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A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads
ARTICLE IN PRESS
Atmospheric Environment 39 (2005) 6088–6097 www.elsevier.com/locate/atmosenv
A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads G. Omstedta,, B. Bringfeltb, C. Johanssonc,d a
Swedish Meteorological and Hydrological Institute, 601 76 Norrko¨ping, Sweden b ¨ Ortugsgatan 43, 603 79 Norrko¨ping, Sweden c Department of Applied Environmental Science, Stockholm university, SE-10691 Stockholm, Sweden d Environment & Health Protection Administration, Box 38 024, SE-10064 Stockholm, Sweden Received 12 March 2005; received in revised form 14 June 2005; accepted 27 June 2005
Abstract One of the most important parameters that controls the suspension of road dust particles in the air is road surface moisture. This is calculated every hour from a budget equation that takes into account precipitation, evaporation and runoff. During wet conditions a road dust layer is built up from road wear which strongly depends on the use of studded tyres and road sanding. The dust layer is reduced during dry road conditions by suspension of particles due to vehicle-induced turbulence. The dust layer is also reduced by wash-off due to precipitation. Direct non-tailpipe vehicle emissions due to the wear and tear of the road surface, brakes and tyres are accounted for in the traditional way as constant emission factors expressed as mass emitted per vehicle kilometre. The model results are compared with measurements from both a narrow street canyon in the city centre of Stockholm and from an open highway outside the city. The model is able to account for the main features in the day-to-day mean PM10 variability for the street canyon and for the highway. A peak in the PM10 concentration is typically observed in late winter and early spring in the Nordic countries where studded tyres are used. This behaviour is due to a combination of factors: frequent conditions with dry roads, high number of cars with studded tyres and an accumulated road dust layer that increases suspension of particles. The study shows that using a constant emission factor for PM10 or relating PM10 emissions to NOx cannot be used for prediction of day-to-day variations in PM10 concentrations in the traffic environments studied here. The model needs to describe variations in dust load, wetness of the road and how dust suspension interacts with these processes. r 2005 Elsevier Ltd. All rights reserved. Keywords: Road dust; Road wear; Suspension; Coarse particles; PM10; PM2.5
1. Introduction In urban areas fugitive dust emissions due to vehicles travelling on roads is the most important source of Corresponding author. Tel.: +46 11 495 84 46; fax: +46 11 495 80 01. E-mail address: [email protected] (G. Omstedt).
coarse particles (aerodynamic diameter between 2.5 and 10 mm), e.g. Pakkanen et al. (1996), Rogge et al. (1993), Chow et al. (1996) and Querol et al. (2004). Presently in Stockholm vehicle exhaust particles contribute less than 10% to the PM10 emissions due to road traffic (Forsberg et al., 2005). More than 90% is non-tailpipe emissions of mechanically generated particles. In other cities in Europe, exhaust particles contribute more or equally
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.06.037
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Nomenclature
fq
a
fRF
share of road wear particles in the total dust layer (0–1) share of studded tyres (0–1) ast b share of sand particles in the total dust layer (0–1) etotal total emission factor (mg (vehicle km)1) f direct ef direct part of the total emission factor (mg (vehicle km)1) suspension ef road dust suspension part of the total emission factor (mg (vehicle km)1) ref winter reference emission factor for particles during ef;PM winter conditions (mg (vehicle km)1) traffic E PM total emission of particles from a road (mg h1) Ep hourly potential evaporation (mm) F vehicle flow (vehicles h1)
to particle mass emissions when compared to nontailpipe emissions (Querol et al., 2004). The EU legislation (Council Directive 1999/30/EC) regulates PM10 to protect the health of the population and this requires tighter controls on the sources of PM10 in order to achieve the limit values. Many cities in Europe have severe problems meeting the limit values for PM10 and therefore abatement plans are necessary. In order to carry out efficient traffic and air quality management, a quantitative understanding of the processes controlling the emissions is necessary. For this purpose, validated model tools need to be developed. The day-to-day emission of coarse particles from roads is difficult to predict. It depends on meteorological factors and factors associated with the road surface and vehicle factors. Very few have attempted to consider all these processes in modelling PM10 emissions. An early model is the US-EPA model (EPA, 1993). This empirical model has been criticised for being not physically acceptable and thus unable to provide reliable estimates of PM10 emissions from paved roads (Venkatram, 2000). A modified version of the US-EPA model has been frequently used in Germany (Duering et al., 2002) but, due to large deviations between calculations and observations, empirically estimated emission factors as a function of ‘‘traffic situations’’ are now used (Duering et al., 2004). Gamez et al. (2001) proposed to calculate the PM10 emission as the sum of all primary PM10 sources: exhaust, vehicle components’ (tyre, brake, clutch) wear, road abrasion and emission resulting from material entering the road via external sources (for example, dirt inputs, sanding, etc.). The TRAKER road dust measurement system is a technique for estimation of potential road dust emissions (Etyemezian et al., 2003a). The system has
fSUSP g ke kDECAY l lw ls RF rg rr
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reduction function due to the moisture content of the road surface (0–1) hourly decay of road dust layer due to runoff by rain (0–1) hourly decay of road dust layer due to suspension (0–1) moisture amount in the road dust and on the road surface (mm) empirical factor for evaporation empirical factor for suspension of particles total normalised dust layer (0–1) normalised dust layer for road wear (0–1) normalised dust layer for sand (0–1) hourly water runoff (mm) hourly reduction of g due to evaporation (mm) hourly precipitation (mm)
been used for investigating temporal changes in emission potentials from paved roads in Treasure Valley, ID (Kuhns et al., 2003). These measurements show important dynamical variations in road dust emissions. For Nordic countries using studded tyres during wintertime, other models are used. For Norwegian road emissions, NILU uses an empirical model based on an assumption of linear relations between suspended particles: the fraction of heavy-duty vehicles and the fine particle fraction from roadwear. This expression is then multiplied by factors that depend on the use of studded tyres and road surface wetness (Dag Tonnesen, private communication). For Helsinki (Finland) Kukkonen et al. (2001) use an empirical model based on linear regression between NOx and PM10 concentrations.
2. Methodology and measurement sites The emission model has been evaluated against measurements in two different traffic environments: a rather well-defined street canyon and an open highway. Two different dispersion models were used for the calculation of dispersion from the street/road to the measurement points, the OSPM model for the street canyon site (Berkowicz, 2000) and a Gaussian line source model for the highway site (Gidhagen et al., 2004b). Model comparisons are first done for NOx to evaluate the performance of the dispersion models and the results have been presented by Gidhagen et al. (2004a) for the street canyon and by Gidhagen et al. (2004b) for the highway. For details on these models and the model performance we refer to these publications.
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2.1. The street canyon site Hornsgatan is a 24 m wide, four lane street surrounded by 24 m high houses on both sides (Gidhagen et al., 2004a). Traffic intensity is about 35,500 vehicles per day during weekdays with an average of 5% of heavyduty vehicles, mostly buses, of which almost everyone uses ethanol. More details regarding traffic conditions are given by Gidhagen et al. (2004a). NOx is measured using chemiluminescence analysers (Thermo Electron) at 3 m height and at roof level on a building, some 500 m east of Hornsgatan, in a less trafficked area (urban background). PM10 and PM2.5 are measured using a TEOM instrument (Tapered Element Oscillating Microbalance, Rupprecht and Pataschnik) equipped with two electrical ball-valves to automatically switch between the two PM inlets. A TEOM instrument is located at the urban background station Rosenlundsgatan, about 600 m from Hornsgatan and at 30 m height. Meteorological measurements (wind speed and direction, temperature, humidity, global radiation and precipitation) were taken in a 15-meter-high tower on the roof of a building close to the street canyon site. Cloud cover was obtained from the Mesan system (Ha¨ggmark et al., 2000), which is an operational analysis system based on the optimal interpolation technique. Observations from synoptic and automatic stations, radar and satellites were used. 2.2. The highway site The highway monitoring site is located 28 km north of Stockholm, along the principal highway E4 (see Gidhagen et al. (2004b) for a detailed description). The highway follows a north–south straight line, with open fields on both sides. A six-week monitoring campaign was undertaken from March to May 2003. PM10 and NOx were measured continuously 10 m from the road. Traffic volume was recorded by a permanent traffic counter located a few kilometres south of the monitoring site. Average traffic volume is 52,300 vehicles per day, with an average of 6% diesel-fuelled trucks and buses. Five per cent of the light-duty vehicles are dieselfuelled. During rush hours the traffic intensity is about 5000 vehicles per hour. Wind speed, direction and turbulence were registered by an ultrasonic monitor (Gill Instruments, type WindSonic) mounted on a 15 m tower. Temperature was registered at 2 m height and a thermistor bridge also measures the vertical gradient between 2 and 10 m height. Precipitation, humidity and global radiation were obtained from a weather station close to the highway a few kilometres south of the site (data collected by the National Road Administration). Background NOx levels were taken from a monitor located at a rural site some 45 km northwest of the experiment site. Background PM10 concentrations were
taken from a monitor located at a rural site some 90 km south of the experimental site. 2.3. Model description The model makes use of emission factors and traffic data for calculations of emissions of particles in traffic environments. Let E traffic denote total emission of PM particles from a road; then total E traffic , PM ¼ Fef
(1)
where PM can either be PM10 or PM2.5, F is number of vehicles per time unit and etotal is the total emission f factor (mg vkm1; vkm ¼ vehicle kilometre). The total emission factor can be divided into a direct part and a part that is due to suspension of road dust particles: etotal ¼ edirect þ esuspension . f f f
(2)
The direct emissions can be divided into exhaust pipe, tyre, brake and road wear: pipe components wear wear ¼ eexhaust þ evehicle þ eroad . edirect f f f f
(3) Two kinds of suspended particles are put into the model:
Particles
generated and subsequently suspended in the air caused by the wear of pavement. Particles suspended from traction sand put onto roads.
Traction sand is only used during winter. The wear is primarily due to the action of studded tyres during winter but also during summer tyres cause wear of roads. Dust layers are defined as l w p1:0 dust layer for road wear; l s p1:0 dust layer for sand:
ð4Þ
The total normalised dust layer l is a weighted sum l ¼ al w þ bl s ,
(5)
where a and b are the share of road wear particles and sand particles, respectively, in the total road dust layer. Here we assume equal contributions, i.e. a ¼ b ¼ 0:5. The emission factor for suspension of road dust is calculated as winter esuspension ¼ f q leref , f;PM f
(6)
where l increases due to road wear and sanding and is reduced due to runoff (washoff by rain or snow) and suspension. fq (Eq. 18) is a reduction factor related to the moisture content of the road surface. It is normalised, i.e. fqp1.0, and acts in two ways. During dry conditions when fq is increasing, the suspension increases. Then, l will decrease and after some time the
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suspension also will decrease. During wet conditions fq winter is small. eref denotes a reference emission factor f;PM that depends on type of particles (PM10 or PM2.5) and traffic environment. It can be estimated by field measurements scaling with NOx as tracer (see below). Under summer conditions the variations in the road dust layer are not so important; therefore we simplify Eq. (6) in the following way: summer esuspension ¼ f q eref . f;PM f
(7)
Winter is defined as the time when studded tyres are allowed on the road: October to April. For each hour lw is increased by kstast and reduced by runoff and suspension given by the factors fRF and fSUSP: l w;tþ1 ¼ ðl w;t þ kst ast Þf RF f SUSP ,
(8)
where t and t+1 denote the time step of 1 h. ast is the share of studded tyres, varying between 90% in the north and 40% in the south part of Sweden during December to March. To get lw normalised, kst is chosen so that the sum of the contributions from road wear is 1.0 by the end of the winter. For calculation of ls, running information of sanding occasions is needed. Since such data may not be easily available, they are calculated by using meteorological data. Sanding is assumed to occur during slippery conditions. If a day has rainfall or snowfall (as given by the hourly meteorological observations) and if the temperature or the dew point temperature is between 2 and +11C, then slippery conditions are assumed. To normalize the dust layer ls, it is increased for 1 h in sanding days by 1/N, where N is the number of such days during the winter season. ls is reduced hourly due to runoff and suspension in the same way as for lw. For the hour described above, the ‘‘new’’ ls is calculated as 1 f f ; (9) l s;tþ1 ¼ l s;t þ N RF SUSP for all other hours l s;tþ1 ¼ l s;t f RF f SUSP
(10)
is used. The total normalized dust layer is l tþ1 ¼ al w;tþ1 þ bl s;tþ1 .
(11)
lt+1 is used as input to the next hour. fRF and fSUSP are calculated using the moisture content of the road dust g, expressed in mm water equivalent. An hourly precipitation rr (mm) is added to initial gt and the sum is minimised to 1.0 mm. The rest goes as runoff by Eq. (17). gtþ1 ¼ minð1:0; gt þ rrÞrg .
(12)
gt is reduced hourly by the factor rg due to evaporation. Let emax (mmh1) denote the evaporation from the road dust layer during maximum moisture content gmax,
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which is assumed to be 1.0 mm. Then rg is calculated by emax , (13) rg ¼ exp gmax obtained by solving the differential equation
dg g ¼ emax dt gmax
(14)
formed by equalling the rate of drying the dust layer to maximum evaporation emax scaled down by the fraction g/gmax. emax is calculated using potential evaporation, Ep, given by the Penman formula (Penman, 1948): DRn þ ðrcp =ra Þde E p ¼ 3600 , (15) LðD þ gÞ where Ep is the so-called potential evaporation expected from a wet surface. Rn is the incoming net radiation, ra is the aerodynamic resistance, D is the derivative of saturation vapour pressure with respect to temperature, de is the saturation vapour pressure deficit (de ¼ esat(T)–esat(Td) where T and Td are the temperature and dew point temperature respectively), g is the psychrometer constant, r is the density of air, cp is the heat capacity and L is the latent heat of vaporisation. The net radiation is calculated using global radiation and cloud cover observations according to Nielsen et al. (1981). In the model, emax in Eq. (13) is calculated by emax ¼ ke E p ,
(16)
where ke is an empirical factor estimated as ke ¼ 0.075 in earlier development in other Swedish streets (Bringfelt et al., 1997). This value was found to give a reasonable decrease rate of the moisture content g also in the present study, by testing dry periods lasting up to 20 days. In the model, Ep was calculated hourly from observations. emax obtained with ke ¼ 0.075 in Eq. (16) was then substituted in Eq. (13). Putting rg into Eq. (12) gives approximately an exponential decrease of g to half the initial value after a few days. The modelled PM10 concentration was not found to be sensitive to the value of ke. However, for model generality, it is considered important that ke has a value that makes the moisture content g decrease at a realistic rate in dry periods. The large reduction of Ep to get emax is because Ep is potential without any reduction of evaporation. Substantial amounts of moisture may be present in the sand in limited areas on the road. The hourly water runoff, RF, is calculated as RFtþ1 ¼ maxððgtþ1 þ rrÞrg 1:0; 0:0Þ
(17)
and is used to get the hourly dust layer decay factor fRF, due to runoff. If precipitation is smaller than 1 mm, or if runoff is smaller than 2 mm, fRF is put to 1.0. For RF greater than 10 mm fRF ¼ 0.95. For RF between 2 and 10 mm, fRF is interpolated between 1.0 and 0.95. If the
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emission factor for PM10 (mg/v km)
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2000 1600 1200 800 400 0 2-Jan
2-Mar
1-May
30-Jun
29-Aug
28-Oct
27-Dec
Fig. 1. Emission factors for PM10 estimated by the tracer method. Hourly data from Hornsgatan/Sweden for the year 2000. Grey line is the running daily average.
runoff is small, there will be no reduction of the dust layer. If the runoff exceeds an upper limit, here taken to be 10 mm, then the reduction will be 5%. Rainfall and snowfall are treated in the same way. For snow this can be justified due to runoff caused by melting. It is possible to model simple delaying runoff until melting. However, clearing the road of snow is more difficult to describe. Increased moisture in the dust layer should reduce the source strength for suspension. This reduction factor, fq, has been derived from earlier studies (Bringfelt et al., 1997) as f q ¼ 1 0:93g.
(18)
Both rainfall and snowfall are considered as active in reducing suspension. The hourly relative decay of the dust layer due to reduction by suspension is calculated from f SUSP ¼ 1 kDECAY f q ,
(19)
where kDECAY is an empirical parameter. The course of the modelled PM10 concentration is sensitive to the value of kDECAY. In the present study, the value kDECAY ¼ 0.001 was obtained by trying some values to get the best covariation between calculated and measured PM10 concentrations. Earlier tests of the model in other Swedish streets also showed 0.001 to be the best value of kDECAY (Bringfelt et al., 1997). In dry conditions, Eqs. (18) and (19) with kDECAY ¼ 0.001 give fqE1 and fSUSP ¼ 0.999, so the dust layer will be modelled 1% smaller each hour. After 1 month of 744 identical hours, the dust layer will be about halved (scaled from 1 to 0.999744 ¼ 0.48), which seems to be a realistic estimate. The calculations should start at the beginning of the winter season when the dust layer is small. Several of the equations use a time step of 1 h based on hourly observations. Together, they give the solution for the budget of the total dust layer.
Table 1 Values of reference emission factors used in this work
PM10 (mg vkm1) PM2.5 (mg vkm1)
Winter
Summer
1200 150
200 30
2.4. Reference emission factors A critical parameter in this model is the reference emission factor. It sets a baseline for the model and should be estimated from situations with high suspension. A useful method for estimation of emissions from field measurements is scaling, with NOx as tracer. Emission factors for particles can be estimated using this method if background and roadside concentrations of particles and NOx are available: ! roadside C background NOx C PM PM PM ef ¼ ef , (20) C roadside C background NOx PM x where eNO is the emission factor for NOx, which is often f more well known than that for particles. The method calculates the total emission factor and therefore includes both direct emissions and emissions from the dust layer. Fig. 1 gives an example of analysed emission factors for PM10 using hourly data from Hornsgatan in Stockholm. The figure shows both the strong yearly variation and the much faster variation associated with wet and dry periods. From this analysis, and correspondingly for PM2.5, reference emission factors have been estimated, shown in Table 1.
3. Results In this paper we show the comparisons for PM10 and PM2.5 using the empirical emission model (no measurements of PM2.5 were done along the highway). The
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daily mean PM10 levels. The correlation coefficient is 0.77 and the measured and calculated mean concentrations are similar. The model is able to account for the main features in PM10 variability, especially the peak in PM10 concentrations in late winter and early spring. Some exceptions exist, e.g., the model calculates too high concentrations around the 30 of April. A possible explanation is effects of street cleaning which is not included in the model. Fig. 3b shows the same data as Fig. 3a but concentrations are now sorted according to size (for assessment of the EU directive it is the number of exceedances during the whole year that is important, not exactly when it occurs). Even though the model tends to underestimate concentrations, the agreement is good. For the 98-percentile the modelled value is less than 1% lower than measured data, for the 90-percentile about 10% lower and for the yearly mean 4% lower.
comparisons are done for daily mean values since the EU directive for PM10 does not require any higher resolution. 3.1. Street canyon site Comparisons between measured and modelled concentrations of PM10 and PM2.5 were done for the year 2000. Data for this year were also used in the street emission ceilings (SEC) exercise (http://aix.meng. auth.gr/sec/). These data are rather typical for Hornsgatan as shown by comparing PM10 levels during 5 years (Fig. 2). A pronounced seasonal variation with peak values during late winter and early spring is observed every year. There is no significant trend in PM10 levels since the measurements started at this site in 1995. Fig. 3 shows a comparison between measured and modelled
160 Street canyon Urban Background
140 PM10 (µg m-3)
120 100 80 60 40 20 0 jan- apr- jul- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec2000 2000 2000 2000 2000 2001 2001 2001 2001 2002 2002 2002 2002 2003 2003 2003 2003 2004 2004 2004 2004
Fig. 2. Measured concentrations of PM10 at the kerb side site (Hornsgatan) and urban background rooftop (Rosenlundsgatan) presented as a running monthly average. Measurement data using TEOM have been corrected by a factor of 1.2 to account for evaporative losses of PM.
Number of datapoints=358 Average measured=24.9 Average modelled=24.0 r=0.77
120
160 140 PM10-modelled (µg/m3)
PM10street - PM10roof (µg/m3)
160
80
40
120 100 80 60 40 yearlymean 90-percentile 98-percentile
20 0
0
(a)
measured background modelled 24.9 24.0 64.5 58.7 102.6 101.7
0 1-Jan
1-Mar
30-Apr
29-Jun
28-Aug
27-Oct
26-Dec
(b)
20 40
60 80 100 120 140 160
PM10-(measured-background) (µg /m3)
Fig. 3. (a) Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM10 (mg m3) at Hornsgatan for the year 2000. (b) Same results as (a) but sorted according to size.
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3.2. Highway site
The numbers of days exceeding the EU directive are 73 modelled and 78 measured. Fig. 4 presents the results for PM2.5. The correlation is somewhat lower than for PM10. The measured and modelled mean concentrations are similar.
Fig. 5 shows a comparison between measured and calculated hourly mean values of PM10 at the highway site. Background PM10 concentrations were taken from
30 25
Number of datapoints=356 Average measured=5.4 Average modelled=5.5 r=0.72
20
PM2.5-modelled (µg/m3)
PM2.5street-PM2.5roof (µg/m3)
30
10
20 15 10 5
modelled 5.5 11.4 16.9
0
0
(a)
measuredbackground yearly mean 5.4 90-percentile 10.1 98-percentile 16.2
0 1-Jan
1-Mar
30-Apr
29-Jun
28-Aug
27-Oct
26-Dec
(b)
5 10 15 20 25 30 PM2.5-(measured-background) (µg/m3)
Fig. 4. (a) Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM2.5 (mg m3) at Hornsgatan for the year 2000. (b) Same results as (a) but sorted according to size.
PM10E1-PM10background (µg/m3)
200
wind direction SSW-NNW
PM10-modelled (µg/m3)
200 160 120 80 Number of datapoints=511 Average mesured=22.9 Average modelled=22.2 r=0.68
40 0 0
160
40
80
120
160
200
PM10E1-PM10background(µg/m3)
120
80
40
0 20-Mar
27-Mar
3-Apr
10-Apr
17-Apr
24-Apr
1-May
8-May 3
15-May
Fig. 5. Comparison of measured (+) and modelled (solid line) hourly mean concentrations of PM10 (mg m ) at Vallstana¨s for the monitoring campaign 2003. The upper figure shows results for westerly winds.
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Table 2 Summary of results for the two different locations used in this study Vallstana¨sa
Hornsgatan
Number of data points Average measured (background) Average measured (local contribution) Average modelled Linear fits Correlation coefficient Root mean square error a
NOx (mg m3)
PM10 (mg m3)b
PM2
8277 31.2 155.3 132.5 y ¼ 0:59x þ 40 0.76 66.1
358 14.3 24.9 24.0 Y ¼ 0:80x þ 4:1 0.77 17.3
356 8.8 5.4 5.5 y ¼ 0:81x þ 1:2 0.72 2.9
5
(mg m3)b
NOx (mg m3)
PM10 (mg m3)
626 3.1 72.9 64.4 y ¼ 0:85x þ 2:1 0.76 32.0
511 8.1 22.9 22.2 y ¼ 0:89x þ 1:9 0.68 19.5
Only for westerly winds. Daily mean.
b
160
250
140
100
150
80 100
60 40
3.3. Sensitivity analysis The model was tested for different assumptions concerning the key parameters in the model. The main interest is the strong yearly variations observed in PM10 concentrations, as shown in Fig. 2. Running monthly average values are shown in Fig. 6. As a reference we use measured PM10 concentrations for Hornsgatan in the year 2000. The agreement between calculations with the complete model and measurements is good. In the first experiment we simplified the model (Eq. (6)) by using constant fq and l functions (fq ¼ l ¼ 1.0) and no summer sub-model (Eq. (7)), i.e. using only a constant emission factor. Under these assumptions the PM10 concentrations vary in a similar way as measured NOx concentrations. It is obvious that with this simplification it is not possible to describe the strong yearly variation in PM10 concentrations. In the second experiment we use a constant dust layer (l ¼ 1.0) and no summer sub-model. The calculated concentrations start too high, but increase in a similar way as measured PM10 concentrations. After the peak value is achieved the concentrations decrease but remain on a too high level. In the third experiment, only the summer sub-model was removed and then the calculated summer concentrations were underestimated. The main reason is that the road wear of the model (Eq. (8)) is not adjusted for summer conditions.
NOx (µg /m3)
200
120 PM10 (µg/m3)
a monitor located at a rural site some 90 km south of the experimental site. The scatter depicted in Fig. 5 is large but the correlation is reasonable. For the most well-defined meteorological conditions with westerly winds, orthogonal to the highway, the correlation is 0.68. A summary of the results from Vallstana¨s and Hornsgatan is given in Table 2, including results for comparison of measured and modelled concentrations of NOx.
50
20 0
0 1-Jan
1-Mar 30-Apr 29-Jun 28-Aug 27-Oct 26-Dec constant fq and l functions measured NOx constant l function measured PM10 complete model no summer sub model
Fig. 6. Sensitivity of the model for different assumptions. Calculated concentrations of PM10 (mg m3) for Hornsgatan presented as a running monthly average.
4. Discussion During wintertime the road wear is high due to the use of studded tyres. Traction sanding increases road wear due to the sand-paper effect (Kupiainen et al., 2005). Snow and ice keep the roads wet and therefore road dust suspension is small, which means that the local contribution of particle concentrations in air will also be small; this increases the dust layer. In early spring, evaporation rates increase due to higher solar radiation and higher temperatures, making the roads dry up more efficiently. This increases the suspension of road dust. Later in the spring the dust layer will therefore also decrease due to suspension. The model describes these processes rather well as shown in Fig. 6.
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Street sweeping is not considered in the present version of the model because its effects on air quality are not well understood. According to Kuhns et al. (2003) street sweeping with mechanical and vacuum sweepers is ineffective in reducing PM10 road dust emission in the short term. Their finding is confirmed by our own experiments in Stockholm (Norman and Johansson, 2005). On a longer time period, street sweeping can remove particles that may evolve into PM10, and then sweeping may have a beneficial effect on air quality. PM10 dust emissions may also be reduced over longer time scales by removing the debris found near gutters and road shoulders, which may be acting as a reservoir (Etyemezian et al., 2003b), but this is not considered here. According to the model the mean emission due to suspension of road dust for the street canyon is 205 mg (vehicle km)1. This may be compared to the exhaust emissions, which is 20–30 mg (vehicle km)1 (emission factors based on a Swedish vehicle exhaust emission model—EVA-model). Most of the exhaust PM is ultrafine mode (o100 nm) as shown in particle size distribution measurements in a road tunnel in Stockholm (Kristensson et al., 2004). In other cities with a higher number of diesel vehicles, some fraction of the PM mass may be larger than 2.5 mm (QUARG, 1996). Brake wear has been estimated for Stockholm based on the total amount of linings consumed and the total yearly amount of transports and communication (Westerlund and Johansson, 2002). They arrived at a total wear of 17 mg vkm1 for light-duty vehicles and 84 mg vkm1 for heavy-duty vehicles. Garg et al. (2000) found that 86% and 63% of the airborne PM was smaller than 10 and 2.5 mm in diameter, respectively. They also found that only about 35% of the brake pad mass loss was emitted as airborne; a large fraction may thus end up in water and soil recipients. Assuming that 35% is airborne, we estimate emission factors for brake wear of 5.9 and 29 mg vkm1 for light- and heavy-duty vehicles, respectively. Tyre wear is probably a minor source of airborne PM. USEPA gives an emission factor of 1.2 mg vkm1 for tyre wear (EPA, 1985). Based on roadside mass and chemical analysis of particles in North Carolina (USA) Abu–Allaban et al. (2003) did not find any tyre wear in the PM10 fraction. The sum of exhaust, brake and tyre wear is therefore about 35 mg vkm1, much less than the road dust emission due to road and sand wear ( 205 mg vkm1).
5. Conclusions A new model for calculation of emissions from vehicle-induced non-tailpipe particles (as PM10 and PM2.5) is presented. The model is compared with measurements, both from a narrow street canyon and
for an open highway, with good results. Similar results have been obtained earlier in other Swedish locations. The model is able to account for the main features in the PM10 variability, especially the peak in PM10 concentrations in late winter and early spring that is commonly experienced in the Nordic countries where studded tyres are used. The study shows that using a simple emission factor for PM10, or relating PM10 emissions to NOx, cannot be used for prediction of PM10 concentration in the traffic environments studied here. The model needs to describe variations in dust load, wetness of the road and how suspension interacts with these processes. The model includes several empirical factors that may be site specific and it therefore needs to be tested on other streets and roads where the traffic and climate conditions can be significantly different. It is still under development but the results presented here are promising. The model is now incorporated in two air quality model systems: one for the Stockholm area (http:// www.slb.nu/) and the other for Sweden including a database of Swedish roads (http://www.luftkvalitet.se). It will therefore be tested in a number of different practical applications.
Acknowledgments This work was financed by the Swedish Road Administration and the Swedish Environmental Protection Agency (via the SNAP programme; http:// www.snap.se). The technical support with measurements by the staff at SLB of the Environment and Health Protection Administration is gratefully acknowledged. Many thanks to Hans Backstro¨m for important contributions to the model.
References Abu-Allaban, M., Gillies, J.A., Gertler, A.W., Clayton, R., Proffitt, D., 2003. Tailpipe, resuspended road dust, and brake-wear emission factors from on-road vehicles. Atmospheric Environment 37, 5283–5293. Berkowicz, R., 2000. OSPM-A parameterised street pollution model. Environmental Monitoring and Assessment 65, 323–331. Bringfelt, B., Backstro¨m, H., Kindell, S., Omstedt, G., Persson, C., Ullerstig, A., 1997. Calculation of PM10 concentrations in Swedish cities. SMHI Report RMK No. 76. Chow, J.C., Watson, J.G., Lowenthal, D.H., Countess, R.J., 1996. Sources and chemistry of PM10 aerosol in Santa Barbara County, CA. Atmospheric Environment 30 (9), 1489–1499. Duering, I., Lohmeyer, A., Lutz, M., Reichenbaecher, W., 2002. Estimation of the ‘‘non exhaust pipe’’ PM10 emissions of streets for practical traffic air pollution modelling. In: Proceedings of the 11th International Symposium Transport
ARTICLE IN PRESS G. Omstedt et al. / Atmospheric Environment 39 (2005) 6088–6097 and Air Pollution in Graz, Vol. 1. VKM-THD, University of Technology, pp. 309–316. Duering, I., Braechlin, W., Boesinger, R., Mueller, M., Lohmeyer, A., 2004. Experiences when modelling Roadside PM10 concentrations. Proceedings at the Ninth International Conference on Harmonisation within atmospheric dispersion modelling for regulatory purposes. GarmischPartenkirchen, Germany. EPA, 1985. Compilation of air pollutant emission factors. Fourth edition, Vol. II: Mobile sources. US Environmental Protection Agency, Ann Arbor, USA. EPA, 1993. Emission factor documentation for AP-42. Section 13.2.1, Paved Roads. EPA Contract No. 68-D0-0123, Work Assignment No. 44, MRI Project No. 9712-44. Etyemezian, V., Kuhns, H., Gillies, J., Green, M., Pitchford, M., Watson, J., 2003a. Vehicle-based road dust emissions measurements: I—methods and calibration. Atmospheric Environment 37, 4559–4571. Etyemezian, V., Kuhns, H., Gillies, J., Chow, J., Hendrickson, K., McGrown, M., Pitchford, M., 2003b. Vehicle-based road dust emissions measurements (III):effect of speed, traffic volume, location, and season on PM10 road dust emissions in the Treasure Valley, ID. Atmospheric Environment 37, 4583–4593. Forsberg, B., Hansson, H.-C., Johansson, C., Areskoug, H., Persson, K., Ja¨rvholm, B., 2005. Comparative health impact assessment of local and regional particulate air pollutions in Scandinavia. Ambio 34, 11–19. Gamez, A.J., Berkowicz, R., Ketzel, M., Lohmeyer, A., Reichenba¨cher, W., 2001. Determination of the ‘‘non exhausted pipe’’ PM10 emissions of roads for practical traffic air pollution modelling. In: Proceedings at the Seventh International Conference on Harmonisation Within Atmospheric Dispersion Modelling for Regulatory Purposes. Garg, B.D., Cadle, S.H., Mulawa, P.A., Groblicki, P.J., 2000. Brake wear particulate matter emissions. Environmental Science and Technology 34, 4463–4469. Gidhagen, L., Johansson, C., Langner, J., Olivares, G., 2004a. Simulation of NOx and ultrafine particles in a street canyon in Stockholm, Sweden. Atmospheric Environment 38, 2029–2044. Gidhagen, L., Johansson, C., Omstedt, G., Langner, J., Olivares, G., 2004b. Model simulations of NOx and ultrafine particles close to a Swedish highway. Environment Science and Technology 38, 6730–6740. Ha¨ggmark, L., Ivarsson, K.I., Gollvik, S., Olofsson, P.O., 2000. Mesan, an operational mesoscale analysis system. Tellus 52A (1), 20. Kristensson, A., Johansson, C., Westerholm, R., Swietlicki, E., Gidhagen, L., Wideqvist, U., Vesely, V., 2004. Real-world
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traffic emission factors of gases and particles measured in a road tunnel in Stockholm, Sweden. Atmospheric Environment 38, 657–673. Kuhns, H., Etyemezian, V., Green, M., Hendrickson, K., McGrown, M., Barton, K., Pitchford, M., 2003. Vehiclebased road dust emissions measurements-Part II: effect of precipitation, wintertime road sanding, and street sweepers on inferred PM10 emission potentials from paved and unpaved roads. Atmospheric Environment 37, 4573–4582. Kukkonen, J., Ha¨rko¨nen, J., Karppinen, A., Pohjola, M., Pietarila, H., Koskentalo, T., 2001. A semi-empirical model for urban PM10 concentrations, and its evaluation against data from an urban measurement network. Atmospheric Environment 35, 4433–4442. Kupiainen, K.J., Tervahattu, H., Ra¨isa¨nen, M., Ma¨ka¨la¨, T., Aurela, M., Hillamo, R., 2005. Size and composition of particles from pavement wear, tires, and traction sanding. Environmental Science and Technology 39, 699–706. Nielsen, L.B., Prahm, L.P., Berkowicz, R., Condradsen, K., 1981. Net incoming radiation estimated from hourly global radiation and/or cloud observation. Journal of Climate 1, 255. Norman, M., Johansson, C., 2005. Evaluation of different measures to reduce road dust emissions in Stockholm. Presented at the 5th Urban Air Quality Conference, Valencia, Spain, March 29–31. Pakkanen, T.A., Kerminen, V.-M., Korhonen, C.H., Hillamo, R.E., Aarnio, P., Koskentalo, T., Quarge, 1996. Airborne particulate matter in the United Kingdom. Quality of urban air review group, University of Birmingham, School of Chemistry, Edgbaston, Birmingham, UK. Penman, H.L., 1948. Natural evapotranspiration from open water, bare soil and grass. Proceedings of the Royal Society of London Series A 193, 120–145. Querol, X., Alastuey, A., Ruiz, C.R., Artin˜ano, B., Hansson, H.C., Harrison, R.M., Buringh, E., ten Brink, H.M., Lutz, M., Bruckmann, P., Straehl, P., Schneider, J., 2004. Speciation and origin of PM10 and PM2.5 in selected European cities. Atmospheric Environment 38, 6547–6555. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., 1993. Sources of fine organic aerosol. 3. road dust, tire debris, and organometallic brake lining dust: Roads as sources and sinks. Environmental Science and Technology 27, 1892–1904. Venkatram, A., 2000. A critique of empirical emission factor models: a case study of the AP-42 model for estimating PM10 emissions from paved roads. Atmospheric Environment 34, 1–11. Westerlund, K.G., Johansson, C., 2002. Emissions of metals and particulate matter due to wear of brake linings in Stockholm. In: Brebbia, C.A., Martin-Duque, J.F. (Eds.), Air Pollution X.
Atmospheric Environment 39 (2005) 6088–6097 www.elsevier.com/locate/atmosenv
A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads G. Omstedta,, B. Bringfeltb, C. Johanssonc,d a
Swedish Meteorological and Hydrological Institute, 601 76 Norrko¨ping, Sweden b ¨ Ortugsgatan 43, 603 79 Norrko¨ping, Sweden c Department of Applied Environmental Science, Stockholm university, SE-10691 Stockholm, Sweden d Environment & Health Protection Administration, Box 38 024, SE-10064 Stockholm, Sweden Received 12 March 2005; received in revised form 14 June 2005; accepted 27 June 2005
Abstract One of the most important parameters that controls the suspension of road dust particles in the air is road surface moisture. This is calculated every hour from a budget equation that takes into account precipitation, evaporation and runoff. During wet conditions a road dust layer is built up from road wear which strongly depends on the use of studded tyres and road sanding. The dust layer is reduced during dry road conditions by suspension of particles due to vehicle-induced turbulence. The dust layer is also reduced by wash-off due to precipitation. Direct non-tailpipe vehicle emissions due to the wear and tear of the road surface, brakes and tyres are accounted for in the traditional way as constant emission factors expressed as mass emitted per vehicle kilometre. The model results are compared with measurements from both a narrow street canyon in the city centre of Stockholm and from an open highway outside the city. The model is able to account for the main features in the day-to-day mean PM10 variability for the street canyon and for the highway. A peak in the PM10 concentration is typically observed in late winter and early spring in the Nordic countries where studded tyres are used. This behaviour is due to a combination of factors: frequent conditions with dry roads, high number of cars with studded tyres and an accumulated road dust layer that increases suspension of particles. The study shows that using a constant emission factor for PM10 or relating PM10 emissions to NOx cannot be used for prediction of day-to-day variations in PM10 concentrations in the traffic environments studied here. The model needs to describe variations in dust load, wetness of the road and how dust suspension interacts with these processes. r 2005 Elsevier Ltd. All rights reserved. Keywords: Road dust; Road wear; Suspension; Coarse particles; PM10; PM2.5
1. Introduction In urban areas fugitive dust emissions due to vehicles travelling on roads is the most important source of Corresponding author. Tel.: +46 11 495 84 46; fax: +46 11 495 80 01. E-mail address: [email protected] (G. Omstedt).
coarse particles (aerodynamic diameter between 2.5 and 10 mm), e.g. Pakkanen et al. (1996), Rogge et al. (1993), Chow et al. (1996) and Querol et al. (2004). Presently in Stockholm vehicle exhaust particles contribute less than 10% to the PM10 emissions due to road traffic (Forsberg et al., 2005). More than 90% is non-tailpipe emissions of mechanically generated particles. In other cities in Europe, exhaust particles contribute more or equally
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.06.037
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Nomenclature
fq
a
fRF
share of road wear particles in the total dust layer (0–1) share of studded tyres (0–1) ast b share of sand particles in the total dust layer (0–1) etotal total emission factor (mg (vehicle km)1) f direct ef direct part of the total emission factor (mg (vehicle km)1) suspension ef road dust suspension part of the total emission factor (mg (vehicle km)1) ref winter reference emission factor for particles during ef;PM winter conditions (mg (vehicle km)1) traffic E PM total emission of particles from a road (mg h1) Ep hourly potential evaporation (mm) F vehicle flow (vehicles h1)
to particle mass emissions when compared to nontailpipe emissions (Querol et al., 2004). The EU legislation (Council Directive 1999/30/EC) regulates PM10 to protect the health of the population and this requires tighter controls on the sources of PM10 in order to achieve the limit values. Many cities in Europe have severe problems meeting the limit values for PM10 and therefore abatement plans are necessary. In order to carry out efficient traffic and air quality management, a quantitative understanding of the processes controlling the emissions is necessary. For this purpose, validated model tools need to be developed. The day-to-day emission of coarse particles from roads is difficult to predict. It depends on meteorological factors and factors associated with the road surface and vehicle factors. Very few have attempted to consider all these processes in modelling PM10 emissions. An early model is the US-EPA model (EPA, 1993). This empirical model has been criticised for being not physically acceptable and thus unable to provide reliable estimates of PM10 emissions from paved roads (Venkatram, 2000). A modified version of the US-EPA model has been frequently used in Germany (Duering et al., 2002) but, due to large deviations between calculations and observations, empirically estimated emission factors as a function of ‘‘traffic situations’’ are now used (Duering et al., 2004). Gamez et al. (2001) proposed to calculate the PM10 emission as the sum of all primary PM10 sources: exhaust, vehicle components’ (tyre, brake, clutch) wear, road abrasion and emission resulting from material entering the road via external sources (for example, dirt inputs, sanding, etc.). The TRAKER road dust measurement system is a technique for estimation of potential road dust emissions (Etyemezian et al., 2003a). The system has
fSUSP g ke kDECAY l lw ls RF rg rr
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reduction function due to the moisture content of the road surface (0–1) hourly decay of road dust layer due to runoff by rain (0–1) hourly decay of road dust layer due to suspension (0–1) moisture amount in the road dust and on the road surface (mm) empirical factor for evaporation empirical factor for suspension of particles total normalised dust layer (0–1) normalised dust layer for road wear (0–1) normalised dust layer for sand (0–1) hourly water runoff (mm) hourly reduction of g due to evaporation (mm) hourly precipitation (mm)
been used for investigating temporal changes in emission potentials from paved roads in Treasure Valley, ID (Kuhns et al., 2003). These measurements show important dynamical variations in road dust emissions. For Nordic countries using studded tyres during wintertime, other models are used. For Norwegian road emissions, NILU uses an empirical model based on an assumption of linear relations between suspended particles: the fraction of heavy-duty vehicles and the fine particle fraction from roadwear. This expression is then multiplied by factors that depend on the use of studded tyres and road surface wetness (Dag Tonnesen, private communication). For Helsinki (Finland) Kukkonen et al. (2001) use an empirical model based on linear regression between NOx and PM10 concentrations.
2. Methodology and measurement sites The emission model has been evaluated against measurements in two different traffic environments: a rather well-defined street canyon and an open highway. Two different dispersion models were used for the calculation of dispersion from the street/road to the measurement points, the OSPM model for the street canyon site (Berkowicz, 2000) and a Gaussian line source model for the highway site (Gidhagen et al., 2004b). Model comparisons are first done for NOx to evaluate the performance of the dispersion models and the results have been presented by Gidhagen et al. (2004a) for the street canyon and by Gidhagen et al. (2004b) for the highway. For details on these models and the model performance we refer to these publications.
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2.1. The street canyon site Hornsgatan is a 24 m wide, four lane street surrounded by 24 m high houses on both sides (Gidhagen et al., 2004a). Traffic intensity is about 35,500 vehicles per day during weekdays with an average of 5% of heavyduty vehicles, mostly buses, of which almost everyone uses ethanol. More details regarding traffic conditions are given by Gidhagen et al. (2004a). NOx is measured using chemiluminescence analysers (Thermo Electron) at 3 m height and at roof level on a building, some 500 m east of Hornsgatan, in a less trafficked area (urban background). PM10 and PM2.5 are measured using a TEOM instrument (Tapered Element Oscillating Microbalance, Rupprecht and Pataschnik) equipped with two electrical ball-valves to automatically switch between the two PM inlets. A TEOM instrument is located at the urban background station Rosenlundsgatan, about 600 m from Hornsgatan and at 30 m height. Meteorological measurements (wind speed and direction, temperature, humidity, global radiation and precipitation) were taken in a 15-meter-high tower on the roof of a building close to the street canyon site. Cloud cover was obtained from the Mesan system (Ha¨ggmark et al., 2000), which is an operational analysis system based on the optimal interpolation technique. Observations from synoptic and automatic stations, radar and satellites were used. 2.2. The highway site The highway monitoring site is located 28 km north of Stockholm, along the principal highway E4 (see Gidhagen et al. (2004b) for a detailed description). The highway follows a north–south straight line, with open fields on both sides. A six-week monitoring campaign was undertaken from March to May 2003. PM10 and NOx were measured continuously 10 m from the road. Traffic volume was recorded by a permanent traffic counter located a few kilometres south of the monitoring site. Average traffic volume is 52,300 vehicles per day, with an average of 6% diesel-fuelled trucks and buses. Five per cent of the light-duty vehicles are dieselfuelled. During rush hours the traffic intensity is about 5000 vehicles per hour. Wind speed, direction and turbulence were registered by an ultrasonic monitor (Gill Instruments, type WindSonic) mounted on a 15 m tower. Temperature was registered at 2 m height and a thermistor bridge also measures the vertical gradient between 2 and 10 m height. Precipitation, humidity and global radiation were obtained from a weather station close to the highway a few kilometres south of the site (data collected by the National Road Administration). Background NOx levels were taken from a monitor located at a rural site some 45 km northwest of the experiment site. Background PM10 concentrations were
taken from a monitor located at a rural site some 90 km south of the experimental site. 2.3. Model description The model makes use of emission factors and traffic data for calculations of emissions of particles in traffic environments. Let E traffic denote total emission of PM particles from a road; then total E traffic , PM ¼ Fef
(1)
where PM can either be PM10 or PM2.5, F is number of vehicles per time unit and etotal is the total emission f factor (mg vkm1; vkm ¼ vehicle kilometre). The total emission factor can be divided into a direct part and a part that is due to suspension of road dust particles: etotal ¼ edirect þ esuspension . f f f
(2)
The direct emissions can be divided into exhaust pipe, tyre, brake and road wear: pipe components wear wear ¼ eexhaust þ evehicle þ eroad . edirect f f f f
(3) Two kinds of suspended particles are put into the model:
Particles
generated and subsequently suspended in the air caused by the wear of pavement. Particles suspended from traction sand put onto roads.
Traction sand is only used during winter. The wear is primarily due to the action of studded tyres during winter but also during summer tyres cause wear of roads. Dust layers are defined as l w p1:0 dust layer for road wear; l s p1:0 dust layer for sand:
ð4Þ
The total normalised dust layer l is a weighted sum l ¼ al w þ bl s ,
(5)
where a and b are the share of road wear particles and sand particles, respectively, in the total road dust layer. Here we assume equal contributions, i.e. a ¼ b ¼ 0:5. The emission factor for suspension of road dust is calculated as winter esuspension ¼ f q leref , f;PM f
(6)
where l increases due to road wear and sanding and is reduced due to runoff (washoff by rain or snow) and suspension. fq (Eq. 18) is a reduction factor related to the moisture content of the road surface. It is normalised, i.e. fqp1.0, and acts in two ways. During dry conditions when fq is increasing, the suspension increases. Then, l will decrease and after some time the
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suspension also will decrease. During wet conditions fq winter is small. eref denotes a reference emission factor f;PM that depends on type of particles (PM10 or PM2.5) and traffic environment. It can be estimated by field measurements scaling with NOx as tracer (see below). Under summer conditions the variations in the road dust layer are not so important; therefore we simplify Eq. (6) in the following way: summer esuspension ¼ f q eref . f;PM f
(7)
Winter is defined as the time when studded tyres are allowed on the road: October to April. For each hour lw is increased by kstast and reduced by runoff and suspension given by the factors fRF and fSUSP: l w;tþ1 ¼ ðl w;t þ kst ast Þf RF f SUSP ,
(8)
where t and t+1 denote the time step of 1 h. ast is the share of studded tyres, varying between 90% in the north and 40% in the south part of Sweden during December to March. To get lw normalised, kst is chosen so that the sum of the contributions from road wear is 1.0 by the end of the winter. For calculation of ls, running information of sanding occasions is needed. Since such data may not be easily available, they are calculated by using meteorological data. Sanding is assumed to occur during slippery conditions. If a day has rainfall or snowfall (as given by the hourly meteorological observations) and if the temperature or the dew point temperature is between 2 and +11C, then slippery conditions are assumed. To normalize the dust layer ls, it is increased for 1 h in sanding days by 1/N, where N is the number of such days during the winter season. ls is reduced hourly due to runoff and suspension in the same way as for lw. For the hour described above, the ‘‘new’’ ls is calculated as 1 f f ; (9) l s;tþ1 ¼ l s;t þ N RF SUSP for all other hours l s;tþ1 ¼ l s;t f RF f SUSP
(10)
is used. The total normalized dust layer is l tþ1 ¼ al w;tþ1 þ bl s;tþ1 .
(11)
lt+1 is used as input to the next hour. fRF and fSUSP are calculated using the moisture content of the road dust g, expressed in mm water equivalent. An hourly precipitation rr (mm) is added to initial gt and the sum is minimised to 1.0 mm. The rest goes as runoff by Eq. (17). gtþ1 ¼ minð1:0; gt þ rrÞrg .
(12)
gt is reduced hourly by the factor rg due to evaporation. Let emax (mmh1) denote the evaporation from the road dust layer during maximum moisture content gmax,
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which is assumed to be 1.0 mm. Then rg is calculated by emax , (13) rg ¼ exp gmax obtained by solving the differential equation
dg g ¼ emax dt gmax
(14)
formed by equalling the rate of drying the dust layer to maximum evaporation emax scaled down by the fraction g/gmax. emax is calculated using potential evaporation, Ep, given by the Penman formula (Penman, 1948): DRn þ ðrcp =ra Þde E p ¼ 3600 , (15) LðD þ gÞ where Ep is the so-called potential evaporation expected from a wet surface. Rn is the incoming net radiation, ra is the aerodynamic resistance, D is the derivative of saturation vapour pressure with respect to temperature, de is the saturation vapour pressure deficit (de ¼ esat(T)–esat(Td) where T and Td are the temperature and dew point temperature respectively), g is the psychrometer constant, r is the density of air, cp is the heat capacity and L is the latent heat of vaporisation. The net radiation is calculated using global radiation and cloud cover observations according to Nielsen et al. (1981). In the model, emax in Eq. (13) is calculated by emax ¼ ke E p ,
(16)
where ke is an empirical factor estimated as ke ¼ 0.075 in earlier development in other Swedish streets (Bringfelt et al., 1997). This value was found to give a reasonable decrease rate of the moisture content g also in the present study, by testing dry periods lasting up to 20 days. In the model, Ep was calculated hourly from observations. emax obtained with ke ¼ 0.075 in Eq. (16) was then substituted in Eq. (13). Putting rg into Eq. (12) gives approximately an exponential decrease of g to half the initial value after a few days. The modelled PM10 concentration was not found to be sensitive to the value of ke. However, for model generality, it is considered important that ke has a value that makes the moisture content g decrease at a realistic rate in dry periods. The large reduction of Ep to get emax is because Ep is potential without any reduction of evaporation. Substantial amounts of moisture may be present in the sand in limited areas on the road. The hourly water runoff, RF, is calculated as RFtþ1 ¼ maxððgtþ1 þ rrÞrg 1:0; 0:0Þ
(17)
and is used to get the hourly dust layer decay factor fRF, due to runoff. If precipitation is smaller than 1 mm, or if runoff is smaller than 2 mm, fRF is put to 1.0. For RF greater than 10 mm fRF ¼ 0.95. For RF between 2 and 10 mm, fRF is interpolated between 1.0 and 0.95. If the
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emission factor for PM10 (mg/v km)
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2000 1600 1200 800 400 0 2-Jan
2-Mar
1-May
30-Jun
29-Aug
28-Oct
27-Dec
Fig. 1. Emission factors for PM10 estimated by the tracer method. Hourly data from Hornsgatan/Sweden for the year 2000. Grey line is the running daily average.
runoff is small, there will be no reduction of the dust layer. If the runoff exceeds an upper limit, here taken to be 10 mm, then the reduction will be 5%. Rainfall and snowfall are treated in the same way. For snow this can be justified due to runoff caused by melting. It is possible to model simple delaying runoff until melting. However, clearing the road of snow is more difficult to describe. Increased moisture in the dust layer should reduce the source strength for suspension. This reduction factor, fq, has been derived from earlier studies (Bringfelt et al., 1997) as f q ¼ 1 0:93g.
(18)
Both rainfall and snowfall are considered as active in reducing suspension. The hourly relative decay of the dust layer due to reduction by suspension is calculated from f SUSP ¼ 1 kDECAY f q ,
(19)
where kDECAY is an empirical parameter. The course of the modelled PM10 concentration is sensitive to the value of kDECAY. In the present study, the value kDECAY ¼ 0.001 was obtained by trying some values to get the best covariation between calculated and measured PM10 concentrations. Earlier tests of the model in other Swedish streets also showed 0.001 to be the best value of kDECAY (Bringfelt et al., 1997). In dry conditions, Eqs. (18) and (19) with kDECAY ¼ 0.001 give fqE1 and fSUSP ¼ 0.999, so the dust layer will be modelled 1% smaller each hour. After 1 month of 744 identical hours, the dust layer will be about halved (scaled from 1 to 0.999744 ¼ 0.48), which seems to be a realistic estimate. The calculations should start at the beginning of the winter season when the dust layer is small. Several of the equations use a time step of 1 h based on hourly observations. Together, they give the solution for the budget of the total dust layer.
Table 1 Values of reference emission factors used in this work
PM10 (mg vkm1) PM2.5 (mg vkm1)
Winter
Summer
1200 150
200 30
2.4. Reference emission factors A critical parameter in this model is the reference emission factor. It sets a baseline for the model and should be estimated from situations with high suspension. A useful method for estimation of emissions from field measurements is scaling, with NOx as tracer. Emission factors for particles can be estimated using this method if background and roadside concentrations of particles and NOx are available: ! roadside C background NOx C PM PM PM ef ¼ ef , (20) C roadside C background NOx PM x where eNO is the emission factor for NOx, which is often f more well known than that for particles. The method calculates the total emission factor and therefore includes both direct emissions and emissions from the dust layer. Fig. 1 gives an example of analysed emission factors for PM10 using hourly data from Hornsgatan in Stockholm. The figure shows both the strong yearly variation and the much faster variation associated with wet and dry periods. From this analysis, and correspondingly for PM2.5, reference emission factors have been estimated, shown in Table 1.
3. Results In this paper we show the comparisons for PM10 and PM2.5 using the empirical emission model (no measurements of PM2.5 were done along the highway). The
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daily mean PM10 levels. The correlation coefficient is 0.77 and the measured and calculated mean concentrations are similar. The model is able to account for the main features in PM10 variability, especially the peak in PM10 concentrations in late winter and early spring. Some exceptions exist, e.g., the model calculates too high concentrations around the 30 of April. A possible explanation is effects of street cleaning which is not included in the model. Fig. 3b shows the same data as Fig. 3a but concentrations are now sorted according to size (for assessment of the EU directive it is the number of exceedances during the whole year that is important, not exactly when it occurs). Even though the model tends to underestimate concentrations, the agreement is good. For the 98-percentile the modelled value is less than 1% lower than measured data, for the 90-percentile about 10% lower and for the yearly mean 4% lower.
comparisons are done for daily mean values since the EU directive for PM10 does not require any higher resolution. 3.1. Street canyon site Comparisons between measured and modelled concentrations of PM10 and PM2.5 were done for the year 2000. Data for this year were also used in the street emission ceilings (SEC) exercise (http://aix.meng. auth.gr/sec/). These data are rather typical for Hornsgatan as shown by comparing PM10 levels during 5 years (Fig. 2). A pronounced seasonal variation with peak values during late winter and early spring is observed every year. There is no significant trend in PM10 levels since the measurements started at this site in 1995. Fig. 3 shows a comparison between measured and modelled
160 Street canyon Urban Background
140 PM10 (µg m-3)
120 100 80 60 40 20 0 jan- apr- jul- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec- mar- jun- sep- dec2000 2000 2000 2000 2000 2001 2001 2001 2001 2002 2002 2002 2002 2003 2003 2003 2003 2004 2004 2004 2004
Fig. 2. Measured concentrations of PM10 at the kerb side site (Hornsgatan) and urban background rooftop (Rosenlundsgatan) presented as a running monthly average. Measurement data using TEOM have been corrected by a factor of 1.2 to account for evaporative losses of PM.
Number of datapoints=358 Average measured=24.9 Average modelled=24.0 r=0.77
120
160 140 PM10-modelled (µg/m3)
PM10street - PM10roof (µg/m3)
160
80
40
120 100 80 60 40 yearlymean 90-percentile 98-percentile
20 0
0
(a)
measured background modelled 24.9 24.0 64.5 58.7 102.6 101.7
0 1-Jan
1-Mar
30-Apr
29-Jun
28-Aug
27-Oct
26-Dec
(b)
20 40
60 80 100 120 140 160
PM10-(measured-background) (µg /m3)
Fig. 3. (a) Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM10 (mg m3) at Hornsgatan for the year 2000. (b) Same results as (a) but sorted according to size.
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3.2. Highway site
The numbers of days exceeding the EU directive are 73 modelled and 78 measured. Fig. 4 presents the results for PM2.5. The correlation is somewhat lower than for PM10. The measured and modelled mean concentrations are similar.
Fig. 5 shows a comparison between measured and calculated hourly mean values of PM10 at the highway site. Background PM10 concentrations were taken from
30 25
Number of datapoints=356 Average measured=5.4 Average modelled=5.5 r=0.72
20
PM2.5-modelled (µg/m3)
PM2.5street-PM2.5roof (µg/m3)
30
10
20 15 10 5
modelled 5.5 11.4 16.9
0
0
(a)
measuredbackground yearly mean 5.4 90-percentile 10.1 98-percentile 16.2
0 1-Jan
1-Mar
30-Apr
29-Jun
28-Aug
27-Oct
26-Dec
(b)
5 10 15 20 25 30 PM2.5-(measured-background) (µg/m3)
Fig. 4. (a) Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM2.5 (mg m3) at Hornsgatan for the year 2000. (b) Same results as (a) but sorted according to size.
PM10E1-PM10background (µg/m3)
200
wind direction SSW-NNW
PM10-modelled (µg/m3)
200 160 120 80 Number of datapoints=511 Average mesured=22.9 Average modelled=22.2 r=0.68
40 0 0
160
40
80
120
160
200
PM10E1-PM10background(µg/m3)
120
80
40
0 20-Mar
27-Mar
3-Apr
10-Apr
17-Apr
24-Apr
1-May
8-May 3
15-May
Fig. 5. Comparison of measured (+) and modelled (solid line) hourly mean concentrations of PM10 (mg m ) at Vallstana¨s for the monitoring campaign 2003. The upper figure shows results for westerly winds.
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Table 2 Summary of results for the two different locations used in this study Vallstana¨sa
Hornsgatan
Number of data points Average measured (background) Average measured (local contribution) Average modelled Linear fits Correlation coefficient Root mean square error a
NOx (mg m3)
PM10 (mg m3)b
PM2
8277 31.2 155.3 132.5 y ¼ 0:59x þ 40 0.76 66.1
358 14.3 24.9 24.0 Y ¼ 0:80x þ 4:1 0.77 17.3
356 8.8 5.4 5.5 y ¼ 0:81x þ 1:2 0.72 2.9
5
(mg m3)b
NOx (mg m3)
PM10 (mg m3)
626 3.1 72.9 64.4 y ¼ 0:85x þ 2:1 0.76 32.0
511 8.1 22.9 22.2 y ¼ 0:89x þ 1:9 0.68 19.5
Only for westerly winds. Daily mean.
b
160
250
140
100
150
80 100
60 40
3.3. Sensitivity analysis The model was tested for different assumptions concerning the key parameters in the model. The main interest is the strong yearly variations observed in PM10 concentrations, as shown in Fig. 2. Running monthly average values are shown in Fig. 6. As a reference we use measured PM10 concentrations for Hornsgatan in the year 2000. The agreement between calculations with the complete model and measurements is good. In the first experiment we simplified the model (Eq. (6)) by using constant fq and l functions (fq ¼ l ¼ 1.0) and no summer sub-model (Eq. (7)), i.e. using only a constant emission factor. Under these assumptions the PM10 concentrations vary in a similar way as measured NOx concentrations. It is obvious that with this simplification it is not possible to describe the strong yearly variation in PM10 concentrations. In the second experiment we use a constant dust layer (l ¼ 1.0) and no summer sub-model. The calculated concentrations start too high, but increase in a similar way as measured PM10 concentrations. After the peak value is achieved the concentrations decrease but remain on a too high level. In the third experiment, only the summer sub-model was removed and then the calculated summer concentrations were underestimated. The main reason is that the road wear of the model (Eq. (8)) is not adjusted for summer conditions.
NOx (µg /m3)
200
120 PM10 (µg/m3)
a monitor located at a rural site some 90 km south of the experimental site. The scatter depicted in Fig. 5 is large but the correlation is reasonable. For the most well-defined meteorological conditions with westerly winds, orthogonal to the highway, the correlation is 0.68. A summary of the results from Vallstana¨s and Hornsgatan is given in Table 2, including results for comparison of measured and modelled concentrations of NOx.
50
20 0
0 1-Jan
1-Mar 30-Apr 29-Jun 28-Aug 27-Oct 26-Dec constant fq and l functions measured NOx constant l function measured PM10 complete model no summer sub model
Fig. 6. Sensitivity of the model for different assumptions. Calculated concentrations of PM10 (mg m3) for Hornsgatan presented as a running monthly average.
4. Discussion During wintertime the road wear is high due to the use of studded tyres. Traction sanding increases road wear due to the sand-paper effect (Kupiainen et al., 2005). Snow and ice keep the roads wet and therefore road dust suspension is small, which means that the local contribution of particle concentrations in air will also be small; this increases the dust layer. In early spring, evaporation rates increase due to higher solar radiation and higher temperatures, making the roads dry up more efficiently. This increases the suspension of road dust. Later in the spring the dust layer will therefore also decrease due to suspension. The model describes these processes rather well as shown in Fig. 6.
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Street sweeping is not considered in the present version of the model because its effects on air quality are not well understood. According to Kuhns et al. (2003) street sweeping with mechanical and vacuum sweepers is ineffective in reducing PM10 road dust emission in the short term. Their finding is confirmed by our own experiments in Stockholm (Norman and Johansson, 2005). On a longer time period, street sweeping can remove particles that may evolve into PM10, and then sweeping may have a beneficial effect on air quality. PM10 dust emissions may also be reduced over longer time scales by removing the debris found near gutters and road shoulders, which may be acting as a reservoir (Etyemezian et al., 2003b), but this is not considered here. According to the model the mean emission due to suspension of road dust for the street canyon is 205 mg (vehicle km)1. This may be compared to the exhaust emissions, which is 20–30 mg (vehicle km)1 (emission factors based on a Swedish vehicle exhaust emission model—EVA-model). Most of the exhaust PM is ultrafine mode (o100 nm) as shown in particle size distribution measurements in a road tunnel in Stockholm (Kristensson et al., 2004). In other cities with a higher number of diesel vehicles, some fraction of the PM mass may be larger than 2.5 mm (QUARG, 1996). Brake wear has been estimated for Stockholm based on the total amount of linings consumed and the total yearly amount of transports and communication (Westerlund and Johansson, 2002). They arrived at a total wear of 17 mg vkm1 for light-duty vehicles and 84 mg vkm1 for heavy-duty vehicles. Garg et al. (2000) found that 86% and 63% of the airborne PM was smaller than 10 and 2.5 mm in diameter, respectively. They also found that only about 35% of the brake pad mass loss was emitted as airborne; a large fraction may thus end up in water and soil recipients. Assuming that 35% is airborne, we estimate emission factors for brake wear of 5.9 and 29 mg vkm1 for light- and heavy-duty vehicles, respectively. Tyre wear is probably a minor source of airborne PM. USEPA gives an emission factor of 1.2 mg vkm1 for tyre wear (EPA, 1985). Based on roadside mass and chemical analysis of particles in North Carolina (USA) Abu–Allaban et al. (2003) did not find any tyre wear in the PM10 fraction. The sum of exhaust, brake and tyre wear is therefore about 35 mg vkm1, much less than the road dust emission due to road and sand wear ( 205 mg vkm1).
5. Conclusions A new model for calculation of emissions from vehicle-induced non-tailpipe particles (as PM10 and PM2.5) is presented. The model is compared with measurements, both from a narrow street canyon and
for an open highway, with good results. Similar results have been obtained earlier in other Swedish locations. The model is able to account for the main features in the PM10 variability, especially the peak in PM10 concentrations in late winter and early spring that is commonly experienced in the Nordic countries where studded tyres are used. The study shows that using a simple emission factor for PM10, or relating PM10 emissions to NOx, cannot be used for prediction of PM10 concentration in the traffic environments studied here. The model needs to describe variations in dust load, wetness of the road and how suspension interacts with these processes. The model includes several empirical factors that may be site specific and it therefore needs to be tested on other streets and roads where the traffic and climate conditions can be significantly different. It is still under development but the results presented here are promising. The model is now incorporated in two air quality model systems: one for the Stockholm area (http:// www.slb.nu/) and the other for Sweden including a database of Swedish roads (http://www.luftkvalitet.se). It will therefore be tested in a number of different practical applications.
Acknowledgments This work was financed by the Swedish Road Administration and the Swedish Environmental Protection Agency (via the SNAP programme; http:// www.snap.se). The technical support with measurements by the staff at SLB of the Environment and Health Protection Administration is gratefully acknowledged. Many thanks to Hans Backstro¨m for important contributions to the model.
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