Variations Of PM10 Concentrations Research Articles

A Comparison of Trace Gases and Particulate Matter over Beijing (China) and Delhi (India)

Air pollution represents a significant fraction of the total mortality estimated by the World Health Organization (WHO) global burden of disease project (GBD). The present paper discusses the characteristics of trace gases (O3, NO, NO2, and CO) and particulate matter (PM10 and PM2.5) in two Asian megacities, Delhi (India) and Beijing (China). A continuous measurement of trace gases and particulate matter are considered from 12 measuring sites in Beijing and 8 sites in Delhi. Over Beijing, the annual average of PM2.5, PM10, O3, NO2, and CO is, respectively, 85.3, 112.8, 58.7, and 53.4 μg/m3 and 1.4 mg/m3, and, respectively, over Delhi 146.5, 264.3, 24.7,and 19.8 μg/m3 and 1.73 mg/m3. From the spatial variations of pollutants, the concentrations of particulate matter and trace gases are observed to be much higher in the urban areas compared to the suburban areas. The higher average concentrations of PM10 and PM2.5 over Delhi and Beijing are observed during winter season compared with other seasons. The maximum diurnal variation of PM10 concentration is observed during winter season over Beijing and Delhi. The comparison of trace gases shows that the O3 concentrations during daytime are obviously higher compared with nighttime, and the highest diurnal variation of O3 is observed during summer. The concentrations of CO are highest during winter season, and higher concentrations are observed during nighttime compared to daytime. The O3 and CO show negative correlation over Beijing and Delhi. The negative correlation between O3 and NO2 is merely observed over Beijing, while CO and NO2 concentrations, in contrast, show positive correlation over Beijing.

Effect of Land Use and Cover Change on Air Quality in Urban Sprawl

Due to the frequent urban air pollution episodes worldwide recently, decision-makers and government agencies are struggling for sustainable strategies to optimize urban land use/cover change (LUCC) and improve the air quality. This study, thus, aims to identify the underlying relationships between PM10 concentration variations and LUCC based on the simulated PM10 surfaces in 2006 and 2013 in the Changsha-Zhuzhou-Xiangtan agglomeration (CZT), using a regression modeling approach. LUCC variables and associated landscape indexes are developed and correlated with PM10 concentration variations at grid level. Results reveal that the overall mean PM10 concentrations in the CZT declined from 106.74 μg/m3 to 94.37 μg/m3 between 2006 and 2013. Generally, variations of PM10 concentrations are positively correlated with the increasing built-up area, and negatively correlated with the increase in forests. In newly-developed built-up areas, PM10 concentrations declined with the increment of the landscape shape index and the Shannon diversity index and increased with the growing Aggregation index and Contagion index. In other areas, however, the reverse happens. These results suggest that LUCC caused by urban sprawl might be an important factor for the PM10 concentration variation in the CZT. The influence of the landscape pattern on PM10 concentration may vary in different stages of urban development.

해안 및 내륙도시 내 토지이용도별 미세먼지 분포 특성 및 상호 관련성에 관한 연구

Abstract In order to clarify the characteristics of PM10 in coastal and inland cities and their variation statistical analysis were carried out using environmental and meteorological data observed at Busan and Daegu metropolitans during 4 year from 2010. Averaged PM10 concentration was higher in industrial area than any other land-use sites, and its maximum value reach on over 50 ㎍/㎥ at Jangrim site in Busan. Temporal and spatial variations of PM10 concentration in Busan were more sharply, since topograph and mesoscale wind pattern in Bu san is more complicated than those in Daegu. Correlation of PM10 concentrations between sites within Daegu appeared strongly and maximum values R 2 is about 0.8. This indicate that because wind pattern induced by mesoscale forcing in Daegu are well unified, the variation of PM10 concentration tends to be similar in all sites within Daegu. However, due to complicate wind pattern induced by topography and coastal line, PM10 correlation of sites within Busan was weaker in comparison with in Daegu. And correlation of PM10 at same lane-use in Busan and Daegu tend to be related to the intensity of meteorological forcing, which can decide the intensity of wind pattern.

Categorisation of air quality monitoring stations by evaluation of PM10 variability

Air Quality Monitoring Networks (AQMNs) are composed by a number of stations, which are typically classified as urban, suburban or rural, and background, industrial or traffic, depending on the location and the influence of the immediate surroundings. These categories are not necessarily homogeneous and distinct from one another, regarding the levels of the monitored pollutants. A classification providing groups with these features is of interest for air quality management and research purposes, and therefore, other classification criteria should be explored. In this work, the variations of PM10 concentrations in 43 stations in the AQMN of the Basque Country in the period 2005–2012 have been studied to group them according to common characteristics. The characteristic variations in time are synthesised by the autocorrelation function (ACF), with both daily and hourly data, and by the average diurnal evolution pattern of the normalised concentrations on a seasonal basis (Evol-P). A methodology based on k-means clustering of these features is proposed. Each classification gives a different piece of information that has been phenomenologically related with specific dispersion and emission dynamics. The classification based on Evol-Ps is found to be the most influential one when comparing PM10 levels between groups. A combination of these categorisations provides 5 groups with significantly different levels of PM10, improving the discrimination of the conventional classification. Our results indicate that the time series of the pollutant concentrations contain enough information to provide an objective classification of the monitoring stations in an AQMN.

Spatial and temporal variations in particulate matter concentrations in twelve schools environment in urban and overpopulated camps landscape

Potential adverse effects on children health may result from school exposure to airborne particles. To address this issue, the variations of indoor and outdoor PM10 and PM2.5 concentrations in inner urban city and overpopulated camps school buildings located in the Mediterranean area were evaluated. Thirty six classrooms in twelve school buildings were investigated during fall, winter and spring seasons of 2011 and 2012. The results of the study show there were greater masses of PM10 and PM2.5 indoors that controlled by outdoor sources. For urban schools, the average concentration of indoor PM10 and PM2.5 were 320.0 μg/m3 and 86.2 μg/m3, respectively. Meanwhile, the average concentration of indoor PM10 and PM2.5 for overpopulated camps schools were 396.5 μg/m3 and 109.0 μg/m3, respectively. Almost all PM showed I/O ratios higher than one which denotes an important contribution from indoor source at urban and overpopulated camps schools. Different spatial distribution pattern among and within the schools was observed. For most of the schools a coefficients of divergence (COD) values varied from spatial homogeneity to moderate spatial heterogeneity. Across the all monitored schools, temporal variation of PM10 concentration was observed and significantly highly influenced by the seasonal variation. In both areas, the PM10 concentration displayed a bimodal pattern, with peaks between 7:00 and 11:00 a.m. during spring. Meanwhile a unimodal pattern displayed with peaks between 09:00 and 10:00 a.m. and between 12:30 and 01:30 p.m. for winter and fall, respectively. These peaks were attributed to enhanced anthropogenic activity and to the influence of meteorological conditions.

Downscaling of monthly PM10 indices at different sites in Bavaria (Germany) based on circulation type classifications

Atmospheric circulation affects local concentrations of particulate matter with an aerodynamic diameter of 10μm or less (PM10) in different ways: Via the determination of local meteorological conditions favoring or suppressing the formation and the accumulation of PM10, and through its control on short–and long–range transport of particles and precursors. The quantitative assessment of the connections between the large–scale atmospheric circulation and local PM10 is relevant not only for the understanding of observed variations in PM10 concentrations. It is even more important for estimating the potential effects of projected future changes in large–scale atmospheric circulation on PM10. In this contribution, daily atmospheric circulation types (CTs), resulting from variants of three different classification methods, and their monthly occurrence frequencies have been utilized in three different downscaling approaches for estimating monthly indices of PM10 for the period 1980–2010 at 16 locations in Bavaria (Germany). All variants of approaches have been evaluated via a leave–one–out cross validation procedure in order to attain reliable performance ratings to detect the most suitable downscaling approaches. Results indicate that the highest performance of downscaling approaches is achieved in winter when the best performing models explain on average roughly 50% of the observed PM10 variance. From this it can be concluded that classification–based approaches are generally suitable for the downscaling of PM10, particularly during winter when PM10 concentrations in Bavaria reach maximum values. As preferable settings of the downscaling approaches, the usage of rather small spatial domains and a relatively high number of classes for circulation type classification and furthermore the utilization of multiple linear regression analyses or random forest analyses for relating CTs to PM10 have been ascertained. These findings provide the basis for further enhancements of the classification–based downscaling of monthly PM10 that will be realized in successive investigations.

Study of particulate matters pollution related with meteorological factors for a city from South-Central of Romania

Reducing the effects of climate change and air pollution is at present a global priority. Development and implementation of effective policies in order to achieve these reductions is a challenge that requires a good understanding of the underlying phenomena of climate change and air pollution. This paper aims to highlight the seasonal variation of PM10 concentration in Pitesti city depending on major meteorological factors (temperature, intensity of solar radiation, and relative humidity). The applied methodology consists in statistical processing, using specialized software, of a database containing historical records of concentration values of this pollutant and of meteorological parameters recorded concurrently. The results of processing a series of approximately 30000 values recorded from 2008 to 2011 indicate the fact that in every season (winter,spring, summer, and autumn) the concentration of PM10 varies according to a sixth degree polynomial function, whose variable is one of the considered meteorological factors. The mathematical relationship that best approximates the variation of average PM10 concentration in relation with the three meteorological factors is by the form of a multiple linear regression equation. Keywords: air pollution and human health, particulate matters related with meteorological factors, statistical analysis

Comparing the Performance of Statistical Models for Predicting PM10 Concentrations

The ability to accurately model and predict the ambient concentration of Particulate Matter (PM) is essential for effective air quality management and policies development. Various statistical approaches exist for modelling air pollutant levels. In this paper, several approaches including linear, non-linear, and machine learning methods are evaluated for the prediction of urban PM10 concentrations in the City of Makkah, Saudi Arabia. The models employed are Multiple Linear Regression Model (MLRM), Quantile Regression Model (QRM), Generalised Additive Model (GAM), and Boosted Regression Trees1-way (BRT1) and 2-way (BRT2). Several meteorological parameters and chemical species measured during 2012 are used as covariates in the models. Various statistical metrics, including the Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), the fraction of prediction within a Factor of Two (FACT2), correlation coefficient (R), and Index of Agreement (IA) are calculated to compare the predictive performance of the models. Results show that both MLRM and QRM captured the mean PM10 levels. However, QRM topped the other models in capturing the variations in PM10 concentrations. Based on the values of error indices, QRM showed better performance in predicting hourly PM10 concentrations. Superiority over the other models is explained by the ability of QRM to model the contribution of covariates at different quantiles of the modelled variable (here PM10). In this way QRM provides a better approximation procedure compared to the other modelling approaches, which consider a single central tendency response to a set of independent variables. Numerous recent studies have used these modelling approaches, however this is the first study that compares their performance for predicting PM10 concentrations.

Analysis of Impact Factors on the Variation of PM10 Concentration in Seoul, Korea

To identify the primary factor affected the decreased PM 10 concentration in Seoul which is the capital city of Korea, wind speed and emissions in 2003 and 2004 were analyzed. The level of air pollution is intense in Seoul and continually increased since the late 1990s. However the concentration of PM 10 has been greatly declined recently. In particular, the concentration of PM 10 decreased 14% in 2003 and 2004 excluding the Asian dust periods. It is suggested that the major factors for the decrease are differences in wind speed between the two years and the period of constant breeze. In 2003, intense Asian dust events happened frequently and it increased the concentration of total PM 10 . The intense dust events were influence by the speed and duration of the wind. It is considered that the meteorological condition was the primary drive for the change of the concentration of PM 10 . The decreased emissions seem to be the additional factor for the change in the concentration of PM 10 .

Yearly emission factors of ammonia and particulate matter from three laying-hen housing systems

The aim of the present study was to measure the concentration of ammonia and particulate matter (PM) that passes through a size‐selective inlet with a 50% cut‐off at 10-μm aerodynamic-equivalent diameter (PM10) and emissions into atmosphere in the following three types of laying-hen houses: traditional battery cages with aerated open-manure storage (BSP) and two best available technique (BAT) housing types, namely, an aviary-system housing (ASH) and a vertical tiered cage with manure belts and forced-air drying (VTC). Measurements were taken continuously for a period of 1 year in each house. Ammonia concentration was measured continuously in each house using an infrared photoacoustic detector with a 15-min sampling interval. PM10 was measured continuously using a scatter light photometer, corrected by the traditional gravimetric-technique concentration to lower the measurement error. The same instrument was also used to collect PM10 through a traditional gravimetric technique. This procedure was performed to adjust the particulate matter-specific gravity of PM that is typical and specific for every animal house. PM10 and ammonia measurements were carried out together with measurements of inside and outside temperature, inside and outside relative humidity and ventilation rate. For the high PM10 concentrations measured in the ASH house during a preliminary survey, concentrations of total suspended particles (TSP) and fine PM (particles <2.5 microns) were also measured to evaluate the dustiness in the building during the working hours. The ammonia concentration was 5.37 mg/m3 in the traditional BSP house (the reference for cage-housing system), 4.95 mg/m3 in the VTC and 3.85 mg/m3 in the ASH. The ammonia-emission factors were 15.445 mg/h.hen place (0.135 kg/year.hen place) for BSP, 8.258 mg/h.hen place (0.072 kg/year.hen place) for VTC, and 23.704 mg/h.hen place (0.208 kg/year.hen place) for ASH. Ammonia emission-reduction efficiency of VTC v. the BSP was 53%, according to thresholds assessed by Integrated Prevention Pollution Control. The ammonia-reduction efficiency of ASH v. that of the standard Reference Housing system for non-cage housing was 68%. Average yearly PM10 concentration was remarkably higher in the ASH, with 0.215 mg/m3 v. 0.108 mg/m3 for the VTC and 0.094 mg/m3 for BSP. In the ASH, the concentration of total suspended particles (TSP) was 0.444 mg/m3 and that of PM2.5 was 0.032 mg/m3. In this facility, a great variation of PM10 concentration occurred in the morning hours. Recorded values for the PM10 emission were 0.433 mg/h.hen for BSP and 0.081 mg/h.hen for VTC, while the ASH showed the highest PM10 emission (1.230 mg/h.hen), with clear peaks occurring in the morning hours during daily farming operations.

Evaluation of Spatial Variability of PM10 Concentrations in London

This study aims to apply principal component analysis (PCA) to identify monitoring sites with similar variations of PM10 concentrations in the London Air Quality Monitoring Network. This statistical methodology was applied to hourly average concentrations measured at 27 monitoring sites during the period from January 2000 to December 2009. The monitoring sites were selected according their efficiency in the study period (greater than 75% for each year). It was observed that the hourly average PM10 concentrations were decreasing along the selected period at almost all monitoring sites. PCA was performed for each year, selecting the number of principal components (PCs) that had at least 95% of the original data variance. Analysing the frequency with which each pair of monitoring sites gave a significant contribution to the same PC, nine city areas with specific PM10 behaviour were identified. Thus, monitoring sites with redundant measurements during the studied period were identified, being possible to remove them to decrease the costs relative to their maintenance or replace them to increase the monitored area.

Study of the PM10 concentration variations along two intra-urban roads within a compact city

Eighty-five measurement campaigns were performed repeatedly to compare the concentration variation profiles along two intra-urban roads-one with open configuration and the other with street canyon effect. Fixed-effects panel data analysis was applied for formulating a model to express the PM(10) concentrations along intra-urban roads in terms of parameters like nearby central monitoring data, traffic counts and meteorological conditions with an objective to analyze the PM(10) concentration variation patterns along the two roads. Our findings reveal that traffic intensity and metrological conditions exert influence on concentration variation for both types of road configurations while wind velocity only affect the pollutants removal effectiveness of open road configuration. Further analysis unveils that the PM(10) concentration distribution profiles within a compact city environment are not always uniform and are dependent on the road configuration. Considerable PM(10) concentration differences were observed along the street canyon, and 70% of their variations are attributed to variations in their road aspect ratios. By contrast, no significant concentration difference is observed at open road configurations.

Influence of meteorological factors and emission sources on spatial and temporal variations of PM10 concentrations in Istanbul metropolitan area

This paper analyzes PM10 concentration data collected at 10 stations for the period of 2005–2009 by Air Quality Section of Istanbul Municipality. The analysis has been focused on spatial and temporal variations of the pollutants and their possible sources in the urban zones of the city. PM10 concentrations in Istanbul show significant variations across the city, with current PM10 levels at several traffic hot points and industrial zones which exceed EC air quality limit. The general temporal pattern is characterized by high concentrations in winter and low in summer. The number of exceedences allowed by the EU was surpassed at all monitoring sites during the analyzed years which reflect the serious pollution problem in the city.

Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer

The associations between the variations of PM10 concentration during summer monsoon dry seasons over the Klang Valley, Malaysia and the local meteorological factors, synoptic weather conditions as well as the regional hotspots number were examined based on simple multiple linear regression analysis. The regressive relationships established, suggest that the variation of PM10 in Klang Valley was governed significantly by all of the examined factors. Local meteorological conditions are among those factors which governed the largest day-to-day variations of PM10 concentration in the Klang Valley areas during the dry season. When augmented by synoptic meteorological variables and foreign emission sources, a remarkable increase in the explained variance was apparent. On the other hand, domestic burning sources only had a minimal impact on PM10 fluctuations. Important synoptic weather patterns which influence the air pollution variations were also identified. These synoptic conditions include the strengthening of the summer monsoon southwesterly winds over the equatorial area. In addition, the formation of cyclonic circulation, associated with typhoon formation over the north-west Pacific and the South China Sea as well as over the Bay of Bengal, are found to have had a profound impact on PM10 variations over the Malaysian region through the modulation of regional moisture distributions.

Model study of atmospheric particulates during dust storm period in March 2010 over East Asia

Abstract A Regional Air Quality Model System (RAQMS) is utilized to investigate the spatial and temporal distributions of PM10 concentrations and soil dust aerosol over East Asia in March 2010, when a severe dust storm occurred on 19–23 March. Dust aerosols were primarily generated in Gobi Deserts on 19 March and then swept across vast areas of East Asia. Model results are verified by in-situ observations from 17 Chinese cities, satellite retrievals, as well as lidar measurements in Japan. Validations demonstrate a good skill of RAQMS in representing the distribution and variation of PM10 concentrations and the major features of dust storm evolution. In general, the model performs better in the eastern parts of China than that in the western parts of China. Monthly mean surface PM10 concentrations were above 400 μg m−3 in west China, about 200–400 μg m−3 in north and central China, and below 100 μg m−3 in the remaining parts of China and East Asia. Dust aerosol was the major contributor to PM10 mass concentration during the study period, accounting for more than 80% in west China, 30%–60% in north and central China, and about 20%–40% in south China and the western Pacific regions. The dust storm in March 2010 was unique in its influential extent and transport pathway because it impacted as far as the Pearl River Delta of China, such as Hong Kong. Interannual variability of dust budget is analyzed, showing the dust emission, deposition and atmospheric loading in March 2010 are all larger than those in 2002 and 2006. Of the total dust emission (110.4 Mt), about 68% was redeposited onto the underlying surface by dry and wet deposition processes, and the remaining 32% was suspended in the atmosphere or subject to long-range transport.

Variations of PM10 concentrations in Wuhan, China

Concentrations of PM(10) (particulate matter as a key urban air pollutant) were obtained from nine monitor stations within the city of Wuhan for analysis after an intensive observational data collection period that commenced in January 2006 and concluded in December 2008. According to our data, PM(10) pollution intensified and reached a high alert level of air pollution during the month of November each year. It remained at a high alert level until the following April where it again decreased to a low alert level during the summer months. During the winter and spring months, the occurrence rate (in percent)of PM(10) was five to eight times higher (high alert level) than measurements detected during the summer months. The effects of intrinsic factors (pollution sources) and remote preconditions (dust storm propagation and formation of secondary aerosol) on severe PM(10) concentrations in Wuhan are first analyzed. After which, suggestions to reduce PM(10) pollutants are provided.

Model Analysis of PM10 Concentration Variations Over a Mineral Products Industrial Area in Saraburi, Thailand

Air pollution associated with particulate matters is a serious problem in the mineral products industrial area (MPIA) in Saraburi, central Thailand. PM10 concentrations monitored at Nah Phra Laan station located in the MPIA show that PM10 levels exhibit strong seasonal variations; the number of days in 2005 that PM10 concentrations exceeded the daily National Ambient Air Quality Standard of 120 μg/m3 were 58%, 29%, and 12% in the winter, rainy, and summer seasons, respectively. In this paper, the Hybrid Particle And Concentration Transport (HYPACT) model with meteorological fields from the Regional Atmospheric Modeling System (RAMS) was applied to Saraburi to investigate the impacts of meteorological parameters upon seasonal variations in PM10 concentration. Analysis of model results shows that daily average PM10 concentrations exceeding 200 μg/m3 are found in the downwind direction of emission sources and their horizontal gradients are strong. Hourly PM10 concentrations exhibit obvious diurnal variation with maximum values in wintertime at around 2000–2100 local standard time in association with low ventilation with light wind speed and weak vertical mixing, while in the rainy season, they are generally higher in the daytime than in the nighttime, as that mixing height in cloudy days is low in daytime whereas emission rates are high during working time.

Chemical characteristics and mutagenic activity of PM10 in Torino, a Northern Italian City

Epidemiological studies of particulate matter (PM) have associated PM mass, as well as certain individual components of PM such as secondary particulate with adverse human health effects. For example genotoxic effects attributed to PM may relate to the content of organic compounds but also to the oxidative DNA damage generated by transition metals like iron. However the exact physiochemical mechanism by which PM produces adverse effects is not clear. The aims of this study were to evaluate (1) concentrations of PM10, (2) amounts of bioavailable iron associated with PM10, (3) amounts of secondary particulate expressed as SO4(=) and NO3(-) and (4) the mutagenic activities of PM10 organic extracts. Sampling was carried out in a meteochemical station located in Torino, a northern Italian city with high levels of PM10. The mean PM10 concentration in the considered period was 46.1+/-28.8 microg/m3, the iron mean concentration was 0.83 microg/m3 (+/-0.65 microg/m3) and the bioavailable Fe was 5.7% (+/-4.4%). The data showed that secondary particulate matter (as sum of sulfates and nitrates) constituted about 47% of PM10 total mass. Both iron and secondary species concentrations were positively associated with PM10 levels. Seasonal variations of PM10 concentration, iron level and secondary species amount were significant. Samples were tested for mutagenicity with Salmonella typhimurium strains TA98 and TA100, with and without metabolic activation and a positive response was observed especially for TA98. There were positive statistical associations between mutagenicity and PM10, bioavailable iron, sulfates and nitrates concentrations. Therefore, these results showed the usefulness of this biological approach for monitoring PM10.

Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City

Suspended particulate matter is significantly related to the degradation of air quality in urban agglomerations, generating adverse health effects. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important in order to improve public awareness and air quality management. This study aims at developing models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average (DA) value of PM10 concentration and at comparatively assessing the above mentioned techniques. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece, whose population and industrialization is continuously increasing. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and DA value of wind speed), persistency in PM10 levels and weekly and annual variation of PM10 concentration. The validation of the models revealed that NN model showed slightly better skills in forecasting PM10 concentrations, as the regression and the NN model can forecast 55 and 61% of the variance of the data, respectively. In addition, several statistical indexes were calculated in order to verify the quality and reliability of the developed models. The results showed that their skill scores are satisfying, presenting minor differences. It was also found that both are capable of predicting the exceedances of the limit value of 50 μg/m3 at a satisfactory level.

A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads

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.