Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling

Aerosol samples for PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters less than 2.5 and 10 μm, respectively) were collected from 1993 to 1995 at five sites in Brisbane, a subtropical coastal city in Australia. This paper investigates the contributions of emission sources to PM_2.5 and PM₁₀ aerosol mass in Brisbane. Source apportionment results derived from the chemical mass balance (CMB), target transformation factor analysis (TTFA) and multiple linear regression (MLR) methods agree well with each other. The contributions from emission sources exhibit large variations in particle size with temporal and spatial differences. On average, the major contributors of PM₁₀ aerosol mass in Brisbane include: soil/road side dusts (25% by mass), motor vehicle exhausts (13%, not including the secondary products), sea salt (12%), Ca-rich and Ti-rich compounds (11%, from cement works and mineral processing industries), biomass burning (7%), and elemental carbon and secondary products contribute to around 15% of the aerosol mass on average. The major sources of PM_2.5 aerosols at the Griffith University (GU) site (a suburban site surrounded by forest area) are: elemental carbon (24% by mass), secondary organics (21%), biomass burning (15%) and secondary sulphate (14%). Most of the secondary products are related to motor vehicle exhausts, so, although motor vehicle exhausts contribute directly to only 6% of the PM_2.5 aerosol mass, their total contribution (including their secondary products) could be substantial. This pattern of source contribution is similar to the results for Rozelle (Sydney) among the major Australian studies, and is less in contributions from industrial and motor vehicular exhausts than the other cities. An attempt was made to estimate the contribution of rural dust and road side dust. The results show that road side dusts could contribute more than half of the crustal matter. More than 80% of the contribution of vehicle exhausts arises from diesel-fuelled trucks/buses. Biomass burning, large contributions of crustal matter, and/or local contributing sources under calm weather conditions, are often the cause of the high PM₁₀ episodes at the GU site in Brisbane.     

Low emission zones reduce PM10 mass concentrations and diesel soot in German cities

In many European cities mass concentrations of PM₁₀ (particles less than 10 μm in size) are still exceeding air quality standards as set by the European Commission in 1999. As a consequence, many cities introduced low emission zones (LEZs) to improve air quality and to meet the limit values. In Germany currently 48 LEZs are in operation. By means of dispersion modeling, PM₁₀ concentrations were estimated to decrease up to 10%. Analysis of PM₁₀ levels conducted for Cologne, Berlin, and Munich some time after the LEZs were introduced showed reduction of PM₁₀ mass concentration in the estimated range. The PM₁₀ particle fraction is, however, composed of particles with varying toxicity, of which diesel soot is highly health relevant. An evaluation of air quality data conducted in Berlin showed that in 2010 traffic-related soot concentrations measured along major roads decreased by 52% compared to 2007. Diesel particle emissions in Berlin were reduced in 2012 by 63% compared to a business-as-usual scenario (reference year 2007). A strong reduction of the traffic-related particle fraction of PM_2.5 was also reported for Munich. Therefore, it is likely that the effects of LEZs are considerably more significant to human health than was anticipated when only considering the reduction of PM₁₀ mass concentrations.

Implications: The implementation of low emission zones in German cities might result in a reduction of PM₁₀ levels concentrations by up to 10%. However, it is difficult to show a reduction of PM₁₀ annual averages in this order of magnitude as meteorology has a large impact on the year-to-year variation of PM mass concentrations. Monitoring of other PM metrics such as black smoke (BS) or elemental carbon (EC) might be a better strategy for evaluating LEZs effects. The benefit of low emission zones on human health is far greater than is presently visible from routine measurements of PM₁₀.     

A consistent method for modelling PM10 and PM2.5 concentrations across the United Kingdom in 2004 for air quality assessment

A modelling method has been developed to map PM₁₀ and PM_2.5 concentrations across the UK at background and roadside locations. Separate models have been calibrated using gravimetric measurements and Tapered Element Oscillating Microbalance instruments (TEOM) using source apportionments appropriate to the size fractions and sampling methods. Maps have been prepared for a base year of 2004 and predictions have been calculated for 2010 and 2020 on the basis of current policies. Comparisons of the modelling results with air quality regulations suggest that exceedences of the EU Daughter Directive stage 1 24-h limit value for PM₁₀ at the roadside in 2004 will be largely eliminated by 2020. The concentration cap of 25 μg m⁻³ for PM_2.5 proposed within the CAFÉ Directive is expected to be met at all locations. Projections for 2010 and 2020 suggest that the proposed exposure reduction (ER) target is likely to be considerably more stringent and require additional measures beyond current policies. Thus the model results suggest that the balance between the stringency of the concentration cap and the ER target in the proposed directive is appropriate. Measures to achieve greater reductions should therefore have the maximum public health benefit and air quality policy is not driven by the need to reduce concentrations at isolated ‘hotspots’.     

Quantifying the effect of urban tree planting on concentrations and depositions of PM10 in two UK conurbations

Trees are efficient scavengers of particulate matter and are characterised by higher rates of dry deposition than other land types. To estimate the potential of urban tree planting for the mitigation of urban PM₁₀ concentrations, an atmospheric transport model was used to simulate the transport and deposition of PM₁₀ across two UK conurbations (the West Midlands and Glasgow). Tree planting was simulated by modifying the land cover database, using GIS techniques and field surveys to estimate reasonable planting potentials. The model predicts that increasing total tree cover in West Midlands from 3.7% to 16.5% reduces average primary PM₁₀ concentrations by 10% from 2.3 to 2.1 μg m⁻³ removing 110 ton per year of primary PM₁₀ from the atmosphere. Increasing tree cover of the West Midlands to a theoretical maximum of 54% by planting all available green space would reduce the average PM₁₀ concentration by 26%, removing 200 ton of primary PM₁₀ per year. Similarly, for Glasgow, increasing tree cover from 3.6% to 8% reduces primary PM₁₀ concentrations by 2%, removing 4 ton of primary PM₁₀ per year. Increasing tree cover to 21% would reduce primary PM₁₀ air concentrations by 7%, removing 13 ton of primary PM₁₀ per year.     

Contribution of railway traffic to local PM10 concentrations in Switzerland

Field measurement campaigns of PM10 and its elemental composition (daily sampling on filters) covering different seasons were performed at two sites near the busiest railway station of Switzerland in Zurich (at a distance of 10 m from the tracks) and at a site near a very busy railway line with more than 700 trains per day. At this latter site parallel samples were taken at 10, 36 and 120 m distances from the tracks with the aim to study the distance dependence of the railway induced PM10 concentrations.To distinguish the relatively small railway emissions from the regional background (typically 20–25 μg m⁻³), simultaneous samples were also taken at an urban background site in Zurich. The differences in PM10 and elemental concentrations between the railway exposed sites and the background site were allocated to the railway contribution.Small, however, measurable PM10 concentration differences were found at all sites. The elemental composition of these differences revealed iron as the only quantitatively important constituent. As a long-term average it amounted to approximately 1 μg m⁻³ Fe at a distance of 10 m from the tracks at all three sites. Assuming that iron was at least partly oxidised (e.g. in the form of Fe₂O₃) the contribution can amount up to 1.5 μg m⁻³. Emissions of copper, manganese and chromium from trains were also clearly identified. However, compared to iron these, elements were emitted in very low quantities.No significant contribution from rock material (calcium, aluminium, magnesium, sodium) was observed as might have been expected from erosion, abrasion and resuspension from the gravel below the tracks. Particle emissions from diesel exhaust were not considered as trains in Switzerland are operated nearly exclusively by electric locomotives.The railway, induced contribution to ambient PM10 decreased rapidly with increasing distance from the tracks. At a distance of 120 m this contribution dropped to only 25% of the contribution observed at 10 m distance.     

Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001

Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da < 2.5 microm (PM2.5) and coarse PM with 2.5 < da < 10 microm (PM2.5-10) were collected using dichotomous samplers. The inhalable particle fraction, PM10, was determined as the sum of fine and coarse concentrations. Wind speed, temperature and relative humidity (RH) were also measured continuously. Average concentrations of PM2.5 for the 1989-2001 period ranged from 38.5 microg/m3 to 53 microg/m3. For PM2.5-10 levels ranged from 35.8-48.2 microg/m3 and for PM10 results were 74.4-101.2 microg/m3 across the four sites. Both annual and daily PM2.5 and PM10 concentration levels exceeded the U.S. National Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.     

Receptor modelling of PM10 concentrations at a United Kingdom national network monitoring site in central London

A receptor model for predicting future PM₁₀ concentrations has been developed within the framework of the UK Airborne Particles Expert Group and applied during the recently completed review of the UK National Air Quality Strategy. The model uses a combination of measured PM₁₀, oxides of nitrogen and particulate sulphate concentrations to provide daily estimates of the contributions to total particle concentrations from primary combustion, secondary and other (generally coarse) particle sources. Projections of past and future concentrations of PM₁₀ are estimated by applying appropriate reductions to the current concentrations of the three components based on an understanding of the likely impact of current policies on future levels. Projections have been derived from 1996, 1997 and 1998 monitoring data and compared with UK national air quality objectives and European Union limit values. One of the key uncertainties within the receptor modelling method is the assignment of the residual PM₁₀, remaining after the assignment of primary combustion and secondary particle contributions, to the ‘other’ particle fraction. An examination of the difference between measured PM₁₀ and PM_2.5 concentrations confirms our assignment of the bulk of this residual to coarse particles. Projections based on 1996 monitoring data are the highest and those based on 1998 monitoring data are the lowest. Whilst there is considerable difference between these projections they are consistent with measured concentrations for previous years. All three projections suggest that with current agreed policies the EU annual mean limit value will be achieved. The 24-h mean limit value is projected to be achievable when projections are derived from 1997 and 1998 data, but not from 1996 data. All three projections suggest that with current agreed policies the central London site will not achieve the provisional 1997 UK National Air Quality Strategy objective.     

Factors affecting the concentrations of PM10 in central Taiwan

In this study, the synoptic weather types that have high probability and low probability of producing PM10 episode are referred to as HPE and LPE, respectively. Multiple linear regressions analysis showed that NO2 was the most important contributor (35.61%) to the concentrations of PM10 for HPE weather. For LPE weather, the season factor had the greatest contribution (48.11%) to the concentrations of PM10. Using the correlation coefficients between the concentrations of PM10 and SO2 or NO2 on HPE and LPE to calculate the increase of PM10 from LPE to HPE, we found that the increase of PM10 owing to the increase of SO2 and NO2 from LPE to HPE was 12.93microg/m3 which was about 51% of the total amount of PM10 increased from LPE to HPE. Results of factor analysis showed that the first component could be attributed to the result of local pollution especially for the weather patterns of types P3 and P6, while the secondary component for the weather patterns of types P1 and P4 can be attributed to the long-range transport of SO2 pollutants from China.     

Using NOx and CO monitoring data to indicate fine aerosol number concentrations and emission factors in three UK conurbations

It is increasingly accepted that although exposure to elevated concentrations of PM₁₀ is associated with an increased risk of mortality and morbidity, the relationship may not be causal. Rather, there is evidence that number concentrations may be a more appropriate metric than mass concentrations in evaluating health risk. Number concentrations are not routinely monitored and spatial and temporal patterns are poorly quantified. CO and NO_x are co-pollutants with their major urban source in common with fine particles, i.e. road vehicle emissions; are routinely monitored in many cities and are also related to ill health. Datasets of particle number concentration measurements from approximately month-long field campaigns in Manchester, Edinburgh and Birmingham (UK) are compared with simultaneous concentrations of CO and NO_x from nearby fixed monitors. It was found that it might be possible to reliably predict particle number concentrations (diameters>100 nm) on an hourly basis in Manchester city centre from knowledge of NO_x or CO concentrations alone. The influences of meteorology, spatial variability in emissions and lack of co-location upon the correlations are investigated using cluster analysis. The cluster analysis revealed that these relationships may vary between cities and are dependent upon monitor location but in ways that can be ascribed. For two out of three sites there existed a linear relationship between average cluster aerosol and gas concentrations. This indicates that although airmass aging disrupts the short-term linear relationship, the relationship in the average survives. An emission ratio of particles (approx. 100–500 nm diameter) to NO_x of approximately 50 cm⁻³ ppb⁻¹ was estimated in Manchester and Birmingham. Particle mass spectrometry measurements indicated that organic compounds dominated these particles and an emission rate of 0.58 ton km⁻² a⁻¹ of organic particulate matter from road transport has been estimated for the Greater Manchester conurbation.     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.     

Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models.For this reason, a modified semi-empirical box model for predicting the PM₁₀ concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.     

Estimating the emission source reduction of PM10 in central Taiwan

Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.     

Patterns and concentrations of PM10 in a mountainous basin region

In this study, continuous data of PM10 (particles with aerodynamic diameter < 10 microns) concentration measurements for a 4-yr period were analyzed. These measurements have been carried out in the Eordea Basin, an industrial area in the northwestern mountainous region of Greece. The annual, monthly, and diurnal patterns are presented and investigated regarding the prevailing meteorological conditions and atmospheric processes that affect the ambient concentrations of PM10. The effect of wind on controlling PM10 concentration is also discussed. Based on the data analysis, an attempt is made to provide useful information about air quality levels, taking into account U.S. Environmental Protection Agency air quality standards.     

Spatial and seasonal variation of PM10 mass concentrations in Taiwan

Hourly data of PM10 concentration collected from an air quality-monitoring network has been analyzed over Taiwan from 1994 to 1999. Fourteen sites from 72 monitoring stations were selected to evaluate the spatial and seasonal variations in the regions of north, southwest, south, east and National Park. The selected monitoring sites are located in a suburban environment, except Nantz and Linyuan that are located in industrial areas. Moreover, Yangming and Hengchuen are located in National Park. Spatial and seasonal variations of PM10 concentrations are rather large over Taiwan. Annual average in south is approximately six times higher than in National parks. In northern sites, the highest concentration occurs in March–May, which is attributed to the occurrence of dust storms in arid regions of central Asia and the transport of dust by northeasterly monsoon. A marked seasonal variation of PM10 concentrations can be observed both in southwestern and southern regions. The pattern is characterized by high concentrations in winter and low in summer. Appearance of the highest monthly PM10 concentration in winter of south may be in part due to the lowest number of monthly precipitation days and low temperature, both of which occurred in winter. The frequency of PM10 daily mean concentration for exceeded 150 μg m⁻³ is 15% during winter in south, which reflects the serious pollution problem there. Monitoring sites in National Park are representatives of remote environments, but the PM10 concentrations are still affected by the dust storms and human activities.     

Analysis of the PM2.5 emission inventory and source apportionment in Jilin City,Northeast of China

Environmental Science and Pollution Research - This study collected and compiled statistical data on atmospheric pollution in Jilin City, China during 2013–2014, using models and methods to...     

Improvements to the UK PCDD/F and PCB atmospheric emission inventory following an emissions measurement programme

PCDD/F data are presented from 75 samples of primary emissions sampled between 1995-97 as part of the compliance monitoring survey undertaken by the UK Environment Agency. Municipal solid waste (MSW), chemical waste and clinical waste incinerators, cement kilns, sinter plants and sewage sludge incinerators were the source categories monitored and reported here. Based on this monitoring programme, the previous national UK emission estimates by Eduljee and Dyke (1) of 560-1100 g I-TEQ a(-1) for 1993 have been revised downwards to 220-660 g I-TEQ a(-1). Despite source reduction measures, MSW incinerators remain a significant source of PCDD/Fs to the atmosphere, contributing between 30-50% of the EPCDD/F I-TEQ emission, rather than the approximately 80% they were estimated to contribute in 1993. 2,3,7,8-substituted PCDD/F congener profile data are presented for some of the source categories and generally support the view that differences in the mixtures ('fingerprints') of PCDD/Fs emitted from different sources are observed. New data on the dioxin-like PCB emissions are presented for cement kilns and sinter plants. These show that TEQ-rated PCBs can make an important contribution to the I-TEQ emitted from certain combustion sources. High concentrations of a full range of PCB congeners/homologues have been measured in the atmosphere close to sintering strands, although the precise source of PCBs from this process remains unclear.     

PM10 and PM2.5 concentrations in Central and Eastern Europe:: results from the Cesar study

Between November 1995 and October 1996, particulate matter concentrations (PM₁₀ and PM_2.5) were measured in 25 study areas in six Central and Eastern European countries: Bulgaria, Czech Republic, Hungary, Poland, Romania and Slovak Republic. To assess annual mean concentration levels, 24-h averaged concentrations were measured every sixth day on a fixed urban background site using Harvard impactors with a 2.5 and 10 μm cut-point. The concentration of the coarse fraction of PM₁₀ (PM_10−2.5) was calculated as the difference between the PM₁₀ and the PM_2.5 concentration. Spatial variation within study areas was assessed by additional sampling on one or two urban background sites within each study area for two periods of 1 month. QA/QC procedures were implemented to ensure comparability of results between study areas. A two to threefold concentration range was found between study areas, ranging from an annual mean of 41 to 98 μg m⁻³ for PM₁₀, from 29 to 68 μg m⁻³ for PM_2.5 and from 12 to 40 μg m⁻³ for PM_10−2.5. The lowest concentrations were found in the Slovak Republic, the highest concentrations in Bulgaria and Poland. The variation in PM₁₀ and PM_2.5 concentrations between study areas was about 4 times greater than the spatial variation within study areas suggesting that measurements at a single sampling site sufficiently characterise the exposure of the population in the study areas. PM₁₀ concentrations increased considerably during the heating season, ranging from an average increase of 18 μg m⁻³ in the Slovak Republic to 45 μg m⁻³ in Poland. The increase of PM₁₀ was mainly driven by increases in PM_2.5; PM_10−2.5 concentrations changed only marginally or even decreased. Overall, the results indicate high levels of particulate air pollution in Central and Eastern Europe with large changes between seasons, likely caused by local heating.     

Estimating the contribution of industrial facilities to annual PM10 concentrations at industrially influenced sites

If measures to reduce the industrial discharge of PM10 shall be planned with high accuracy, a first step must be to estimate the contribution of single industrial facilities to the overall PM10 burden as accurately as possible. In northern Duisburg as an example, an area where iron and steel producing industry is concentrated, PM10 was measured at 4 sampling sites very close to an industrial complex of blast furnaces, a sinter plant, oxygen steel works and a coke oven plant for 9 months in 2006. At two sites metals in PM10 were determined. The results, together with analytical data of urban background sites in the region and data of wind direction and wind speed were used for an estimation of the contribution of single plants to the PM10 burden. A careful analysis of the data showed, that the data of PM10, calcium, iron and zinc measured at two sites close to the industrial area and information about the urban background aerosol were sufficient to calculate the PM10 contribution of the main single plants. The data could be compared with those of modelling.     

An empirical approach for the prediction of daily mean PM10 concentrations

An empirical model has been devised to predict concentrations of PM₁₀ at background and roadside locations in London. Factors to calculate primary PM₁₀ and PM_2.5 concentrations are derived from annual mean NO_X, PM_2.5 and PM₁₀ measurements across London and south east England. These factors are used to calculate daily means for the primary and non-primary PM₁₀ fractions for the London area. The model accurately predicts daily mean PM₁₀ and EU Directive Limit values across a range of sites from kerbside to rural. Predictions of future PM₁₀ can be made using the expected reductions in secondary PM₁₀ and site specific annual mean NO_X predicted from emission inventories and dispersion modelling. The model suggests that the EU Directive Limit values will be exceeded close to many of London's busiest roads, and perhaps at central background sites should there be a repeat of 1996 meteorological conditions during 2005. A repeat of 1997 meteorology conditions during 2005 would lead to the EU Limit Value being exceeded alongside the busiest central London roads only. The model is applicable for London and south east England but the methodology could be applied elsewhere at a city or regional level. The model relies on the currently observed ratio between NO_X and PM₁₀. This ratio has remained constant over the last 4 years but might change in the future. The NO_X:PM₁₀ ratio derived from measurements and used in this model, implies that emission inventories might over estimate primary PM₁₀ by more than 50%.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.