A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Abstract A simple line-source model is proposed to describe the downwind dispersion of pollutants near the roadway. This model avoids the cumbersome integration necessary for conventional Gaussian models. It also allows for plume rise due to the heated exhaust and is therefore potentially more accurate than conventional Gaussian models. The model parameters are estimated from data obtained in the General Motors Sulfate Dispersion Experiment. These parameters can be further refined as data from similar experiments become available. <s> BIB001 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> The information in this paper is directed to those persons involved in air quality along roadways. Data from an ongoing model validation program for dispersion of pollutants from roadways in Texas show that pollutant concentrations do not increase as rapidly with decreasing wind speed as predicted by most models which are based on a gaussian formulation. In fact, the actual ground level concentration may decrease with decreasing wind speed when the speed drops below some critical value. The Texas data should be sufficient to address many of the difficulties that are inherent to gaussian models at low wind speeds. <s> BIB002 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> The UK atmospheric dispersion modelling system is a computer code for modelling the dispersion of buoyant or neutrally buoyant gaseous and particulate emissions to the atmosphere. It comprises a number of individual modules, each dealing with either one aspect of the dispersion process or data input and output. Each module can be modified independently to keep it, and hence the whole model, scientifically up-to-date. Emissions can be of any duration. The effects of plume rise, wet and dry deposition, radio-active decay, hilly and variable roughness terrain, coastal regions and large buildings are allowed for. Fluctuations in concentration about the calculated mean are also modelled for time scales less than one hour. Output includes mean values, variances and percentiles of air concentration, dosage (time integrated concentration raised to some power), and deposition to the ground. For emissions of radio-active isotopes estimates can be made of the ground level gamma-radiation dose rate beneath the plume. A user interacts with the model via a system of menus based on Microsoft Windows so that it is easy to use. <s> BIB003 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> The air pollution dispersion model EPISODE has been developed at the Norwegian Institute for Air Research (NILU) over the past several years in order to meet the needs of modern air quality management work in urban areas. The model has recently been used as a basis for exposure calculations of NOx and NO2 in order to assess the effects of different traffic diversion measures on health and well being for the residents in the Vålerenga-Ekeberg-Gamlebyen area in Oslo. Here we describe some results from the most recent evaluations of the model for NOx and NO2 at station Nordahl Brunsgate in Oslo for the period 1 October 1996-19 November 1996. In addition examples of population exposure calculations for Oslo performed during the winter period of 1995-96, are also presented. <s> BIB004 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> In this paper, an evaluation of four Gaussian dispersion models—GM, CALINE3, PAL2, and ISCST2—commonly used for vehicular pollution prediction is presented. The evaluation has been done using field data monitored at one of the metropolitan cities, Chennai (Madras), India. Various statistical parameters that indicate the model's accuracy in prediction have been calculated. The relative accuracies of the models are compared. Such an analysis will be of use to regulatory agencies in selection of an appropriate model with confidence. As per the analysis conducted in this study, the model GM proved to be the best among the four followed in order by CALINE3, ISCST2, and PAL2. The prediction by all four models was comparable when the wind-road angle was perpendicular with wind speed being 2–5 m/s and the atmospheric stability condition being neutral. The prediction was the most accurate for the case of ground level receptor-perpendicular wind direction combination. The model GM provided the best simulation for a... <s> BIB005 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> The evaluation of the high percentiles of concentration distributions is required by most national air quality guidelines, as well as the EU directives. However, it is problematic to compute such high percentiles in stable, low wind speed or calm conditions. This study utilizes the results of a previous measurement campaign near a major road at ElimaK ki in southern Finland in 1995, a campaign speci"cally designed for model evaluation purposes. In this study, numerical simulations were performed with a Gaussian "nite line source dispersion model CAR-FMI and a Lagrangian dispersion model GRAL, and model predictions were compared with the "eld measurements. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted data sets was good for both models considered, as measured using various statistical parameters. For instance, considering all NO data (N"587), the so-called index of agreement values varied from 0.76 to 0.87 and from 0.81 to 1.00 for the CAR-FMI and GRAL models, respectively. The CAR-FMI model tends to slightly overestimate the NO concentrations (fractional bias FB"#14%), while the GRAL model has a tendency to underestimate NO concentrations (FB"!16%). The GRAL model provides special treatment to account for enhanced horizontal dispersion in low wind speed conditions; while such adjustments have not been included in the CAR-FMI model. This type of Lagrangian model therefore predicts lower concentrations, in conditions of low wind speeds and stable strati"cation, in comparison with a standard Lagrangian model. In low wind speed conditions the meandering of the #ow can be quite signi"cant, leading to enhanced horizontal dispersion. We also analyzed the di!erence between the model predictions and measured data in terms of the wind speed and direction. The performance of the CAR-FMI model deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. However, the performance of the GRAL model varied less with wind speed and direction; the model simulated better the cases of low wind speed and those with the wind nearly parallel to the road. 2001 Elsevier Science Ltd. All rights reserved. <s> BIB006 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air quality and support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition-resuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models. A range of monitoring techniques is used to measure pollutant concentrations in urban streets. Point measurement methods (continuous monitoring, passive and active pre-concentration sampling, grab sampling) are available for gaseous pollutants. A number of sampling techniques (mainly based on filtration and impaction) can be used to obtain mass concentration, size distribution and chemical composition of particles. A combination of different sampling/monitoring techniques is often adopted in experimental studies. Relatively simple mathematical models have usually been used in association with field measurements to obtain and interpret time series of pollutant concentrations at a limited number of receptor locations in street canyons. On the other hand, advanced numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy. <s> BIB007 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Abstract We discuss the application of the integrated air quality model AURORA to evaluate the concentration levels of NO 2 , SO 2 , PM 10 (1999/30/EC), CO and benzene (2000/69/EC) in 11 selected streets in the city of Antwerp. Together with the emission module, the analytical street box module of AURORA was used for the assessment of pollutant concentrations in street canyons. Comparisons with a measurement station located in one of the evaluated street canyons show agreements that are within the uncertainty range given by the guidelines in the EU directives, except for the NO 2 concentrations. An analysis of the measured and modelled concentrations and percentiles shows that concentrations of NO 2 , CO and benzene vary widely from street to street, whereas for SO 2 and PM 10 the model results show less variation between individual streets, indicating a more important contribution from the urban and regional background. <s> BIB008 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> This paper examines a popular method of estimating emissions from fugitive sources, which consists of computing horizontal fluxes using measured vertical profiles of velocities and concentrations at several locations downwind of a source. The horizontal flux is the height integral of the product of the velocity normal to the source and the concentration. If the losses between the source and the measurement location are small, the emission rate per unit length of the source is the horizontal flux. The calculation of horizontal flux is not straightforward because we need to make assumptions about the behavior of the concentration profile; the concentration measurements only suggest a plausible profile. We show that this uncertainty in the calculation can be reduced by using a dispersion model to interpret the data. We then provide a quantitative basis for the discussion by comparing horizontal flux calculations presented by Veranth et al. (Atmospheric Environment 37 (2003) 2295) with emissions inferred from a dispersion model applied to the same data set. <s> BIB009 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Abstract This study evaluates and inter-compares two atmospheric dispersion models against the results of a measurement campaign that was conducted near a major road at Elimaki in southern Finland from 15 September to 30 October 1995. The campaign was specifically designed for model evaluation purposes. The concentrations of NOx, NO2 and O3 were measured simultaneously at three locations, at three heights (3.5, 6 and 10 m) on both sides of the road. Traffic densities and relevant meteorological parameters were also measured on-site. The models evaluated were the Gaussian finite line source models California line source dispersion model, version 4 (CALINE4) and Contaminants in the Air from a Road—Finnish Meteorological Institute, version 3 (CAR-FMI); the latter was used in combination with the MPP-FMI meteorological pre-processing model. The agreement of measured and predicted datasets was good for both models, as measured using various statistical parameters. For instance, for the hourly NOx and NO2 data and predictions, analyzed separately at two distances from the road (17 and 34 m), each of these at two heights (3.5 and 6 m), the index of agreement values range from 0.77 to 0.88, and from 0.83 to 0.92 for the evaluations of the CAR-FMI and CALINE4 models, respectively. The performance of both models was better at a distance of 34 m, compared with that at a distance of 17 m. We also analyzed the difference between the model predictions and measured data in terms of the wind speed and direction. The performance of both models in most cases deteriorated as the wind speed decreased, and as the wind direction approached a direction parallel to the road. <s> BIB010 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Abstract We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July–August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an eight-lane highway located in Raleigh, NC. The air quality measurements consisted of long path optical measurements of NO at distances of 7 and 17 m from the edge of the highway. Sonic anemometers were used to measure wind speed and turbulent velocities at 6 and 20 m from the highway. Traffic flow rates were monitored using traffic surveillance cameras. The dispersion model [Venkatram, A., 2004. On estimating emissions through horizontal fluxes. Atmospheric Environment 38, 2439–2446] explained over 60% of the variance of the observed path averaged NO concentrations, and over 90% of the observed concentrations were within a factor of two of the model estimates. Sensitivity tests conducted with the model indicated that the traffic flow rate made the largest contribution to the variance of the observed NO concentrations. The meteorological variable that had the largest impact on the near road NO concentrations was the standard deviation of the vertical velocity fluctuations, σw. Wind speed had a relatively minor effect on concentrations. Furthermore, as long as the wind direction was within ±45° from the normal to the road, wind direction had little impact on near road concentrations. The measurements did not allow us to draw conclusions on the impact of traffic-induced turbulence on dispersion. The analysis of air quality and meteorological observations resulted in plausible estimates of on-road emission factors for NO. <s> BIB011 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Abstract Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods. Long-term exposure to nitrogen dioxide (NO2), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO2) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated. The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO2, NO, BS, and SO2 concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO2, NO, BS and SO2, respectively. We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations. <s> BIB012 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Background:Long-term exposure to outdoor air pollution has typically been estimated on the aggregate level, and more individual measures of exposure are needed. We investigated the associations with lung function of residential outdoor air pollution in early life, total lifetime, and days before lun <s> BIB013 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> An important component of air quality management and health risk assessment is improved understanding of spatial and temporal variability in pollutant concentrations. We compare, for Vancouver, Canada, three approaches for estimating within-urban spatiotemporal variability in ambient concentrations: spatial interpolation of monitoring data; an empirical/statistical model based on geographic analyses (“land-use regression”; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered—nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone—represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (standard deviation) are modest. LUR and CMAQ perform well in predicting concentrations at monitoring sites (average absolute bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resolution; LUR (monitors) offers the greatest (least) spatial resolution. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3–6 for NO, 4–6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concentrations, the findings may depend on which estimation method is employed. <s> BIB014 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. <s> BIB015 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Dispersion Models <s> Background: Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. Objectives: We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Methods: Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Results: Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO2 measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO2 and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO2 was attenuated (HR 1.37; 95% CI, 0.69-2.71). Conclusions: Traffic-related pollution exposure at school and homes may both contribute to the development of asthma. Editor's SummaryTraffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. McConnell et al. (p. 1021) evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Parent-reported physician diagnosis of new-onset asthma was identified during 3 years of follow-up of a cohort of kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. Traffic-related pollution exposure was assessed based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. The authors report an increase in asthma risk with modeled traffic-related pollution exposure from roadways near homes and schools. Ambient NO2 was also associated with increased risk. Models that included both NO2 and modeled traffic exposures suggested independent associations of asthma with traffic-related pollution at school and at home, whereas the estimate for NO2 was attenuated. The authors conclude that traffic-related pollution exposure at school and home may both contribute to the development of asthma <s> BIB016
Dispersion models simulate the physical and chemical processes of the dispersion and transformation of atmospheric pollutants, so as to predict the pollutant concentrations associated with emission sources, as well as their spatial and temporal variations BIB007 BIB005 . Dispersion models have been widely used in vehicular pollution prediction with making use of the environmental characteristics, such as traffic intensity, vehicle speed, terrain elevation, obstruction height, meteorological conditions, etc. The dispersion models vary depending on the mathematics used to develop the model. Gaussian-based dispersion models are the most commonly used models for pollutant dispersion analysis. In these models, the dispersion in downwind direction is a function of the mean wind speed blowing across the Gaussian plume under steady state conditions  . Chock proposed a simple line-source model, which is referred to as GM, to describe the downwind dispersion of pollutants near the roadway BIB001 . California Department of Transportation developed four generations of California Line Source Dispersion Model, of which CALINE3 and CALINE4 are commonly used. These models were used to predict the concentrations of CO, NO 2 and suspended PM near highways and arterial streets, given traffic emissions, site geometry and meteorology   . Mcconnell et al. applied CALINE4 to model traffic-related pollution exposure from roadways near home and near schools BIB016 . American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee proposed a near field steady state Gaussian plume model AERMOD to model particle dispersion  . Researchers later expanded its use to gas phase dispersion  . Some other Gaussian-based dispersion models have been proposed to model the local air pollution dispersions, such as the United Kingdom Atmospheric Dispersion Modeling System (UK-ADMS) BIB003 , Contaminants in the Air from a Road, by the Finnish Meteorological Institute (CAR-FMI)  , etc. Some of them specifically focus on dispersion modeling under low-speed wind conditions BIB002 BIB009 BIB011 . Levitin et al. evaluated the performance of CALINE4 and CAR-FMI models in modeling NO 2 and NO x dispersion near a major road BIB010 . Vardoulakis et al. reviewed four of Gaussian-based dispersion models in street canyons BIB007 . Researchers also developed other non-Gaussian dispersion models for air pollution assessment. The Flemish institute for technological research (VITO in Dutch) proposed an integrated air quality model to calculate the pollutant concentrations within a street canyon using a steady state box model BIB008 . The Norwegian Institute for Air Research developed a Lagrangian-Eulerian Models model EPISODE to estimate the exposures to NO 2 and NO x in Oslo, Norway BIB004 . Oftedal et al. applied EPISODE to assess the outdoor air pollution personal exposure for children BIB013 . Oettl et al. used the Gaussian-based model CAR-FMI and the Lagrangian dispersion model (GRAL, Graz Lagrangian Model) together to estimate the pollutant concentrations near a major road on low wind speed conditions BIB006 . The main advantage of dispersion models is that they do not require a dense network of monitoring stations. The disadvantages of dispersion models relate to the input data and assumptions about dispersion patterns: (1) the variety of input data is obtained at a relatively expensive cost; (2) these assumptions may be unrealistic or limited to local environments. Some researchers applied multiple methods to assess air pollution exposure and compared their performance. Gulliver et al. employed four different methods for long-term exposure assessment: nearest neighbor, kriging, dispersion model and LUR model  . They compared the performance of these four methods in the term of their ability to predict mean annual PM 10 concentrations in London, UK. Their results suggested the superiority of LUR models over other methods in long-term exposure assessment in complex urban environments. Marshall et al. also implemented all of these three general approaches to estimate within-urban spatio-temporal variability in ambient concentrations BIB014 . They concluded that different methods reflected different spatial scales: urban scale for interpolation approaches and dispersion models, and neighborhood scale for LUR models. Researchers have also combined different methods together to assess the air pollution at different levels. Brunekreef et al. assessed the long-term exposure to traffic-related air pollutants using a combination of exposure indicators, interpolation of measurements and LUR BIB015 . They first interpolated the pollutant measurements at regional scale using both ordinary kriging and inverse distance weighted interpolation. Results showed high correlation between estimates obtained with these two interpolation methods. At urban scale, regression models were developed to assess background concentrations of the pollutants. Beelen et al. estimated the long-term outdoor exposure as a function of a regional, an urban and a local component BIB012 . Different components were estimated using different techniques. They applied IDW interpolation to estimate the regional component, and LUR model on the urban component. The local component was assessed by using a GIS and a digital road network with linked traffic intensities. The cooperation of three different-level components takes into account small-scale variations in air pollution assessment.