A simple urban dispersion model tested with tracer data from Oklahoma City and Manhattan

A simple urban dispersion model is tested that is based on the Gaussian plume model and modifications to the Briggs urban dispersion curves. An initial dispersion coefficient (σ_o) of 40 m is assumed to apply in built-up downtown areas, and the stability is assumed to be slightly unstable during the day and slightly stable during the night. Observations from tracer experiments during the Joint Urban 2003 (JU2003) field study in Oklahoma City and the Madison Square Garden 2005 (MSG05) field study in Manhattan are used for model testing. The tracer SF₆ was released during JU2003 near ground level in the downtown area and concentrations were observed at over 100 locations within 4 km from the source. Six perfluorocarbon tracer (PFT) gases were released near ground level during MSG05 and sampled by about 20 samplers at the surface and on building roofs. The evaluations compare concentrations normalized by source release rate, C/Q, for each sampler location and each tracer release, where data were used only if both the observed and predicted concentrations exceeded threshold levels. At JU2003, for all samplers and release times, the fractional mean bias (FB) is about 0.2 during the day (20% mean underprediction) and 0.0 during the night. About 45 –50% of the predictions are within a factor of two (FAC2) of the observations day and night at JU2003. The maximum observed C/Q is about two times the maximum predicted C/Q both day and night. At MSG05, for all PFTs, surface samplers, and release times, FB is 0.14 and FAC2 is about 45%. The overall 60 min-averaged maximum C/Q is underpredicted by about 40% for the surface samplers and is overpredicted by about 25% for the building-roof samplers.     

Simulation of short-range diffusion experiment in low-wind convective conditions

A previously obtained analytical solution to model the short-range dispersion of pollutants in low winds from surface releases has been used to simulate diffusion tests conducted during winter in weakly convective conditions at the Indian Institute of Technology (IIT) Delhi. The turbulence parameterization based on friction velocity has been tested to simulate diffusion experiment. Such a parameterization in this study is considered justifiable on two counts: (1) prevailing meteorological and dispersion conditions have been generally of weakly unstable type as indicated by values of Monin–Obukhov length and bulk Richardson number, (2) uncertainties associated with the application of convective velocity based similarity parameterization to simulation of diffusion experiment at IIT Delhi, resulting in significant underprediction in most of the cases (Atmos. Environ. 30 (1996a) 1137). With this parameterization, the model simulations have improved considerably and compare reasonably well with the observations. Further, the results from a simple Gaussian model have been included for comparison. This study is in continuation of the work done earlier to simulate near-source dispersion in weak winds.     

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.     

Characteristics of puff dispersion in an urban environment

Instantaneous releases of sulfur hexafluoride tracer were carried out as part of the Joint Urban 2003 field campaign in Oklahoma City. Data from 10 fast-response tracer samplers were used to examine the crosswind and along-wind spread of the tracer, the decay of tracer concentrations, and the retention of tracer within approximately 1 km of the release locations. The time variation of the median values of the tracer concentrations, normalized by the peak value observed at a given sampler, could be approximately described by an exponential decay with characteristic decay times on the order of 1–2 min. The longer times were found for early morning releases and the shorter times were associated with later morning or afternoon releases, suggesting that atmospheric stability or the depth of the mixed layer may affect puff dispersion even in urban environments. The median retention times required for 99% of the exposure to be realized at a given location were found to be correlated reasonably well with the median decay times. These characteristic time scales should be regarded as lower limits for concentration decay because the analysis excluded a number of anomalous cases in which the decaying concentrations exhibited an extended tail that indicated a very slow ventilation rate. The median values of the along-wind dispersion parameter σ_x grouped into downwind distance ranges can be described by a linear variation with distance with an initial “hold up” contribution due to building effects of about 30–45 m, but there are considerable variations about this relationship. Downwind 0.5–1 km from the release point the lateral puff dispersion (σ_y) was roughly 70% of the along-wind dispersion.     

Frank A. Chambers

Abstract

This work assessed the usefulness of a current air quality model (American Meteorological Society/Environmental Protection Agency Regulatory Model [AERMOD]) for predicting air concentrations and deposition of perfluorooctanoate (PFO) near a manufacturing facility. Air quality models play an important role in providing information for verifying permitting conditions and for exposure assessment purposes. It is important to ensure traditional modeling approaches are applicable to perfluorinated compounds, which are known to have unusual properties. Measured field data were compared with modeling predictions to show that AERMOD adequately located the maximum air concentration in the study area, provided representative or conservative air concentration estimates, and demonstrated bias and scatter not significantly different than that reported for other compounds. Surface soil/grass concentrations resulting from modeled deposition flux also showed acceptable bias and scatter compared with measured concentrations of PFO in soil/grass samples. Errors in predictions of air concentrations or deposition may be best explained by meteorological input uncertainty and conservatism in the PRIME algorithm used to account for building downwash. In general, AERMOD was found to be a useful screening tool for modeling the dispersion and deposition of PFO in air near a manufacturing facility.     

Implementation of a windblown dust parameterization into MODELS-3/CMAQ: Application to episodic PM events in the US/Mexico border

Windblown dust is known to impede visibility, deteriorate air quality and modify the radiation budget. Arid and semiarid areas with unpaved and unvegetated land cover are particularly prone to windblown dust, which is often attributed to high particulate matter (PM) pollution in such areas. Yet, windblown dust is poorly represented in existing regulatory air quality models. In a study by the authors on modeling episodic high PM events along the US/Mexico border using the state-of-the-art CMAQ/MM5/SMOKE air quality modeling system [Choi, Y.-J., Hyde, P., Fernando, H.J.S., 2006. Modeling of episodic particulate matter events using a 3D air quality model with fine grid: applications to a pair of cities in the US/Mexico border. Atmospheric Environment 40, 5181–5201], some of the observed PM₁₀ NAAQS exceedances were inferred as due to windblown dust, but the modeling system was incapable of dealing with time-dependent episodic dust entrainment during high wind periods. In this paper, a time-dependent entrainment parameterization for windblown dust is implemented in the CMAQ/MM5/SMOKE modeling system with the hope of improving PM predictions. An approach for realizing windblown dust emission flux for each grid cell over the study domain on an hourly basis, which accounts for the influence of factors such as soil moisture content, atmospheric stability and wind speed, is presented in detail. Comparison of model predictions with observational data taken at a pair of US/Mexico border towns shows a clear improvement of model performance upon implementation of the dust emission flux parameterization.     

A note on subgrid-scale processes in photochemical modelling

The effects of including a parameterization of subgrid-scale dispersion and chemical processes in the Eulerian-Lagrangian transport model of Hess are investigated. A case chosen to maximize the subgrid-scale effects is studied, and it is found that the predicted change in maximum O₃ when subgrid-scale processes are included is small (∼5%) compared with predictions based on direct injection of point emissions. It is also found for this model that the parameterization of subgrid-scale processes is not necessary to provide numerical stability in calculating the chemical kinetics.     

Spatially differentiated and source-specific population exposure to ambient urban air pollution

Models assessing exposure to air pollution often focus on macro-scale estimates of exposure to all types of sources for a particular pollutant across an urban study area. While results based on these models may aid policy makers in identifying larger areas of elevated exposure risk, they often do not differentiate the proportion of population exposure attributable to different polluting sources (e.g. traffic or industrial). In this paper, we introduce a population exposure modeling system that integrates air dispersion modeling, Geographic Information Systems (GIS), and population exposure techniques to spatially characterize a source-specific exposure to ambient air pollution for an entire urban population at a fine geographical scale. By area, total population exposure in Dallas County in 2000 was more attributable to vehicle polluting sources than industrial polluting sources at all levels of exposure. Population exposure was moderately correlated with vehicle sources (r = 0.440, p < 0.001) and weakly with industrial sources (r = 0.069, p = 0.004). Population density was strongly correlated with total exposure (r = 0.896, p < 0.001) but was not significantly correlated with individual or combined sources. The results of this study indicate that air quality assessments must incorporate more than industrial or vehicle polluting sources-based population exposure values alone, but should consider multiple sources. The population exposure modeling system proposed in this study shows promise for use by municipal authorities, policy makers, and epidemiologists in evaluating and controlling the quality of the air in the process of urban planning and mitigation measures.     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Referencing of street-level flows measured during the DAPPLE 2004 campaign

Street-level mean flow and turbulence govern the dispersion of gases away from their sources in urban areas. A suitable reference measurement in the driving flow above the urban canopy is needed to both understand and model complex street-level flow for pollutant dispersion or emergency response purposes. In vegetation canopies, a reference at mean canopy height is often used, but it is unclear whether this is suitable for urban canopies. This paper presents an evaluation of the quality of reference measurements at both roof-top (height = H) and at height z = 9H = 190 m, and their ability to explain mean and turbulent variations of street-level flow. Fast response wind data were measured at street canyon and reference sites during the six-week long DAPPLE project field campaign in spring 2004, in central London, UK, and an averaging time of 10 min was used to distinguish recirculation-type mean flow patterns from turbulence. Flow distortion at each reference site was assessed by considering turbulence intensity and streamline deflection. Then each reference was used as the dependent variable in the model of Dobre et al. (2005) which decomposes street-level flow into channelling and recirculating components. The high reference explained more of the variability of the mean flow. Coupling of turbulent kinetic energy was also stronger between street-level and the high reference flow rather than the roof-top. This coupling was weaker when overnight flow was stratified, and turbulence was suppressed at the high reference site. However, such events were rare (<1% of data) over the six-week long period. The potential usefulness of a centralised, high reference site in London was thus demonstrated with application to emergency response and air quality modelling.     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

WinMISKAM is evaluated from an emergency response perspective. Comparisons are made between ground level concentrations observed during selected Mock Urban Setting Test (MUST) field trials and predictions generated by the model. The model was driven by 5 min averaged on-site meteorological data, and used minimum grid spacing of 0.5 m in both the horizontal and vertical. The code was found to perform well, with 46% of all predictions (paired in time and space) and 83% of arc maxima predictions within a factor of two of observed concentrations. The model was found to perform better for neutral cases than stable cases with 27% of stable case predictions and 57% of neutral case predictions within a factor of two when compared in time and space.     

Dispersion modelling of a wintertime particulate pollution episode in Christchurch,New Zealand

This paper examines the inter-suburb dispersion of particulate air pollution in Christchurch, New Zealand, during a wintertime particulate pollution episode. The dispersion is simulated using the RAMS/CALMET/CALPUFF modelling system, with data from a detailed emissions inventory of home heating, motor vehicles and industry. During the period 27 July–1 August 1995, peak 1 h and 24 h PM₁₀ concentrations of 368 and 107 μg m⁻³, respectively, were observed. Peak concentrations occurred at night, when particulate emissions from wood- and coal-burning domestic heating appliances were at a maximum and emitted into a stable boundary layer. The model is generally able to reproduce the observed PM₁₀ time series recorded at surface monitors located throughout the urban area. For this simulation, the fractional gross error ranges between 0.69 and 0.99, and the fractional bias ranges between −0.17 and 0.30. Strong horizontal concentration gradients of 100 μg m⁻³ km⁻¹, both in the observational record and model predictions, are apparent. Three emission reduction options, designed to reduce the severity of particulate pollution episodes in Christchurch, are simulated. When both domestic open-hearth fires and all coal burning are removed, the 24 h average peak concentration is reduced by 55%. The number of guideline exceedences of PM₁₀ in the modelled period is reduced from five to one. Removing open-hearth fires results in 42% reduction in PM₁₀ concentration, resulting in three exceedences of the guideline, and removing coal-burning fires yields a 32% reduction in PM₁₀, resulting in four exceedences of the guideline.     

A Comparison of Model Performance between AERMOD and AUSTAL2000

ABSTRACT

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäβ der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

IMPLICATIONS This study contributes to the understanding of dispersion modeling and demonstrates the advantage of detailed meteorological data. It also helps air quality regulators and planners to identify the most appropriate model to use. It is indicated that AERMOD is more suitable for air quality planning and regulation, when all required meteorological information is available, because its predictions are mostly closer to field observations. Furthermore AUSTAL2000 predicted concentrations that showed a narrow range and triggered far less impacts from the source.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

Using CALINE dispersion to assess vehicular PM2.5 emissions

This paper explores the range of CALINE4's PM_2.5 modeling capabilities by comparing previously collected PM_2.5 data with CALINE4 predicted values. Two sampling sites, a suburban site located at an intersection in Sacramento, CA, and an urban site located in London, were used. Predicted concentrations are graphed against observed concentrations and evaluated against the criterion that 75% of the points fall within the factor-of-two prediction envelope. For the suburban site, data estimated by CALINE4 produced results that fell within the acceptable factor-of-two percentage envelope. A reverse dispersion test was also conducted for the suburban site using observed and calculated emission factors, and although it showed correlations between the observed values and CALINE4 predicted values, it could not conclusively prove that the model is accurate at predicting PM_2.5 concentrations. Although the results suggest that CALINE4 PM_2.5 predictions may be reasonably close to observed values, the number of observations used to verify the model was small and consequently, findings from the suburban site should be considered exploratory. For the urban site, a much larger data set was evaluated; however, the CALINE4 results for this site did not fall 75% within the factor-of-two envelope. Several factors, including street canyon effects, likely contributed to an inaccuracy of the emission factors used in CALINE4, and therefore, to the overall CALINE4 predictions. In summary, CALINE4 does not appear to perform well in densely populated areas and differences in topography may be a decisive factor in determining when CALINE4 may be applicable to modeling PM_2.5. For critical transportation projects requiring PM_2.5 analysis, use of CALINE4 may not be optimal because of its inability to produce reasonable estimates for highly trafficked areas. Additional data sets for CALINE4 analysis, particularly in urban environments, are required to fully understand CALINE4's PM_2.5 modeling capabilities.     

A Comparison of Measured and Simulated Ozone Concentrations in the Rural Areas of the Eastern United States during Summer 1995

ABSTRACT

The recent regulatory actions toward a longer-term (i.e., 8-hr) average ozone standard have brought forth the potential for many rural areas in the eastern United States to be in noncompliance. However, since a majority of these rural areas have generally few sources of anthropogenic emissions, the measured ozone levels primarily reflect the effects of the transport of ozone and its precursor pollutants and natural emissions. While photochemical grid models have been applied to urban areas to develop ozone mitigation measures, these efforts have been limited to high ozone episode events only and do not adequately cover rural regions. In this study, we applied a photochemical modeling system, RAMS/UAM-V, to the eastern United States from June 1-August 31, 1995. The purpose of the study is to examine the predictive ability of the modeling system at rural monitoring stations that are part of the Clean Air Status Trends Network (CASTNet) and the Gaseous Pollutant Monitoring Program (GPMP).

The results show that the measured daily 1-hr ozone maxima and the seasonal average of the daily 1-hr ozone maxima are in better agreement with the predictions of the modeling system than those for the daily 8-hr ozone maxima. Also, the response of the modeling system in reproducing the measured range of ozone levels over the diurnal cycle is poor, suggesting the need for improvement in the treatment of the physical and chemical processes of the modeling system during the nighttime and morning hours if it is to be used to address the 8-hr ozone standard.     

A Tracer Study of Dispersion Over a City

The planning and conduct of series of tracer experiments carried out in St. Louis in the period between 1963 and 1965 is described. Tentative results indicate that horizontal dispersion over an urban area does not differ greatly from that observed over open country, except for a much greater initial spreading of the tracer plume. Vertical dispersion during the daytime does not appear to differ greatly from that observed over open country, and can be best expressed in terms of travel time rather than travel distance. Vertical dispersion during the evening over an urban area is much greater than that observed over open country; the limited results obtained suggest the formation of a slightly unstable layer as the air flows over the city.     

Validation of the UK Met. Office’s name model against the ETEX dataset

The ETEX 1 data set has been used to assess the performance of the UK Met Office’s long-range dispersion model NAME. In terms of emergency response modelling the model performed well, successfully predicting the overall spread and timing of the plume across Europe. However, in common with most other models, NAME overpredicted the observed concentrations. This is in contrast with other NAME validation studies which indicate either no significant bias or a tendency to underpredict concentrations. This suggests the reasons for overpredicting are specific to the ETEX situation. Explanations include inadequate vertical diffusion or transport, possible venting by convective activity, and experimental errors. An assessment of a range of advection schemes of varying complexity indicated no clear advantage, at present, in using more sophisticated random walk techniques at long range, a simple diffusion coefficient based scheme providing some of the best results. A brief look is also taken at a simulation of the more problematical ETEX 2 release.