Intercomparison of Numerical Urban Dispersion Models – Part I: Street Canyon and Single Building Configurations

Microscale Computational Fluid Dynamics (CFD) models havebecome an efficient and common simulation tool forassessment and prediction of air quality in urban areas.The proper validation of such a model is a crucialprerequisite for its practical application. Within theframework of the European research network TRAPOS a workinggroup on computational fluid dynamics modelling wasestablished and model intercomparison exercises werelaunched. Different Computational Fluid Dynamics Codes wereapplied for simulating the wind flow and pollutantconcentration patterns in several test cases. The aim ofthe present model intercomparison is (1) to assess andallocate the source of differences that appear whendifferent CFD codes using the same turbulence model areapplied to well defined test cases and (2) to improve theknowledge base for model development and application.Throughout the series of model applications coveringmanifold urban configurations, the overall agreementbetween the various models and experimental data is fair.In spite of quantitative differences between the variousnumerical results, the models are capable of reproducingthe flow patterns and dispersion characteristics observedin urban areas but they show significant differences forthe turbulent kinetic energy field that controls thedispersion of pollutants.     

Application of Dispersion Modelling for Analysis of Particle Pollution Sources in a Street Canyon

The dominating source of particles in urban air is road traffic. In terms of number concentration, its main contribution is within the range of ultrafine particles (Dp < 100 nm). The dispersion conditions, i.e. transport and dilution, of the submicrometer particles are expected to be like for gases and therefore the particle concentrations in a street canyon can be calculated using gaseous pollutants dispersion models. Such processes, like coagulation or condensation, are less important due to the short residence time within the street canyon environment.Two extensive measuring campaigns were conducted in the street Jagtvej in Copenhagen, Denmark. The particle size distributions were measured by a Differential Mobility Analyser (DMA) coupled to a particle counter, providing high time resolution data (1/2 hourly) on a continuous basis. Measurements of NO_x, CO and meteorological parameters were also available. The measured particle number concentrations, especially below 100 nm, reveal very similar dependence on the meteorological conditions as the NO_x concentrations. This underpins the conclusion that dilution properties are similar for particles and NO_x. For particle sizes over 100 nm, somewhat different behaviour is observed. This points toward existence of additional particle sources, not related to traffic emissions within the street canyon. A significant contribution is believed here to be attributed to long-range transport. The total particle emission from traffic, including daily variation and size distribution, has been calculated using the OSPM dispersion model. Results are in accordance with a previous analysis based on statistical modelling.     

Smoke Plume Trajectory Modeling

A combination of numerical modeling and large-scale experimentation has yielded a tremendous amount of information about the structure, trajectory and composition of smoke plumes from large crude oil fires. A numerical model, a large outdoor fire plume trajectory (ALOFT), has been developed at NIST to predict the downwind concentration of smoke and other combustion products. The model is based on the fundamental conservation equations that govern the introduction of hot gases and particulate matter from a large fire into the atmosphere. The model has been used to estimate distances from fires under of variety of meteorological and topographic conditions where ground level concentrations of smoke and combustion products fall below regulatory threshold levels.     

An Evaluation of Daumod Model in Estimating Urban Background Concentrations

This article presents an evaluation of the performance of the urban atmospheric dispersion model (DAUMOD) in estimating nitrogen oxides (NO_x) background concentrations in Copenhagen. Estimations of hourly average (averaged over a year), mean daily and mean monthly concentrations of NO_x are compared with observed values for two years of data. The model slightly underestimates low hourly average values and overestimates high values. The cumulative frequency distribution of mean daily concentration obtained from model estimations is in good agreement with the obtained from observed data. We performed a statistical analysis to determine the agreement between estimated and observed concentration values. The results show that 95.8% of hourly average estimations, 86.8% of mean daily and 100% of monthly average concentrations are within a factor of two of the observed values. The normalised mean square error of predictions is +0.13 for hourly average estimations, +0.22 for mean daily values and +0.02 for monthly mean concentrations. The fractional bias values are: –0.049 for hourly mean estimations, –0.047 for mean daily values and –0.053 for monthly average estimations. The values of the statistical parameters allow us to consider that though estimations are lightly larger than the observed values, the model performance is acceptable.     

Influence of Traffic Emissions Estimation Variability on Urban Air Quality Modelling

The main objective of this work is to analyse how uncertainties in emission data of nitrogen oxides (NO_x) and volatile organic compounds (VOC), originated from road traffic, influence the model prediction of ozone (O₃) concentration fields. Different methods to estimate emissions were applied and results were compared in order to obtain their variability. Based on these data, different emission scenarios were compiled for each pollutant considering the minimum and the maximum values of the estimated emission range. These scenarios were used as input to the MAR-IV mesoscale modelling system. Simulations have been performed for a summer day in the Northern Region of Portugal. The different approaches to estimate NO_x and VOC traffic emissions show a significant variability of absolute values and of their spatial distribution. Comparison of modelling results obtained from the two scenarios presents a dissimilarity of 37% for ozone concentration fields as a response of the system to a variation in the input emission data of 63% for NO_x and 59% for VOC. Far beyond all difficulties and approximations, the developed methodology to build up an emission data base shows to be consistent and an useful tool in order to turn applicable an air quality model. Nevertheless, the sensitivity of the model to input data should be considered when it is used as a decision support tool.     

Modelling nitrogen fluxes at the landscape scale

The distribution and impacts of different nitrogen pollutants are inextricably linked. To understand the problem fully, the interactions between the different pollutants need to be taken into account. This is particularly important when it comes to abatement techniques, since measures to reduce emissions of one nitrogen pollutant can often lead to an increase in another. This project represents a step towards greater understanding of these issues by linking together new and existing nitrogen flux models into a larger framework. The modelling framework has been constructed and some of the nitrogen flows between fields, farms and the atmosphere have been modelled for a UK study area for typical farm management scenarios.     

Analysis of the St. Petersburg Traffic Data using the OSPM Model

Since October 1998 two DOAS instruments were installed at the level of the first floor and at the top of a building located in St. Petersburg at Pestelya Street. The collected datacovers the time period of December 1998–March 2001, and include concentrations of benzene, toluene, NO and NO₂, ozone and SO₂. There is also an additional information about the traffic intensity and meteorological conditions. The results of the analysis of this data set, using the OSPM model, are presented here with the goal to understand the features of the air pollution dispersion in this street canyon and to analyse the information about the emission factors of the vehicles. In particular, the model results are used for the solution of the inverse problem of reconstructing the emission factors from measured concentrations. The results obtained indicate that most of the concentrations are well inside the Russian standards with the only exception of NO₂ (mean and 98-th percentile are equal to 57.8 and 119.2 g m^-3 for the street level). The same values for benzene are 18.5 and 62.6, respectively. Emission estimates show that there is a possibility that the NO_x and benzene basic emission factors recommended by the Russian national guidelines could result in overestimating the traffic emissions. These considerations are supplemented with the model sensitivity tests carried out in connection with the problem of predictability of NO₂ concentrations in the street canyon. Tests indicate that NO₂ concentrations are not very sensitive to NO_x emissions because of the usually low urban background ozone levels.     

焦化项目大气污染特征及环境影响评价

以甘肃省酒泉市阿克塞哈萨克族自治县某焦化项目为案例,在分析其工艺流程和排污环节的基础上,利用AERMOD模型定量预测评价该焦化项目对区域大气环境质量的影响程度。预测结果表明：SO2,NO2,NH3,H2S的区域最大地面小时质量浓度占标率分别为15.8%,29.6%,16.8%,24.8%;SO2、NO2、苯并[a]芘、固体悬浮物（TSP）的区域最大日均质量浓度占标率分别为7.5%,11.4%,7.2%,95.8%;TSP最大日均质量浓度坐标点为（500,0）,位于厂界内部,高浓度是由焦化厂低矮面源造成的,且浓度随距离消减得较快。     

Improvements in Air Quality Modelling by Using Surface Boundary Layer Parameters derived from Satellite Land Cover Data

Many atmospheric dispersion models include only simpletreatment of surface features to estimate the wind profilesand stability parameters. Detailed characterisation of theland cover, particularly in large and complex urbanconurbations, is especially important, as the surfacefeatures can vary significantly over the area. This paperdiscusses the use of satellite land cover data to derivespatially resolved surface boundary layer (SBL) parameters.These parameters have been used in an air quality model,PEARL (Prediction Air Quality in Urban and RegionalLocations) for estimating monthly and annual COconcentrations. Land cover data, derived from LANDSATThematic Mapper Imagery, has been used to estimate SBLparameters (surface roughness length, albeedo, Bowen ratioand anthropogenic heat flux) for a study area of 10000km² encompassing Greater London and the surroundingcounties. The SBL parameters have been assigned according tomajor land cover types for the whole area at a spatialresolution of 1 × 1 km. Predictions from two versions of the PEARL model (one with land cover data and one without)have been compared with each other and with measured data forannual and monthly CO concentrations from seven London airquality monitoring sites. This comparison shows thatdifferences between predicted and observed values can bereduced by up to a factor of three. The use of SBLparameters derived from land cover data also yields moredetailed predicted annual CO spatial patterns especially inand around suburban areas. The performance of both versionsof the model for monthly CO concentrations has been comparedwith a range of statistical measures. This comparisonconfirms that improved agreement is observed betweenmodelled and measured monthly CO concentrations when use ismade of spatially resolved SBL parameters.     

ROADWAY-2: A Model for Pollutant Dispersion near Highways

The formulations and evaluation of ROADWAY-2, a near-highway pollutant dispersion model, are described. This model incorporates vehicle wake parameterizations derived from canopy flow theory and wind tunnel measurements. The atmospheric velocity and turbulence fields are adjusted to account for velocity-deficit and turbulence production in vehicle wakes. A turbulent kinetic energy closure model of the atmospheric boundary layer is used to derive the mean velocity, temperature, and turbulence profiles from input meteorological data. ROADWAY-2 has been evaluated using SF₆ tracer data from General Motors Sulfate Dispersion Experiment. The model evaluationresults are presented and discussed.