Its contribution to particle volume (8.4%) is much

Figure 1. Continued 5524

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Figure 1. PMF results derived from the analysis of the merged SMPS-APS particle number data collected at roadside. The wind roses depict the trend in scores with wind direction and time for a typical day and the associated graphs show the factors derived for the number spectra and auxiliary metrics. (Key: WS, wind speed; TEMP, temperature; DEWPNT, dew point; RH, relative humidity; BP, barometric pressure; SPD, vehicle speed; PCV, bus and coaches; LDV < 5.2 m, cars and vans; LDV > 5.2 m, trucks, rigid lorries and minibuses; TOWING, cars þ trailer).

greater than that to particle number (2.0%) and the mode in the volume distribution is at 2.0 μm with a lesser mode at around 0.2 μm. This, combined with modest associations with primary pollutants and a diurnal variation with a minimum during the early afternoon, suggests that this component may be associated with background nitrate aerosol or with a local source operating predominantly at nighttime. This correlates weakly with the AMS solid fuel organic aerosol factor, so the latter appears more probable.

Factor 7 is also a relatively minor component contributing 4.8% to particle number and 4.4% to particle volume. It shows an association with nitrogen oxides and with heavy duty vehicles and peaks in the morning rush hour when the contribution of heavy duty diesels to concentrations at Marylebone Road is at its greatest. It is associated most strongly with winds from the southerly sector and shows a peak in the number distribution at around 25 nm and in the volume distribution a substantial peak 5525

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Table 1. Attribution of Mean Particle Volume and Number to Tentatively Assigned Sources mean concentration (s.d.) (%) volume

number

Marylebone Road Emissions exhaust, solid mode (factor 3)

18.8 (12.1)

38.0 (18.8)

exhaust, nucleation mode (factor 4) brake dust (factor 2)

3.6 (2.8) 13.7(10.7)

27.4 (15.8) 1.7 (1.5)

resuspension (factor 7) subtotal

4.4(4.1) 40.5

4.8 (4.4) 71.9

Urban Background accumulation mode (factor 1)

12.8 (10.8)

6.3 (5.6)

suburban traffic (factor 5)

2.3 (2.0)

7.6 (7.9)

solid fuel/nitrate (factor 6) regional (factor 8)

8.4 (7.2) 2.5 (3.6)

2.0 (2.3) 2.7 (4.1)

cooking (factor 9)

6.7 (2.6)

6.6 (7.3)

regional (factor 10)

26.8 (18.1)

subtotal

59.5

3.0 (3.8) 28.2

whose maximum lies somewhere above 10 μm diameter. The latter is attributed to dust resuspension by heavy duty vehicles. Thorpe et al.,20 analyzing data from Marylebone Road showed an association of resuspension predominantly with heavy duty vehicles. Factor 8. The directional association predominantly with northerly winds suggests that this is a component of background air rather than being generated on Marylebone Road, and the very marked diurnal variation shows a peak in the middle of the day consistent with its relationship to concentrations of ozone. This rather minor contributor (2.7% of particle number and 2.5% of particle volume) with a mode in the number distribution at around 80 nm and in the volume distribution at just over 200 nm, appears to be an accumulation mode component regionally transported and mixed down to ground level predominantly at times when the urban boundary layer has extended to its greatest heights. Factor 9 shows strong associations with both southwesterly and northeasterly wind directions suggesting that it has little if any association with traffic on Marylebone Road. Its diurnal variation suggests a source predominantly in the evening. The size distribution with a mode in the number distribution at just over 20 nm and the volume distribution at around 320 nm suggests a combustion source and the similarity of size spectra to those reported by Buonanno et al.21 as associated with cooking suggests this source as the main contributor. This factor makes up 6.6% of particle number and 6.7% of particle volume, and although not large, is nonetheless significant. During the same campaign, Allan et al.14 reported that cooking aerosol inferred from AMS data contributed 34% of primary organic aerosol at a nearby site in central London. Factor 10 makes a large contribution (26.8%) to particle volume but only a small contribution (3.0%) to particle number. Its wind directional association predominantly with the northerly sector and lack of a clear diurnal variation associates it with background air from north London with only a modest contribution from the traffic on Marylebone Road. It has classic accumulation mode peaks at around 75 nm in the number distribution and 280 nm in the volume distribution and is probably

representative of well aged regional aerosol which makes a major contribution to concentrations within London.4 4.1. Relative Contributions of Emissions on Marylebone Road, and the Urban Background. Taking the assignments of factors indicated above, the associated volume and mass are shown in Table 1. This attributes 40.5% of volume to on-road emissions. Over the period of sampling, the mean PM10 mass at Marylebone Road was 45.6 μg m
3, and 28.1 μg m
3 at the urban background site at North Kensington. This implies a roadside increment to mass of 38.3%, which is in remarkably good agreement with the estimate of volume in Table 1 and SI Table S2 of 40.5%. The small difference is readily explicable in terms of different densities associated with the various particle types, and in particular the higher densities of the metal-rich brake dust and resuspended particles. The contribution of nonexhaust particles to the traffic increment is 44.7%, broadly consistent with estimates of 59% by both Amato et al. and Bukowiecki,6,22 explored below. The average particle number count at the North Kensington background site, measured by a condensation particle counter was 25 019 cm
3 over the sampling period and that at Marylebone Road derived from the SMPS/APS was 61,596 cm
3. On the basis of these figures, the Marylebone Road traffic contribution to particle number is 59.4%, compared to 71.9% estimated from the PMF analysis (Table 1). However, as the SMPS/APS omits particles counted by the CPC in the 7
15 nm range, it typically under-measures by about 30%, which would bring the numbers more closely into line. 4.2. Comparison with Other Studies. Quantification of brake wear and resuspended particles has proved problematic in the past, and there are few literature reports of disaggregated measurements. Thorpe et al.20 calculated the source strength of brake wear on Marylebone Road from CORINAIR emission factors, and estimated resuspension from the difference between total particle emissions and the sum of exhaust and abrasion emissions. They estimated that abrasion sources were responsible for 44
57% of total coarse (PM2.5
10) particle emissions, with resuspension accounting for 43
56%. Taking account of the fact that about onethird of the volume of factor 2 (brake wear) particles are in the fine (PM2.5) fraction, the ratio of factor 2 (brake wear) to factor 7 (resuspension) is around 2:1. However, the size distribution of brake wear particles in the urban background is virtually the same as that on Marylebone Road,15 and this factor is more likely to contain a component of the background than the resuspension particles, which are far larger and have a shorter lifetime. Gietl et al.15 estimated an abundance of barium of 1.1% in brake wear particles. Using this figure, and the barium concentration measured on Marylebone Road by Gietl et al.15 in March 2007 (i.e., seven months before the current study), mean brake wear particle concentrations on Marylebone Road are estimated as 1.3 μg m
3 based upon the roadside incremental concentration of barium, or 1.6 μg m
3 if the urban background concentration is not subtracted. This may be compared with the approximate mass concentration of factor 2 (brake wear) calculated as 13.7% of the mean particle mass of 45.6 μg m
3, which equates to 6.2 μg m
3. However, during the March 2007 campaign when the barium measurements were made, the roadside increment was on average 9.2 μg m
3 (ref 15) and 33.8% of 9.2 μg m
3 equates to 3.1 μg m
3, where 33.8% is the contribution of brake wear particles to the traffic increment. The divergence between the estimates of 3.1 μg m
3 derived from the PMF analysis and 1.3 (or 1.6) μg m
3 derived from the barium concentration may result from different weather conditions 5526

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Environmental Science & Technology during the two campaigns, or may reflect uncertainties in the CORINAIR emission factors used in calculating the percentage of barium in brake dust. Amato et al.6 used the ME-2 program applied to trace element data to estimate emissions of brake wear and resuspended dust particles from an urban background site in Barcelona. This showed that resuspension accounted for 17% of PM10 and application of PMF2 showed brake wear to account for 10% of PM10. This corresponds to 37% and 22% of the traffic contribution respectively. These percentages are larger than in our data (Table 1) especially in regard of resuspension, possibly a result of the dryer climatic conditions in Barcelona being more favorable to the resuspension of surface dust. Bukowiecki et al.22 reported emissions of brake dust and resuspended particles from a congested street canyon in Switzerland derived from an analysis of trace element concentrations by PMF. This showed brake wear to account for 21% of traffic related PM10 and resuspended road dust for 38%. Our application of PMF has proved especially successful in identifying the separate contributions from on-road traffic, and particularly the brake dust and resuspension factors which other techniques have had problems in elucidating. However, this work suggests that the size distributions of the four factors associated with on-road traffic are very distinct and hence clearly separated. These results contribute significantly to the very small data set available in the literature on nonexhaust emissions from road vehicles. It appears that the emissions of brake dust are broadly comparable to those in two other European studies but that particle resuspension is significantly lower at the Marylebone Road site, at least at the time of year sampled, than at sites in Spain and Switzerland. The technique shows considerable promise for further application to other sites and to identify other source categories of particulate matter.

’ ASSOCIATED CONTENT

bS

Supporting Information. Further details of the PMF method and weekday/weekend factor profiles. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: þ44 121 414 3494; fax: þ44 121 414 3709; e-mail: r.m. [email protected]. Present Addresses †

Institute of Environmental Assessment and Water Research (IDÆA), Consejo Superior de Investigaciones Cient
ificas (CSIC), C/LLuis Sol
e i Sabar
is S/N, 08028 barcelona, Spain

’ ACKNOWLEDGMENT This work was supported by the UK National Centre for Atmospheric Science and the European Union EUSAAR programme (contract ref 026140) and EUCAARI programme (contract ref 036833). The BOC Foundation contributed to the sampling campaign expenses. ’ REFERENCES (1) Air Quality Guidelines, Global Update 2005; World Health Organization, Regional Office for Europe, 2006.

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(2) Harrison, R. M.; Stedman, J.; Derwent, D. Why are PM10 concentrations in Europe not falling? New directions, atmospheric science perspectives special series. Atmos. Environ. 2008, 42, 603–606. (3) Harrison, R. M.; Yin, J. Particulate matter in the atmosphere: Which particle properties are important for its effects on health? Sci Total Environ. 2000, 249, 85–101. (4) Charron, A.; Harrison, R. M.; Quincey, P. What are the sources and conditions responsible for exceedences of the 24 h PM10 limit value (50 μg m
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(21) Buonanno, G.; Morawska, L.; Stabile, L. Particle emission factors during cooking activities. Atmos. Environ. 2009, 43, 3235–3242. (22) Bukowiecki, N.; Lienemann, P.; Hill, M.; Furger, M.; Richard, A.; Amato, F.; Prevot, A. S. H.; Baltensperger, U.; Buchmann, B.; Gehrig, R. PM10 emission factors for non-exhaust particles generated by road traffic in an urban street canyon and along a freeway in Switzerland. Atmos. Environ. 2010, 44, 2330–2340.

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