A numerical experiment of roadside diffusion under traffic-produced flow and turbulence

Roadside air pollution due to heavy traffic is one of the unsettled issues in the atmospheric environment in urban areas. As a practical application of a Computational Fluid Dynamics (CFD) model, a coupled mesoscale-CFD model was applied to the Ikegamicho area of Kawasaki City, Japan. For this study, the effects of traffic-produced flow and turbulence (TPFT) on the dispersion of the pollutants near the heavy traffic road were mainly investigated in an actual urban area. First, a series of preliminary CFD calculations was conducted for a road tunnel field experiment to obtain a fitting parameter for the traffic-produced flow. The calculation was then performed for 24 h in December 2005 around Ikegamicho, and the results were compared with the data at a roadside monitoring post in the area, located 10 m from the boundary of the ground road. In general, the effect of traffic-produced flow and turbulence was limited at the downstream side of the roads. The maximum concentration of NO_x was reduced and smoothed out along the traffic flow by the traffic-produced flow and turbulence on the road. The effects of traffic-produced turbulence on the dispersion of pollutants were greater than those of traffic-produced flow; however, the effects of traffic-produced flow were not negligible. The concentration of pollutants was not particularly dependent on the turbulent Schmidt number because most of the emission sources were introduced as volume sources in the present calculations, and the effect caused by differences in the material diffusion coefficient was not particularly significant at the outside of the road.     

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .     

石家庄市道路交通环境NO_x污染水平及ARMA模型预测

选择石家庄市区代表性路段作为研究对象,对其交通环境空气中NOx的污染水平进行现状监测。基于Matlab软件建立拟合模型,对下一时期的NOx污染趋势进行预测。结果表明,石家庄市交通环境中NOx小时浓度介于0.047~0.237 mg/m3之间,呈早晚高,且下午明显低于上午的日变化规律;NOx日均浓度介于0.076~0.211 mg/m3之间,其浓度与车流量呈明显的正相关性。利用matlab软件建立的ARMA模型能够较好地预测道路交通环境空气中NOx的浓度变化趋势。     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Assessing Spatial Variability of Ambient Nitrogen Dioxide in Montréal,Canada, with a Land-Use Regression Model

Abstract

The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO₂) in Montréal, Quebec, Canada. These estimates of concentrations of NO₂ will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO₂ was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO₂ and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO₂ was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed no association with NO₂. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO₂; the best-fitting regression model had a R² of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.     

Measurement and Three-Dimensional Modeling of Airflow and Pollutant Dispersion in an Undersea Traffic Tunnel

Abstract

Airflow and pollutant dispersion in a cross-harbor traffic tunnel were experimentally and numerically studied. Concentrations of the gaseous pollutants CO, NO_x, and total hydrocarbons (THC) at three axial locations in the tunnel, together with traffic flow rate, traffic speed, and types of vehicle were measured. Three-dimensional (3D) turbulent flow and dispersion of air pollutants in the tunnel were modeled and solved numerically using the finite volume method. Traffic emissions were modeled accordingly as banded line sources along the tunnel floor. The results reveal that cross-sectional concentrations are nonuniformly distributed and that concentrations rise with downstream distance. The piston effect of vehicles alone can provide 9–23% dilution of air pollutants in the tunnel, compounded to a 23–74% dilution effect according to the ventilation condition.     

Quantifying local traffic contributions to NO2 and NH3 concentrations in natural habitats

NO₂ and NH₃ concentrations were measured across a Special Area for Conservation in southern England, at varying distances from the local road network. Exceedances of the critical levels for these pollutants were recorded at nearly all roadside locations, extending up to 20 m away from roads at some sites. Further, paired measurements of NH₃ and NO₂ concentrations revealed differences between ground and tree canopy levels. At “background” sites, away from the direct influence of roads, concentrations were higher within tree canopies than at ground level; the reverse pattern was, however, seen at roadside locations. Calculations of pollutant deposition rates showed that nitrogen inputs are dominated by NH₃ at roadside sites. This study demonstrates that local traffic emissions contribute substantially to the exceedance of critical levels and critical loads, and suggests that on-site monitoring is needed for sites of nature conservation value which are in close proximity to local transport routes.     

孤立与非孤立城市街道峡谷内污染物扩散

通过求解二维不可压N-S方程、k-ε方程及污染物对流扩散方程,模拟了孤立街道峡谷与非孤立街道峡谷内的流场及交通污染物浓度场.计算结果与风洞试验结果总体趋势一致.非孤立街道峡谷内污染物壁面浓度要大于孤立街道峡谷内的壁面浓度.通过计算街道峡谷建筑屋顶高度处的垂直方向污染物通量,说明了湍流扩散是污染物扩散出街道峡谷的主要原因,其污染物通量总为正,而平均流通量可以为负.非孤立街道峡谷由于平均流流动和湍流流动的总扩散通量减少,造成污染物在街道峡谷内集聚,从而理论上解释了非孤立街道峡谷与孤立街道峡谷污染扩散的差别.     

Weighted road density: A simple way of assigning traffic-related air pollution exposure

BackgroundExisting traffic variables used for predicting NO₂ in epidemiological studies are either difficult to acquire or explain only a small proportion of the variance. The purpose of this study was to develop and validate a new predictor, weighted road density, which combines the maximum amount of information related to traffic into a single variable without the requirement of obtaining traffic counts for a given area.MethodTwo week NO₂ samples were collected using the readings of up to 32 passive samplers on 3 separate rounds between September and December 2006 and again in 2007. Several types of traffic related explanatory variables based on traffic counts, distance to main road and the proposed weighted road density were constructed using GIS software, and tested for association with the NO₂ samplers. Assessment of the best model was based on R² values, as well as leave-one-out cross validation.ResultsThe weighted road density variable and the density variable based on traffic counts resulted in a similar R² (0.59) for predicting NO₂, although weighted road density was much easier to construct and outperformed other variables such as distance to main road.ConclusionAs well as being a powerful predictor for use in a land use regression model, weighted road density can be used as a proxy for exposure to traffic-related pollution, for use in circumstances where direct measurements of pollutant levels are not feasible or are not required.     

Differences in airborne particle and gaseous concentrations in urban air between weekdays and weekends

Airborne particle number concentrations and size distributions as well as CO and NO_x concentrations monitored at a site within the central business district of Brisbane, Australia were correlated with the traffic flow rate on a nearby freeway with the aim of investigating differences between weekday and weekend pollutant characteristics. Observations over a 5-year monitoring period showed that the mean number particle concentration on weekdays was (8.8±0.1)×10³ cm⁻³ and on weekends (5.9±0.2)×10³ cm⁻³—a difference of 47%. The corresponding mean particle number median diameters during weekdays and weekends were 44.2±0.3 and 50.2±0.2 nm, respectively. The differences in mean particle number concentration and size between weekdays and weekends were found to be statistically significant at confidence levels of over 99%. During a 1-year period of observation, the mean traffic flow rate on the freeway was 14.2×10⁴ and 9.6×10⁴ vehicles per weekday and weekend day, respectively—a difference of 48%. The mean diurnal variations of the particle number and the gaseous concentrations closely followed the traffic flow rate on both weekdays and weekends (correlation coefficient of 0.86 for particles). The overall conclusion, as to the effect of traffic on concentration levels of pollutant concentration in the vicinity of a major road (about 100 m) carrying traffic of the order of 10⁵ vehicles per day, is that about a 50% increase in traffic flow rate results in similar increases of CO and NO_x concentrations and a higher increase of about 70% in particle number concentration.     

Removal of lead and zinc ions from aqueous solution using Amasya zeolites from Turkey

A 3-D Eulerian-Lagrangian approach to moving vehicles is presented that takes into account the traffic-induced flow rate and turbulence. The method is applied to pollutant dispersion in an individual street canyon and a system of two street canyons forming a perpendicular intersection. The approach is based on computational fluid dynamics (CFD) calculations using a Eulerian approach for continuous phase and a Lagrangian approach for moving vehicles. The wind speed was assigned values of 4, 7 and 12 m/s. One-way and two-way traffic with different traffic rates per lane is considered. In the case of the intersection, a longitudinal wind direction was assumed. Predictions show differences in the pollutant dispersion in the case of one-way and two-way traffic.     

PM2.5 concentrations in London for 2008–A modeling analysis of contributions from road traffic

We report on the analysis of contributions from road traffic emissions to fine particulate matter (PM_2.5) concentrations within London for 2008 with the OSCAR Air Quality Assessment System. A spatiotemporal evaluation of the OSCAR system has been conducted with measurements from the London air quality network (LAQN). For the predicted and measured hourly time series of concentrations at 18 sites in London, the medians of correlation, mean absolute error, index of agreement, and factor of two (FAC2) of all stations were 0.80, 4.1 μg/m³, 0.86, and 74%, respectively. Spatial evaluation of modeled and observed annual mean concentrations also showed a fairly good agreement, with all the values falling within the FAC2 range. According to model predictions, the urban increment (including the contributions from urban traffic and other urban sources) was evaluated to be on the average 18%, 33%, 39%, and 43% of the total PM_2.5 in suburban environments, in the urban background, near roads, and near busy roads, respectively. However, the highest values of the urban traffic increment can be around 50% of the total PM_2.5 concentrations near motorways and major roads. The total concentrations (including regional background, and the contributions from urban traffic and other urban sources) can therefore be almost three times the regional background. The total urban increment close to busy roads was around 7–8 μg/m³, in which the estimated traffic contribution is more than 2 μg/m³. On the average, urban traffic contributes approximately 1 μg/m³ of PM_2.5 to the urban background across London. According to modeling, approximately two-thirds of the traffic increment originated from exhaust emissions and most of the rest was due to brake and tire wear.

Implications: The urban increment and traffic contribution to the total PM_2.5 are significant and spatially heterogeneous across London. The highly heterogeneous distribution of PM_2.5 hence requires detailed modeling studies to be carried out at high spatial resolution, which can be particularly important for exposure and health impact assessment. This type of information can be used to quantify health impacts resulting from specific sources of PM_2.5 such as traffic emissions, to aid city and national decision makers when formulating pollution control strategies.     

Application of a Three-Layer Photochemical Box Model in an Athens Street Canyon

ABSTRACT

The aim of this paper is to show that a photochemical box model could describe the air pollution diurnal profiles within a typical street canyon in the city of Athens. As sophisticated three-dimensional dispersion models are computationally expensive and they cannot serve to simulate pollution levels in the scale of an urban street canyon, a suitably modified three-layer photochemical box model was applied. A street canyon of Athens with heavy traffic was chosen to apply the aforementioned model. The model was used to calculate pollutant concentrations during two days with meteorological conditions favoring pollutant accumulation. Road traffic emissions were calculated based on existing traffic load measurements. Meteorological data, as well as various pollutant concentrations, in order to compare with the model results, were provided by available measurements. The calculated concentrations were found to be in good agreement with measured concentration levels and show that, when traffic load and traffic composition data are available, this model can be used to predict pollution episodes. It is noteworthy that high concentrations persisted, even after additional traffic restriction measures were taken on the second day because of the high pollution levels.     

A methodology to urban air quality assessment during large time periods of winter using computational fluid dynamic models

The representativeness of point measurements in urban areas is limited due to the strong heterogeneity of the atmospheric flows in cities. To get information on air quality in the gaps between measurement points, and have a 3D field of pollutant concentration, Computational Fluid Dynamic (CFD) models can be used. However, unsteady simulations during time periods of the order of months, often required for regulatory purposes, are not possible for computational reasons. The main objective of this study is to develop a methodology to evaluate the air quality in a real urban area during large time periods by means of steady CFD simulations. One steady simulation for each inlet wind direction was performed and factors like the number of cars inside each street, the length of streets and the wind speed and direction were taken into account to compute the pollutant concentration. This approach is only valid in winter time when the pollutant concentrations are less affected by atmospheric chemistry. A model based on the steady-state Reynolds-Averaged Navier–Stokes equations (RANS) and standard k-? turbulence model was used to simulate a set of 16 different inlet wind directions over a real urban area (downtown Pamplona, Spain). The temporal series of NO_x and PM₁₀ and the spatial differences in pollutant concentration of NO₂ and BTEX obtained were in agreement with experimental data. Inside urban canopy, an important influence of urban boundary layer dynamics on the pollutant concentration patterns was observed. Large concentration differences between different zones of the same square were found. This showed that concentration levels measured by an automatic monitoring station depend on its location in the street or square, and a modelling methodology like this is useful to complement the experimental information. On the other hand, this methodology can also be applied to evaluate abatement strategies by redistributing traffic emissions.     

Variability of Nox and No2 concentrations observed at pedestrian level in the city centre of a medium sized urban area

NO_x and NO₂ concentrations were measured at different locations in a city centre of an urban zone (Population 450 000) in order to study the variation of the outdoor exposure at pedestrian level. These measurements were carried out to understand the influence of traffic emissions at each measured site. The observations were done during four weeks in winter, including several days with high pollution levels. The results at different locations have been used to analyse criteria recommended for locating observation sites in a monitoring network. No large differences in background pollution averaged over several weeks have been found throughout the city centre, even during pollution peaks. Measurements were also carried out inside one street canyon. The contribution of the street traffic to the NO=NO_x−NO₂ concentrations observed at side-walk has been found important, i.e., several times the background level. On the other hand, the majority of observed NO₂ pollution is due to the contribution of background pollution within the street. The pollutant excess at pedestrian level is strongly correlated to the street traffic emission and to the atmospheric turbulence observed at roof level. Application of a box model to the street data demonstrates that such models can be useful to estimate the pollutant accumulation within the street.     

Road traffic impact on urban water quality: a step towards integrated traffic,air and stormwater modelling

Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area.     

Deposition of motor vehicle emissions and winter maintenance along roadside assessed by snow analyses

A vertical snow-sampling method, where a sample was taken throughout the snowpack, was used to estimate the pollutant load on a roadside where average daily traffic density was about 9100 motor vehicles. The snow samples were collected at two sites, forest and open field, at two distances of 10 and 30 m from the road. The concentrations of inorganic anions (Cl(-), NO(-)(3), SO(2-)(4)), total N, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated phenols (PCPhs) were analysed. The results suggest that on roadsides there is a deposition caused by road traffic emissions and winter maintenance which exceeds normal background deposition. Inorganic anions mainly in particle form, originating from winter maintenance, are deposited near the road. PAHs with low molecular weight (相似文献   

Evaluation of particulate matter emissions from non-passenger diesel vehicles in Qatar

ABSTRACT

Road traffic is one of the main sources of particulate matter (PM) in the atmosphere. Despite its importance, there are significant challenges in the quantitative evaluation of its contribution to airborne concentrations. In order to propose effective mitigation scenarios, the proportions of PM traffic emissions, whether they are exhaust or non-exhaust emissions, should be evaluated for any given geographical location. In this work, we report on the first study to evaluate particulate matter emissions from all registered heavy duty diesel vehicles in Qatar. The study was applied to an active traffic zone in urban Doha. Dust samples were collected and characterized for their shape and size distribution. It was found that the particle size ranged from few to 600 μm with the dominance of small size fraction (less than 100 μm). In-situ elemental composition analysis was conducted for side and main roads traffic dust, and compared with non-traffic PM. The results were used for the evaluation of the enrichment factor and preliminary source apportionment. The enrichment factor of anthropogenic elements amounted to 350. The traffic source based on sulfur elemental fingerprint was almost 5 times higher in main roads compared with the samples from non-traffic locations. Moreover, PM exhaust and non-exhaust emissions (tyre wear, brake wear and road dust resuspension) were evaluated. It was found that the majority of the dust was generated from tyre wear with 33% followed by road dust resuspension (31%), brake wear (19%) and then exhaust emissions with 17%. The low contribution of exhaust PM₁₀ emissions was due to the fact that the majority of the registered vehicle models were recently made and equipped with efficient exhaust PM reduction technologies.

Implication: This study reports on the first results related to the evaluation of PM emission from all registered diesel heavy duty vehicles in Qatar. In-situ XRF elemental analysis from main, side roads as well as non-traffic dust samples was conducted. Several characterization techniques were implemented and the results show that the majority of the dust was generated from tyre wear, followed by road dust resuspension and then brake wear; whereas exhaust emissions were tremendously reduced since the majority of the registered vehicle models were recently made and equipped with efficient exhaust PM reduction technologies. This implies that policy makers should place stringent measures on old vehicle license renewals and encourage the use of metro and public transportation.     

Variations in exposure to in-vehicle particle mass and number concentrations in different road environments

Road environments significantly affect in cabin concentration of particulate matter (PM). This study conducted measurements of in-vehicle and on-road concentrations of PM10, PM2.5, PM1, and particle number (PN) in size of 0.02–1 µm, under six ventilation settings in different urban road environments (tunnels, surface roads and elevated roads). Linear regression was then used to analyze the contributions of multiple predictor variables (including on-road concentrations, temperature, relative humidity, time of day, and ventilation settings) to measured variations. On-road measurements of PM2.5, PM1, and PN concentrations from the open surface roads were 5.5%, 3.7%, and 16% lower, respectively, than those measured in tunnels, but 7.6%, 7.1% and 24% higher, respectively, than those on elevated roads. The highest on-road PM10 concentration was observed on surface roads. The time series pattern of in-vehicle particle concentrations closely tracked the on-road concentrations outside of the car and exhibited a smoother profile. Irrespective of road environment, the average I/O ratio of particles was found to be the lowest when air conditioning was on with internal recirculation, the highest purification efficiency via ventilation was obtained by switching on external air recirculation and air conditioning. Statistical models showed that on-road concentration, temperature, and ventilation setting are common factors of significance that explained 58%-80%, 64%-97%, and 87%-98% of the variations in in-vehicle PM concentrations on surface roads, on elevated roads, and in tunnels, respectively.

Implications: Inside vehicles, both driver and passengers will be exposed to elevated particle concentrations. However, for in-vehicle particles, there has been no comprehensive comparative study of the three-dimensional traffic environment including tunnels surface roads and elevated roads. This study focuses on the analysis of the trends and main influencing factors of particle concentrations in different road environments. The results can provide suggestions for the driver's behavior, and provide data support for the environmental protection department to develop pollutant concentration limits within the vehicle.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.