Evaluation of a modelling system for predicting the concentrations of PM2.5 in an urban area

Evaluation of a modelling system for predicting the concentrations of PM2.5 in an urban area

ARTICLE IN PRESS Atmospheric Environment 42 (2008) 4517–4529 www.elsevier.com/locate/atmosenv Evaluation of a modelling system for predicting the co...

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ARTICLE IN PRESS

Atmospheric Environment 42 (2008) 4517–4529 www.elsevier.com/locate/atmosenv

Evaluation of a modelling system for predicting the concentrations of PM2.5 in an urban area M. Kauhaniemia,, A. Karppinenb, J. Ha¨rko¨nenb, A. Kousac, B. Alaviippolaa, T. Koskentaloc, P. Aarnioc, T. Elola¨hdec, J. Kukkonenb a

Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland c Helsinki Metropolitan Area Council, P.O. Box 521, 00521 Helsinki, Finland b

Received 5 November 2007; received in revised form 11 January 2008; accepted 28 January 2008

Abstract We present a modelling system that contains a treatment of the emissions and atmospheric dispersion of fine particulate matter (PM2.5) on an urban scale, combined with a statistical model for estimating the contribution of long-range transported aerosols. The model of PM2.5 emissions includes exhaust emissions, cold starts and driving, as well as, nonexhaust emissions originated from urban vehicular traffic. The influence of primary vehicular emissions from the road and street network was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. We have computed hourly sequential time series of the PM2.5 concentrations in 2002 in a numerical grid in the Helsinki Metropolitan Area. The predicted results were compared against measured data at two locations in central Helsinki: urban roadside station of Vallila and urban background station of Kallio. The predicted daily average PM2.5 concentrations agreed well with the measured values; e.g., the index of agreement values were 0.83 and 0.86 at Vallila and Kallio, respectively, and the absolute values of fractional bias p0.13. As expected, the scatter of data points is substantially wider for the hourly concentration values; e.g., the index of agreement values were 0.69 and 0.74. We also computed the spatial concentration distributions of PM2.5. The predicted contribution from long-range transport to the street level PM2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The emissions originated from cold starts and driving were responsible for o4% of the annual average concentrations at the roadside and urban stations. The model can potentially be used as a practical tool of assessment of urban PM2.5 contributions in various European regions. r 2008 Elsevier Ltd. All rights reserved. Keywords: Urban pollution; Model; Evaluation; PM2.5; CAR-FMI

1. Introduction Aerosol particle concentrations in urban areas are originated from several source categories, such as Corresponding author. Fax: +358 17 162301.

E-mail address: Mari.Kauhaniemi@fmi.fi (M. Kauhaniemi). 1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.01.071

local vehicular traffic, long-range transport (LRT), industrial emissions and contributions from natural sources. The coarse fraction (particles with aerodynamic diameters from 2.5 to 10 mm) tend to consist mainly of soil, sea salt, biogenic and airborne dust components, whereas the fine fraction (diameters below 2.5 mm) consists of contributions

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from mobile (mainly diesel) and stationary combustion processes, and secondary aerosols (such as sulphates, nitrates and ammonium). In Nordic countries, atmospheric LRT constitutes an important part of the total urban background PM2.5 concentrations (e.g., Kukkonen et al., 2008; Johansson et al., 1999). In urban areas, the temporal and spatial variation of both the particle number and PM10 concentrations are commonly closely related to local meteorology and traffic flows. However, these dependencies are more moderate for PM2.5, caused mainly by the substantial longrange transported (LRT’ed) background. Holmes and Morawska (2006) have reviewed various models that have been developed and applied for evaluating the atmospheric dispersion of particles. Clearly, these can be classified according to physical scale, theoretical basis, type of application (e.g., street canyon or roadside), and whether these contain detailed treatments for aerosol processes. However, studies that address operational models for the urban scale modelling of PM2.5, and evaluate the predictions of such methods against data, are very scarce. In this study, we present and evaluate a modelling system that contains a treatment of the emissions and dispersion of fine particulate matter on an urban scale, combined with a semi-empirical model for LRT’ed aerosols. The model applied on an urban scale is a Gaussian plume model, CAR-FMI (contaminants in the air from a road), applied combined with a meteorological pre-processing model, MPP-FMI. The performance of the CAR-FMI model has previously been evaluated both against the results of field measurement campaigns regarding NOx, NO2 and O3 (e.g., Oettl et al., 2001; Levitin et al., 2005), PM2.5 (Tiitta et al., 2002), and the data of urban concentration monitoring networks in Finland and in the UK (e.g., Kousa et al., 2001; Sokhi et al., 2008). Hussein et al. (2007) and Pohjola et al. (2007) evaluated a modelling system that contains CARFMI, MPP-FMI and the aerosol dynamic model UHMA (University of Helsinki, Model for Aerosol Processes) or alternatively, MONO32 (Monodisperse aerosol process model using 32 differential equations). Kukkonen et al. (2008) evaluated the statistical model for assessment of LRT’ed proportion of PM2.5 (the same model that is applied here) against data in the UK and in Finland. They concluded that the model could be a practical tool of assessment in various European regions in order to provide reliable estimates of LRT contributions.

The main objectives of this study are to present the modelling system for emissions and atmospheric dispersion of PM2.5, and evaluate its performance against the measured concentrations at two urban stations. We have also analysed the physical reasons for the differences of model predictions and measured data. Evaluation of the model provides insight on whether it would be a useful tool of assessment in other European urban areas. The more specific objectives include (i) the quantification of the relative contributions of LRT and various local source categories on the street level concentrations, and (ii) the investigation of the spatial concentration distributions. 2. Materials and methods 2.1. Experimental data 2.1.1. Meteorological measurements The locations of the meteorological measurement stations are presented in Fig. 1. We used meteorological observations from the stations at Helsinki–Vantaa airport (about 15 km north of the centre of Helsinki) and Helsinki–Isosaari (an island about 10 km south of the centre of Helsinki), and the sounding observations from the Jokioinen observatory (90 km northwest). 2.1.2. Air quality measurements on regional and urban scales The EMEP stations (co-operative programme for monitoring and evaluating of the long-range

Fig. 1. The location of the relevant EMEP stations (A¨hta¨ri, Uto¨ and Virolahti), the urban air quality monitoring stations (Vallila and Kallio) and the meteorological stations (Helsinki–Vantaa airport, Helsinki–Isosaari and Jokioinen), the data of which was used in this study. The Helsinki Metropolitan Area contains four cities: Helsinki, Espoo, Vantaa and Kauniainen.

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transmission of air pollutants in Europe) that are located nearest to Helsinki are Uto¨, A¨hta¨ri and Virolahti (Fig. 1). The following concentrations are measured daily at the EMEP stations: (i) SO2 4 (sulphate), (ii) the sum of NO 3 (nitrate) and HNO3 (nitrogen acid), and (iii) the sum of NH+ 4 (ammonium) and NH3 (ammonia). The sulphate, nitrate and ammonium ions are in particulate form, while nitrogen acid and ammonia are gaseous compounds in atmospheric conditions. The sampling and reporting period of the EMEP values starts at 06 UTC (in accordance with meteorological conventions). For a detailed description of the sampling and analysis methods, the reader is referred to Leinonen (1999). We have used the PM2.5 measurements at the stations of Vallila and Kallio in central Helsinki (Fig. 1). These stations are operated by the Helsinki Metropolitan Area Council (YTV). The station of Vallila represents urban roadside conditions; the station of Kallio is an urban background station. The measurement height at both stations is 4.0 m. The station of Vallila is situated in a park at a distance of 14 m from the edge of the Ha¨meentie road. The average weekday traffic volume of Ha¨meentie was 13,000 vehicles day1 in 2001. The heights of the buildings in the vicinity of the station, at the other side of the Ha¨meentie road and surrounding the park, range from 10 to 15 m. The Ha¨meentie road is fairly wide; there are four lanes for cars and additionally two lanes for trams. The station of Kallio is located at the edge of a sports ground. The busiest streets in the vicinity of the station are Helsinginkatu at a distance of 80 m and Sturenkatu at a distance of 300 m. The average weekday traffic volume of Helsinginkatu was 7800 vehicles day1 in 2001. The station of Kallio is expected to represent the exposures that are characteristic of the centre of Helsinki. At both of these urban stations, the concentrations of PM2.5 were measured with Eberline FH 62 I-R that is based on b-attenuation method. The flow rates of continuous instruments were calibrated twice a year with mass flow meters (Bronchorst model F-112AC-HA-00-V). The mass measurement of both Eberlines was calibrated by calibration foils. 2.2. Mathematical models 2.2.1. Meteorological pre-processing The relevant meteorological parameters for the atmospheric dispersion models are evaluated using

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data produced by a meteorological pre-processing model (Karppinen et al., 2000). The MPP-FMI model utilises meteorological synoptic and sounding observations, and its output consists of estimates of the hourly time series of the relevant atmospheric turbulence parameters (the Monin–Obukhov length scale, the friction velocity and the convective velocity scale) and the mixing height of the atmospheric boundary layer. 2.2.2. Evaluation of the traffic flows and emissions We conducted an emission inventory in the Helsinki Metropolitan Area in 2002, which included the emissions from vehicular traffic, for the network of roads and streets within the area. We have also surveyed the emissions of other traffic sources, such as harbours and marine traffic, and aviation, and those of major stationary sources. The influence of residential small-scale combustion was not included in the dispersion modelling, as the spatial distribution of the small-scale combustion units is not known with sufficient accuracy. However, we have evaluated the total amount of emissions originated from domestic combustion in the area. The traffic volumes and average travel speeds of each traffic link were computed using EMME/2 transportation planning system (INRO, 1994), which assigns the trips (from zone to zone) to links of a network model. The model allows for the diurnal and daily variations both in traffic volumes and speeds, and traffic emissions. The emission factors of cars in city traffic are based on traffic cycle measurements in Helsinki. The vehicular PM2.5 emissions were modelled to be dependent on vehicle travel velocity, separately for 14 main vehicle categories. These correlations are based on nationally conducted vehicle emission measurements (Laurikko, 1998). The contribution of nonexhaust emissions that originate from the vehicles, such as material from brakes and catalytic converters, and the suspended particulate matter from street surfaces caused by the local traffic flow was estimated semi-empirically (Tiitta et al., 2002). 2.2.3. Urban dispersion modelling The atmospheric dispersion of vehicular emissions has been evaluated using a roadside dispersion model, CAR-FMI (Ha¨rko¨nen, 2002). The dispersion equation is based on a semi-analytic solution of the Gaussian dilution equation for a finite line source (Luhar and Patil, 1989). The model utilises

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the meteorological input data evaluated with the meteorological pre-processing model MPP-FMI. The dispersion parameters are modelled as function of the Monin–Obukhov length, the friction velocity and the mixing height (Gryning et al., 1987); these quantities are computed by the MPPFMI model. Traffic-originated turbulence is modelled with a semi-empirical treatment (Petersen, 1980). The model includes a treatment for the basic reactions of nitrogen oxides, oxygen and ozone, using a receptor-oriented discrete parcel method, and the dry deposition of the fine particles. 2.2.4. Evaluation of the long-range transported concentrations of PM2.5 The LRT’ed contribution to urban particulate matter was evaluated by using a statistical model (Karppinen et al., 2004; Kukkonen et al., 2008) that utilises, as input values, the daily sulphate, nitrate and ammonium ion concentrations measured at the EMEP stations. For a more detailed discussion of this model, the reader is referred to Kukkonen et al. (2008); a brief overview is in the following. The afore-mentioned EMEP measurements of sulphate and the sum of nitrate and nitrogen acid, together with the sum of ammonium and ammonia, are reported as equivalent masses of sulphur and nitrogen, respectively. We therefore define the ionsum (Cion) as follows:  C ion ¼ 3:0ðSO2 4 ÞS þ 4:4ðNO3 þ HNO3 ÞN

þ 1:3ðNHþ 4 þ NH3 ÞN ,

(1)

where the subscripts S and N denote that the mass had been given as the equivalent mass of sulphur or nitrogen. These values were converted to equivalent  + masses of the ions SO2 4 , NO3 and NH4 , using the conversion factors 3.0, 4.4 and 1.3, respectively. This conversion was necessary to make the particulate mass concentration variables comparable. The unit of Cion is the same as that used for the  + concentrations of SO2 4 , NO3 and NH4 ; we have 3 used in this study mg m . The measured daily averaged urban air PM2.5 concentrations were associated with the ion-sum values as follows: C PM2:5 ¼ k1 C ion þ k0 ,

(2)

where k1 and k0 are regression coefficients determined experimentally. The terms k1Cion and k0 were interpreted to represent contributions from LRT’ed and all local sources (local traffic in this study), respectively.

In this study, we have used the value of the regression coefficient k1 ¼ 1.64, based on the statistical regression of the data measured at the same three EMEP stations and the same two urban stations as in this study; however, these correspond to a different time period, in 1998–2000 (Karppinen et al., 2004). The values of Cion were determined separately for each day in 2002. 2.2.5. The concentration of PM2.5 originated from all local sources and LRT Traffic particulates are not only directly emitted exhaust particles from the engines, but also include particles from wear on road, tires and brakes, and airborne dust from road surfaces. The total concentration of PM2.5 in an urban area can be written as a simple semi-empirical parameterisation: C PM2:5 ¼ ð1 þ a þ cÞC tr;e PM2:5 þ k 1 C ion þ b,

(3)

tr;e where the terms C tr;e PM2:5 and aC PM2:5 are the concentrations originated from vehicular traffic exhaust and non-exhaust emissions, respectively (both of these without the emissions associated with cold starts and driving), cC tr;e PM2:5 are the concentrations from cold starts and driving, and b denotes the contribution of all other local sources, except for vehicular traffic. The term k1Cion represents LRT. The contribution of non-exhaust particulate matter emissions has been estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions without cold start and driving emissions. In Eq. (3), the suspension (and re-suspension) of particulate matter from road surfaces could be contained either in the non-exhaust coefficient ‘a’ or in the term ‘b’ (non-vehicular local sources). However, in field concentration measurements, it is commonly not possible to differentiate the vehicular non-exhaust and suspension sources. We have therefore included by definition the suspension of particulate matter to the contribution of nonexhaust particulate matter emissions. We have used here a coefficient a ¼ 1.8, determined in a field measurement campaign by Tiitta et al. (2002). The cold start and cold driving emissions were modelled mainly based on laboratory emission measurements (Laurikko, 1998). These emissions were estimated separately for the days, during which the average temperature To0 1C and TX0 1C, respectively, and in terms of the days of the week and the pre-heating of engine. The coefficients in the above-mentioned equation, ‘c’, are defined in Table 1.

ARTICLE IN PRESS M. Kauhaniemi et al. / Atmospheric Environment 42 (2008) 4517–4529 Table 1 Coefficients for the emissions originated from cold starts and cold driving for two categories of days defined in terms of the average ambient temperature, separately for workdays and weekends Seasons

Workdays

Weekend

To0 1C, no pre-heating To0 1C, pre-heating TX0 1C

0.48 0.32 0.22

0.39 0.25 0.17

The coefficients have been evaluated separately for vehicles equipped with and without the pre-heating of engine.

Pre-heating is used in 41% of the vehicles during days with sub-zero average temperatures. According to the tabulated values, as expected, the preheating of engines substantially reduces the cold start and driving emissions. For days with an average sub-zero temperature, an average value of the coefficient c ¼ 0.4. We have also assumed that the influence of other local stationary sources except for vehicular traffic is negligible (b ¼ 0). Within the model, the values of C tr;e PM2:5 vary on an hourly basis, the values of c and Cion on a daily basis, while a and k1 are defined to be constants during any selected year. The evaluation method, as written in Eq. (3), contains three parameters, which have to be determined semi-experimentally (a, c and k1). However, the numerical values of these parameters have been determined based on various datasets that are independent of those to be predicted in this study.

2.3. Statistical parameters Four statistical parameters were computed: the index of agreement (IA), the correlation coefficient (COR), the normalised mean square error (NMSE) and the fractional bias (FB). The parameters IA, COR and NMSE are measures of the correlation of the predicted and observed time series of concentrations, while FB is a measure of the agreement of the mean concentrations. The IA varies from 0.0 (theoretical minimum) to 1.0 (perfect agreement between the observed and predicted values). FB ranges from 2.0 to +2.0 in cases of extreme under- and over-prediction, respectively. Values of the FB that are equal to 0.67 and +0.67 are equivalent to under- and overprediction by a factor of two, respectively.

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3. Results and discussion We have computed the hourly concentrations of fine particulate matter (PM2.5) in the Helsinki Metropolitan Area for 1 year, 2002. The computations included 4087 road and street segments, and the concentrations were computed for approximately 20,000 receptor points. The receptor grid intervals range from 20 m in the vicinity of the major roads in the area to 500 m on the outskirts of the area. The variable receptor grid was required in order to evaluate isoconcentration curves with adequate accuracy. 3.1. Comparison of model predictions with urban measurements 3.1.1. The statistical analysis of measured and predicted concentration time series We have presented in Table 2 the mean, the maximum and the standard deviation, together with the above-mentioned statistical parameters for the measured and predicted daily time series of the PM2.5 concentrations in 2002 for the urban stations. The corresponding data in case of hourly values is presented in Table 3. The IA values of the predicted and measured daily time series of PM2.5 concentrations are 0.83 and 0.86 at the stations of Vallila and Kallio, the COR values are 0.74 and 0.77, and the FB values are 0.13 and 0.09. At both stations, both the average predicted daily PM2.5 concentrations and their temporal variation therefore agrees well with the observed data. As expected, the statistical parameters regarding the corresponding hourly concentrations indicate the same agreement of the average values (FB) as for the daily data, but slightly worse agreement of the temporal variations. The same data has also been presented as scatter plots in Fig. 2a–d. The overall bias is small for the data at both stations. As expected, the scatter of data points is substantially wider for the hourly concentration values, and there are a number of hourly cases, for which the observed values are clearly higher than the predicted ones. The above-mentioned model performance parameters can be compared to those that have been previously evaluated by Kousa et al. (2001) using a similar modelling system, viz. the CAR-FMI model in combination with an urban dispersion model for stationary sources, and measured regional background concentrations. Regarding the station of

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Table 2 The statistical analysis of the predicted and measured daily average time series of PM2.5 concentrations at the monitoring stations of Vallila and Kallio in 2002 Statistical parameter

Vallila

Kallio

Predicted Mean (mg m3) Maximum (mg m3) Standard deviation (mg m3) Index of agreement (IA) Pearson’s correlation coefficient (COR) Normalised mean square error (NMSE) Fractional bias (FB) Number of data

Observed

11.2 39.0 5.31

9.90 52.0 6.75

Predicted 7.92 35.2 4.80

0.83 0.74 0.16 0.13 360

Observed 8.64 42.5 5.89 0.86 0.77 0.16 0.09

360

358

358

The statistical parameters have been defined in the text.

Table 3 The statistical analysis of the predicted and measured hourly average time series of PM2.5 concentrations at the monitoring stations of Vallila and Kallio in 2002 Statistical parameter

Mean (mg m3) Maximum (mg m3) Standard deviation (mg m3) Index of agreement (IA) Pearson’s correlation coefficient (COR) Normalised mean square error (NMSE) Fractional bias (FB) Number of data

Vallila

Kallio

Predicted

Observed

Predicted

Observed

11.3 50.2 6.47

10.4 132 8.48

8.06 37.9 5.13

9.07 128 7.36

0.69 0.51 0.40 0.09 8301

0.74 0.62 0.36 0.12 8301

8323

8323

The statistical parameters have been defined in the text.

Vallila, the IA and COR values for hourly data were better for NO2 than those determined for PM2.5 in this study, while the FB values were worse in case of NO2. It is more difficult to predict the short-term temporal variations of the PM2.5 concentrations, compared with those of NO2, caused by the difficulties to model reliably the temporal variations of the emissions of non-exhaust particulate matter. However, a simple semi-empirical modelling system in case of PM2.5 is expected a priori to predict fairly well the average concentration levels. However, in case of NO2, the average concentrations depend on factors that are challenging to model, such as the spatial and temporal variability of ozone concentrations in an urban area. As the LRT’ed contribution is substantial, it is reasonable to consider also how the model would compare to measurements, if the emissions origi-

nated from local emission sources would not be included. Kukkonen et al. (2008) have analysed in detail the performance of the LRT model applied here, against the concentration measurements in UK and in Finland. For instance, the correlation of ion-sum values with the PM2.5 data measured at two EMEP stations in southern Finland were very high at both of the stations considered; the correlation coefficient squared R2 varied from 0.77 to 0.83 for daily average values during annual or half-annual periods. This provides confidence that the ion-sum is a good proxy variable for the LRT’ed PM2.5, when extensive time periods are considered. However, during shorter periods, for instance in case of LRT’ed smoke from wild-land fires, the model performance can deteriorate. Most of the deviations of predicted and measured hourly concentrations in the urban area are therefore caused by inaccuracies in the urban scale modelling.

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VALLILA

KALLIO

55

55

50

50 2:1

45

1:1

2:1

30 1:2

25 20

Observed (µg/m3)

Observed (µg/m3)

40

35

15

35 30

= 0.54 N = 360 y = 0.94x - 0.63

5

20

10

0 0

5

10 15 20 25 30 35 40 45 50 55

0

Predicted (µg/m3) VALLILA

10 15 20 25 30 35 40 45 50 55 Predicted (µg/m3)

140

130

130 2:1

1:1

120

110

110

100

100

90 80 70

1:2

60 50

Observed (µg/m3)

Observed (µg/m3)

5

KALLIO

140

1:1

2:1

90 80 1:2

70 60 50 40

40

10

R2 = 0.60 N = 358 y = 0.95x + 1.15

5

0

20

1:2

25

15 R2

10

30

1:1

45

40

120

4523

R2 = 0.26 N = 8301 y = 0.67x + 2.76

30 20 10

R2 = 0.38 N = 8323 y = 0.89x + 1.90

0

0 0 10 20 30 40 50 60 70 80 90 100110120130140 Predicted (µg/m3)

0 10 20 30 40 50 60 70 80 90 100110120130140 Predicted (µg/m3)

Fig. 2. Comparison between measured and predicted daily (a, b) and hourly (c, d) concentrations of PM2.5 in 2002, for the monitoring stations at Vallila and Kallio. We have also indicated lines showing an agreement between predictions and data by a factor of two, and a linear numerical fit to the data (grey line) and its equation. R2 ¼ the correlation coefficient squared and N ¼ the number of data points.

3.1.2. Seasonal variation of the measured and predicted concentrations Clearly, the most unfavourable meteorological conditions for the efficient mixing of pollution include stable atmospheric conditions with a low wind speed or calm; in northern areas, the presence

of a strong ground-based inversion is an additionally important factor. Such meteorological conditions are most common during the winter half of the year. For instance, Kukkonen et al. (2005) found that the best meteorological prediction variables for the local-scale PM10 episodes in four European

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cities were the temporal evolution of the temperature inversions and atmospheric stability and, in some of the cases, wind speed. The seasonal variation of the predicted and observed PM2.5 concentrations has been presented in Fig. 3a–h. The seasonal variation of traffic volumes in Finnish cities is typically o20% on a monthly basis. The main factors that influence the seasonal variation of the concentrations of PM2.5 in urban air are therefore the corresponding variation in the meteorological conditions, LRT’ed episodes, the suspension of dust from the street surfaces, and the temporal variation in the emissions from smallscale combustion. The highest measured PM2.5 concentrations occurred in early spring (March and April), summer (July and August) and early autumn (September) at both stations. The PM2.5 episodes in southern Finland in 2002 have been investigated by Niemi et al. (2005); they concluded that several of these were mainly caused by LRT, in particular, the episodes on 15–20 March, 12–14 and 26–29 August, and 4–9 September. The LRT’ed PM2.5 during those periods was in most cases predominantly originated from wild-land fires. However, on 3–14 April 2002, Kukkonen et al. (2005) found out that local sources, including suspended dust, were mainly responsible for the highest concentrations of PM10. The predicted concentrations agree fairly well with the measurements. However, most of the highest measured concentrations (those in spring and summer) are under-predicted by the model at both stations. There are two reasons for these artefacts. The model does not allow for the seasonal variation of vehicular non-exhaust emissions, and therefore misses the concentration peaks in spring that are most likely caused by the suspension of dust. The statistical model that is used for evaluating the LRT’ed contribution cannot completely detect the influence of sources that produce PM2.5 with an exceptional chemical composition (such as those originated from wild-land fires, or sea salt). 3.2. Numerical results regarding the emissions and concentrations of PM2.5 3.2.1. The annual average emissions and their contributions to concentrations The contributions of various pollution source categories to the total emissions of PM2.5 in the Helsinki Metropolitan Area have been presented in Fig. 4a. Vehicular traffic and domestic combustion

are responsible for approximately 66% and 24% of the total emissions, respectively, and the rest is originated from other stationary sources (9%). Non-exhaust emissions form the largest proportion of vehicular traffic emissions (59%). The evaluated total amount of PM2.5 emissions is 1037 tonnes. All the largest stationary sources are coal-fired power plants, also using heavy fuel oil as an additive fuel. The stack heights of the three largest power plants are about 150 m. We have evaluated the relative contributions of various local emission source categories and LRT to the annual concentrations at the ground level for weekdays, based on dispersion modelling; these results have been presented in Fig. 4b and c for the urban PM2.5 stations. The influence of LRT is substantial at the roadside site, and dominating at the urban background site. The influence of smallscale combustion was not allowed for in the dispersion model computations. According to the computations, the emissions originated from cold starts and driving are responsible only for 3.5% and 1.3% of the annual average concentrations at the stations of Vallila and Kallio, respectively. However, in case of the days with an average sub-zero temperature, cold starts and driving increase the amount of exhaust emissions originated from local vehicular traffic by approximately 40%. Caused by the modelling of the emissions and atmospheric dispersion, the fractions of (i) vehicular exhaust, (ii) non-exhaust and (iii) cold start and driving emissions of the total vehicular emissions (Fig 4a) are the same as the corresponding fractions of the concentrations (Fig. 4b and c). 3.2.2. The annual average spatial concentration distributions The predicted annual averages of PM2.5 concentrations in the Helsinki Metropolitan Area, and in the centres of Helsinki (the peninsula in the middle of the map and its surrounding area) and Espoo (in the lower left-hand side) at the ground level are presented in Fig. 5a and b. The concentrations range from 6 mg m3 in the outskirts of the metropolitan area to 412 mg m3 in the most trafficked areas in Helsinki. The corresponding predicted contribution from LRT to the street level PM2.5 varies spatially from 0.4 at the most trafficked areas to nearly 1.0 in the outskirts. As shown in Fig. 5a, the concentrations of PM2.5 are the highest in the centre of Helsinki, and in the

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Predicted Observed

1

2

55 50 45 40 35 30 25 20 15 10 5 0

12

Predicted Observed

1

VALLILA Predicted Observed

3

4

5

Predicted Observed

3

6

PM2.5 (µg/m3)

PM2.5 (µg/m3)

VALLILA Predicted Observed

7

Predicted Observed

6

8

10

11

Autumn (September, October, November)

PM2.5 (µg/m3)

PM2.5 (µg/m3)

Predicted

9

7

8

Summer (June, July, August)

VALLILA

Observed

4 5 Spring (March, April, May) KALLIO

55 50 45 40 35 30 25 20 15 10 5 0

Summer (June, July, August) 55 50 45 40 35 30 25 20 15 10 5 0

12

KALLIO

55 50 45 40 35 30 25 20 15 10 5 0

Spring (March, April, May) 55 50 45 40 35 30 25 20 15 10 5 0

2

Winter (January, February, December)

PM2.5 (µg/m3)

PM2.5 (µg/m3)

Winter (January, February, December) 55 50 45 40 35 30 25 20 15 10 5 0

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KALLIO

VALLILA

55 50 45 40 35 30 25 20 15 10 5 0

PM2.5 (µg/m3)

PM2.5 (µg/m3)

M. Kauhaniemi et al. / Atmospheric Environment 42 (2008) 4517–4529

KALLIO

55 50 45 40 35 30 25 20 15 10 5 0

Predicted Observed

9

10

11

Autumn (September, October, November)

Fig. 3. Seasonal variation of the predicted and observed hourly averages of the PM2.5 concentrations at the stations of Vallila and Kallio.

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PM2.5 emissions in HMA

Area sources 0.9 % Vehicular exhaust 21.6 %

Domestic combustion 24.3 %

Power production 8.4 % Vehicular cold start & driving 5.8 %

VALLILA weekdays

Long-range transport 61.0 %

Vehicular non-exhaust 38.9 %

KALLIO weekdays

Vehicular exhaust 12.7 %

Vehicular non-exhaust 22.8 %

Long-range transport 85.5 %

Vehicular exhaust 4.7 %

Vehicular non-exhaust 8.5 % Vehicular cold start & driving 1.3 %

Vehicular cold start & driving 3.5 %

Fig. 4. (a) The relative magnitudes of various emission source categories on the total emissions of PM2.5 in the Helsinki Metropolitan Area in 2002 and (b, c) the predicted relative contributions of various emission source categories and LRT to the annual average PM2.5 concentrations at the stations of Vallila and Kallio during weekdays in 2002.

vicinity of the main roads and streets. The figure also shows the distinct influence of the ring road 1 (situated at the distance of about 8 km from the city centre), the major roads leading to the Helsinki city centre, the junctions of major roads and streets, and some other urban centres in addition to central Helsinki. These results can be qualitatively compared with those by Areskoug et al. (2000) in case of urban areas in Sweden. They found out that LRT constitutes the most important single contribution to both urban background PM2.5 and PM10, which was illustrated by the spatially uniform urban background concentrations of both of these PM fractions. It was also found out by dispersion model computations that the highest annual average PM2.5 concentrations caused by energy production sources and harbours in the area were 1.5 mg m3 in 2000–2001. The main characteristics of the spatial distribution of the concentrations are therefore caused by the fairly high traffic flows, especially to and from the centre of the capital. The traffic speeds

are also fairly low in central Helsinki, commonly of the order of magnitude of from 20 to 50 km h1, and traffic congestion situations occur each weekday. As expected based on the measurements, the roadside station of Vallila is situated in a more polluted environment in terms of PM2.5 (Fig. 5b), compared with the urban background station of Kallio. The station of Kallio seems to be well located to represent urban background, as there are no steep spatial concentration gradients in the vicinity of the station, and it represents neither the highest (of the order of 12 mg m3) nor the lowest (of the order of 6 mg m3) concentrations in the capital. 4. Conclusions Regarding the urban scale modelling, the evaluation of the vehicular exhaust emissions of PM2.5 is based on a fairly extensive set of laboratory experiments, including also those conducted in sub-zero temperatures (Laurikko, 1998). However, the information is currently scarce on the formation

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Fig. 5. Predicted spatial distribution of the yearly means of PM2.5 concentrations (mg m3) (a) in the Helsinki Metropolitan Area, and (b) in the centres of the cities of Helsinki and Espoo, both of these in 2002. Part (a) shows an outline of the centre of Helsinki and Espoo, presented in part (b). The size of the depicted area in part (a) is 35 km  25 km. The legends in the top left-hand corners show the absolute values of the pollutant concentrations. We have also indicated in figures the locations of the YTV monitoring stations utilised in this study, and the network of main roads and streets.

of vehicle non-exhaust emissions and suspension (e.g., Ketzel et al., 2007). Their contribution was estimated in this study simply to be directly proportional to the concentrations originating from

primary vehicular emissions (excluding cold starts and driving). However, the influence of suspension on the PM2.5 concentrations depends on the mechanical wear of the street surfaces, on street

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maintenance and cleaning, on traffic-induced turbulence and on the meteorological conditions. Omstedt et al. (2005) have developed a model for vehicle-induced non-tailpipe emissions of particles along Swedish roads, and Ketzel et al. (2007) have recently reviewed the methods to evaluate non-exhaust emission factors. However, the application of detailed non-exhaust emission models requires a substantial amount of input data that is specific for each street; the application for an entire urban area is currently not feasible. One of the most important challenges in order to improve the model is therefore to find a practical and realistic treatment of the non-exhaust vehicular emissions. In residential areas of northern European cities, biomass burning in stoves that are used for wintertime house heating may be an important source of fine particles during cold periods. This study did not include dispersion modelling of the small-scale combustion sources. However, domestic combustion is relatively less important in this area, compared with many other major Northern European cities. For example, in Oslo, the most important local sources of particular matter are domestic wood-burning and vehicular traffic, and the influence of wood-burning PM emissions is most dominant in the densely populated central city area (Laupsa and Slørdal, 2002). However, including small-scale combustion in the computations is expected to improve the accuracy of the computations. As more than half of the PM2.5 concentrations in the area considered originates from LRT during most of the year, the performance of the statistical model for LRT is an essential component for the total model performance. A detailed evaluation of the advantages and limitations, and statistical performance of the LRT model applied here has been presented by Kukkonen et al. (2008), in case of UK and Finland. The evaluation of LRT is especially important in case of long-term (such as daily and seasonal) temporal concentration variations. Clearly, the short-term (such as hourly) temporal variations are mostly determined by the contributions originated from local emission sources. In future work, the LRT modelling could most likely be improved by replacing the statistical model used by continental scale dispersion modelling. Such modelling should include in principle all major source categories of PM2.5, especially the contribution from wild-land fires.

The predicted daily and hourly averaged PM2.5 concentrations correlated fairly well with the corresponding data at two urban stations, as measured by the statistical model evaluation parameters. The modelling system does not contain any freely adjustable parameters, and the values of the semi-empirical coefficients used were derived from independent datasets. However, most of the highest measured concentrations (in spring and summer) were under-predicted by the model at both stations. Several of these episodes were mainly caused by LRT’ed particulate matter originated from wildland fires; and some of these were influenced by the suspension of particulate matter from street surfaces. Neither of the above-mentioned processes can explicitly be taken into account by the modelling system. The chemical composition of plumes from wild-land fires is commonly substantially different from an average LRT’ed background (e.g., it contains a high proportion of organic carbon). We also computed the spatial concentration distributions of PM2.5; the concentrations were the highest in the centre of Helsinki, and in the vicinity of the main roads and streets. The predicted contribution from LRT to the street level PM2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The simple model presented requires as input, in addition to the local traffic flow and stationary emissions, and meteorological data, only (i) the values of two coefficients that can be fairly easily determined experimentally (the coefficients of nonexhaust vehicular emissions and the regression coefficient for the LRT model), and (ii) the coefficients related to the influence of cold starts and driving. As shown in this study, the accuracy of the model regarding daily values (or longer term averages) is good. It is therefore envisaged that the model could potentially be used as a practical tool of assessment of long-term urban PM2.5 contributions in various European regions. Acknowledgements This study has been financed by the EC-funded FP5 project SAPPHIRE (Project no. EVK4-200200089) that is part of the Cluster of European Air Quality Research (CLEAR). It is also part of the COST action ES0602 ‘‘Towards a European Network on Chemical Weather Forecasting and Information Systems’’, the Nordic cooperative project SRIMPART and the national project PILTTI.

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