Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.     

Experimental and numerical study of the dispersion of motor vehicle pollutants under idle condition

The aim of the work presented here is to study experimentally and numerically the dispersion characteristics of vehicular exhaust plume at an idle condition in an idealized and simplified environment. The gaseous and particulate concentrations in the exhaust plume of three idling motor vehicles were measured in an isolated environment under calm weather conditions. Despite the difference in the initial concentrations, the pollutants decayed exponentially in all directions.The CFD code PHOENICS 3.3, with the k–ε eddy dissipation sub-model, was used for the numerical simulation. The simulated results match very well with the experimental results close to the source of emission but decay to the ambient concentrations much slower. The effects of the initial emission concentration, exit velocity, exit direction and crosswind intensity have been investigated parametrically. The initial pollutant concentration will increase the local concentrations but the pattern of dispersion remains the same. The exit velocity will increase the momentum of the jet, resulting in a deeper penetration downstream. The exit angle has a stronger influence on pollutant dispersion than both initial pollutant concentration and exit velocity. When the exit angle is 15°, the pollutants tend to spread on the ground region. Crosswind shows a significant effect on the dispersion of the exhaust plume also. It will divert the plume to disperse in the same direction of the wind with limited penetration in the downstream direction.     

Performance of a Gaussian model for centerline concentrations in the wake of buildings

This paper examines the performance and inherent limitations of a Gaussian plume model for predicting ground-level plume centerline concentrations in the wake of buildings. The Gaussian plume equation has been modified to incorporate building wake enhanced dispersion parameters. Model-predicted concentrations were compared to three sets of field observations. Predicted and observed concentrations were partitioned into groups by source-receptor distance, atmospheric stability class, and source-release height. The group analyses provided a way to identify sources of model error. The variability of errors was found to have values between 50 and 100 % of the mean observed concentration for groups where the mean error was small.     

The effects of roadside structures on the transport and dispersion of ultrafine particles from highways

Understanding local-scale transport and dispersion of pollutants emitted from traffic sources is important for urban planning and air quality assessments. Predicting pollutant concentration patterns in complex environments depends on accurate representations of local features (e.g., noise barriers, trees, buildings) affecting near-field air flows. This study examined the effects of roadside barriers on the flow patterns and dispersion of pollutants from a high-traffic highway in Raleigh, North Carolina, USA. The effects of the structures were analyzed using the Quick Urban & Industrial Complex (QUIC) model, an empirically based diagnostic tool which simulates fine-scale wind field and dispersion patterns around obstacles. Model simulations were compared with the spatial distributions of ultrafine particles (UFP) from vehicular emissions measured using a passenger van equipped with a Differential Mobility Analyzer/Condensation Particle Counter. The field site allowed for an evaluation of pollutant concentrations in open terrain, with a noise barrier present near the road, and with a noise barrier and vegetation present near the road.Results indicated that air pollutant concentrations near the road were generally higher in open terrain situations with no barriers present; however, concentrations for this case decreased faster with distance than when roadside barriers were present. The presence of a noise barrier and vegetation resulted in the lowest downwind pollutant concentrations, indicating that the plume under this condition was relatively uniform and vertically well-mixed. Comparison of the QUIC model with the mobile UFP measurements indicated that QUIC reasonably represented pollutant transport and dispersion for each of the study configurations.     

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM_2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM_2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in open areas to 70° or more for sites in very complex terrain. The analysis also showed some days with good forecast meteorology with absolute mean error in wind direction less than 30° when ClearSky correctly predicted PM_2.5 surface concentrations at receptors affected by field burns. On several other days with similar levels of wind direction error the model did not predict apparent plume impacts. In most of these cases, there were no reported burns in the vicinity of the monitor and, thus, it appeared that other, non-reported burns were responsible for the apparent plume impact at the monitoring site. These cases do not provide information on the performance of the model, but rather indicate that further work is needed to identify all burns and to improve burn reports in an accurate and timely manner. There were also a number of days with wind direction errors exceeding 70° when the forecast system did not correctly predict plume behavior.     

Design and Verification of a Programmable Gas Dispensing System for Exposing Plants to Dynamic Concentrations of Air Pollutants

Abstract

Landfills represent a source of distributed emissions source over an irregular and heterogeneous surface. In the method termed “Other Test Method-10” (OTM-10), the U.S. Environmental Protection Agency (EPA) has proposed a method to quantify emissions from such sources by the use of vertical radial plume mapping (VRPM) techniques combined with measurement of wind speed to determine the average emission flux per unit area per time from nonpoint sources. In such application, the VRPM is used as a tool to estimate the mass of the gas of interest crossing a vertical plane. This estimation is done by fitting the field-measured concentration spatial data to a Gaussian or some other distribution to define a plume crossing the vertical plane. When this technique is applied to landfill surfaces, the VRPM plane may be within the emitting source area itself. The objective of this study was to investigate uncertainties associated with using OTM-10 for landfills. The spatial variability of emission in the emitting domain can lead to uncertainties of –34 to 190% in the measured flux value when idealistic scenarios were simulated. The level of uncertainty might be higher when the number and locations of emitting sources are not known (typical field conditions). The level of uncertainty can be reduced by improving the layout of the VRPM plane in the field in accordance with an initial survey of the emission patterns. The change in wind direction during an OTM-10 testing setup can introduce an uncertainty of 20% of the measured flux value. This study also provides estimates of the area contributing to flux (ACF) to be used in conjunction with OTM-10 procedures. The estimate of ACF is a function of the atmospheric stability class and has an uncertainty of 10–30%.     

Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

The intent of this paper is to relate the magnitude of the error bounds of data, used as inputs to a Gaussian dispersion model, to the magnitude of the error bounds of the model output, which include the estimates of the maximum concentration and the distance to that maximum. The research specifically addresses the uncertainty in estimating the maximum concentrations from elevated buoyant sources during unstable atmospheric conditions, as these are most often of practical concern in regulatory decision making. A direct and quantitative link between the nature and magnitude of the input uncertainty and modeling results has not been previously investigated extensively. The ability to develop specific error bounds, tailored to the modeling situation, allows more informed application of the model estimates to the air quality issues.In this study, a numerical uncertainty analysis is performed using the Monte-Carlo technique to propagate the uncertainties associated with the model input. Uncertainties were assumed to exist in four model input parameters: (1) wind speed, (2) standard deviation of lateral wind direction fluctuations, (3) standard deviation of vertical wind direction fluctuations, and (4) plume rise. For each simulation, results were summarized characterizing the uncertainty in four features of the ground-level concentration pattern predicted by the model: (1) the magnitude of the maximum concentration, (2) the distance to the maximum concentration, and (3) and (4) the areas enclosed within the isopleths of 50% and 25% of the error-free estimate of maximum concentration.The authors conclude that the error bounds for the estimated maximum concentration and the distance to the maximum can be double that of the error bounds for individual model input parameters. The model output error bounds for the areas enclosed within isopleth values can be triple the error bounds of the input. It was not our intent to cover all possible combinations for the error in the input parameters. Ours was a much more limited goal of providing a lower bound estimate of model uncertainty in which we assume the input is reasonably well characterized and there is no bias in the input. These results allow estimation of minimum bounds on errors in model output when considering reasonable input error bounds.     

Boundary Layer Emission Monitoring

A new methodology is described for determining the atmospheric emission rate of pollutants from large heterogeneous area sources, such as hazardous waste sites. The procedure hinges upon measuring average pollutant concentrations, at three or more different elevations, while traversing the plume downwind of the area source. A helium-filled tethersonde balloon is used to elevate the sampling lines to their appropriate height. During plume traversing the sampling rate is adjusted to be proportional to the sine of the angle between the wind vector and the direction of the traverse path. The average concentrations are corrected for any upwind, background concentration and then used to derive an average vertical concentration profile. This profile Is numerically integrated, with the wind velocity profile, over the pollutant boundary layer to yield the area source emission rate. The methodology was tested on several large industrial effluent lagoons and proved to be easy to use, robust, and precise.     

Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms

The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source.The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.     

Comparison of concentration/flux ratios measured with a backscatter Lidar and with a chemical tracer

A series of backscatter Lidar measurements were made around a bio-waste power station at Eye in Suffolk over a period of 10 days in May 1999. These measurements were supplemented with bag samples of SF₆ tracer, analysed on site using gas chromatography with an electron capture detector. Despite problems with contamination, a detection limit of 20 ppt was eventually achieved and this permitted useful plume measurements from a release rate of 1–2 l min⁻¹. Concentration/flux ratios were estimated from the Lidar measurements using an integral technique. Of the Lidar runs obtained, 24 were coincident with a tracer release. After allowing for the background of both aerosol and tracer, it was apparent that the independent calibrations of concentration/flux ratio from Lidar or tracer agreed with each other to within 20–60%. This permits the Lidar scans to be used to estimate peak near-ground concentrations, though because of various technical difficulties (poor alignment, background sources of aerosol, or an inconvenient wind direction) this cannot always be achieved. Besides giving confidence in the Lidar calibration, the tracer measurements were valuable in permitting aerosol from the stack to be distinguished from aerosol from fugitive sources. Meteorological parameters were logged simultaneously with the dispersion measurements. These parameters included conventional means of wind speed and direction, temperature, humidity and insolation, and also micrometeorological measurements of turbulence and of turbulent fluxes. The Lidar was used to estimate wind speed and direction at plume height and the boundary layer depth and cloud-base where possible. Source emission characteristics were also logged.     

Improving pollutant source characterization by better estimating wind direction with a genetic algorithm

In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data.     

A Method of Predicting Point and Path- Averaged Ambient Air VOC Concentrations,Using Meteorological Data

A method of predicting point and path-averaged ambient air VOC concentrations is described. This method was developed for the case of a plume generated from a single point source, and is based on the relationship between wind directional frequency and concentration. One-minute means of wind direction and wind speed were used as inputs to a Gaussian dispersion model to develop this relationship.

Both FTIR spectrometry and a whole-air sampling method were used to monitor VOC plumes during simulated field tests. One test set was also conducted using only whole-air samplers deployed in a closely-spaced network, thus providing an evaluation of the prediction technique free of any bias that might exist between the two analytical methods.

Correlations between observed point concentrations and wind directional frequencies were significant at the 0.05 level in most cases. Predicted path-integrated concentrations, based on observed point concentrations and meteorological data, were strongly correlated with observed values. Predicted point concentrations, based on observed path-integrated concentrations and meteorological data, accurately reflected the location and magnitude of the highest concentrations from each test, as well as the shape of the concentration-versus-crosswind distance curve.     

Episodes of high SO2 concentration in The Netherlands

In episodes of high daily mean SO₂ concentrations in the atmosphere there is a very extensive concentration field over The Netherlands caused by influx from both the Ruhr area and Central Europe. The meteorological conditions for such episodes range from very stable to stable with a boundary layer height between 100 and 450 m, wind speed at 200 m between 0 and 18 m s⁻¹ and at 20 m between 0 and 9 m s⁻¹. The last figures indicate that transport, as well as transport combined with stagnation, causes the high concentrations. The episodes nearly all occur in December, January and February, with easterly winds. In two thirds of the cases a local snow cover is present. This cover is more extensive in the east, so probably in all cases there is reduced dry deposition of SO₂. The turbulence is very low with hardly any vertical dispersion at higher altitudes, except around noon. In consequence of the wind direction variations over a day and the wind direction shear, the daily horizontal dispersion is normal or larger than normal. Therefore the contribution of a local high point source in The Netherlands to the ground level concentration will be small during air pollution episodes in winter.     

Control of Asphaltic Concrete Plants in Los Angeles County

Characteristics of maximum short-term ground level concentrations from an elevated point source, namely, the effective plume height, the critical wind speed, the distance to the point of maximum concentration, and the maximum concentration, are derived from the gaussian plume model. Both phases of plume development—before and after it has reached its final height—are considered. The plume rise treatment includes both thermal buoyancy and momentum effects. Certain limitations on critical wind speed are discussed. The dispersion model whose basis is established in this paper should be especially useful in applications where on site meteorological data are unavailable.     

Dispersion over and around a steep obstacle for varying thermal stratification—numerical simulations

A rather steep dump of an open-cast working area for lignite is situated close to the nuclear research installation of Juelich. This dump is more than 200 m high; under light wind conditions the air flow goes around this obstacle during night-time (stable stratification) and over it in the early morning hours when the stratification is destabilized. This air flow is simulated by the non-hydrostatic mesoscale numerical model FITNAH; for the time 0500–0900 LST the concentration field (of a point source) is simulated with a Lagrangian particle model. The computed flow behaviour causes a bifurcation of the plume during night while after 0900 LST dispersion is nearly unaffected by topography.     

A new Lagrangian particle model for the simulation of dense gas dispersion

A Lagrangian stochastic model (MicroSpray), able to simulate the airborne dispersion in complex terrain and in presence of obstacles, was modified to simulate the dispersion of dense gas clouds. This is accomplished by taking into account the following processes: negative buoyancy, gravity spreading and the particle's reflection at the bottom computational boundary. Elevated and ground level sources, continuous and instantaneous emissions, time varying sources, plumes with initial momentum (horizontal, vertical or oblique in any direction), plumes without initial momentum are considered. MicroSpray is part of the model system MSS, which also includes the diagnostic MicroSwift model for the reconstruction of the 3-D wind field in presence of obstacles and orography. To evaluate the MSS ability to simulate the dispersion of heavy gases, its simulation performances are compared in detail to two field experiments (Thorney Island and Kit Fox) and to a chlorine railway accident (Macdona). Then, a comprehensive analysis considering several experiments of the Modelers Data Archive is presented. The statistical analysis on the overall available data reveals that the performance of the new MicroSpray version for dense gas releases is generally reliable. For instance, the agreement between concentration predictions and observations is within a factor of two in the 72% up to 99% of the occurrences for the case studies considered. The values of other performance measures, such as correlation coefficient, geometric mean bias and geometric variance, mostly set in the ranges indicated as good-model performances in the specialized literature.     

Monte Carlo simulation of nitrogen oxides dispersion from a vehicular exhaust plume and its sensitivity studies

The pollutant dispersion behavior from the vehicular exhaust plume has a direct impact on human health, particularly to the drivers, bicyclists, motorcyclists, pedestrians, people working nearby and vehicle passengers. A two-dimensional pollutant dispersion numerical model was developed based on the joint-scalar probability density function (PDF) approach coupled with a k–ε turbulence model to simulate the initial dispersion process of nitrogen oxides, temperature and flow velocity distributions from a vehicular exhaust plume. A Monte Carlo algorithm was used to solve the PDF transport equations in order to obtain the dispersion distribution of nitrogen oxides concentration. The model was then validated by a series of sensitivity experimental studies in order to assess the effects of vehicular exhaust tailpipe velocities, wind speeds and chemistry on the initial dispersion of NO and NO₂ mass concentrations from the vehicular exhaust plume. The results show that the mass concentrations of nitrogen oxides decrease along the centerline of the vehicular exhaust plume in the downstream distance. The dispersion process can be enhanced when the vehicular exhaust tailpipe velocity is much larger than the wind speed. The oxidation reaction of NO plays an important role when the wind speed is large and the vehicular exhaust exit velocity is small, which leads to chemical reduction of NO, and the formation and accumulation of NO₂ in the exhaust plume. It is also found that the effect of vehicular exhaust-induced turbulence in the vicinity of the exhaust tailpipe exit is more dominant than the effect of wind turbulence, while the wind turbulence gradually shows a significant role for the dispersion of nitrogen oxides along with the development of exhaust plume. The range of dispersion of nitrogen oxides in the radial direction is increased along with the development of vehicular exhaust plume.     

SO2 concentrations near tall stacks

A study was conducted to investigate plume dispersion during convective (stability class A) conditions. The purpose of the study was to determine if high concentrations occur near sources (1.2–1.8 km) with tall stacks and to identify the plume behavior during these episodes. The study was conducted at the Tennessee Valley Authority's Paradise Steam Plant.The highest concentrations were measured near the source during wind shear capping conditions, which normally correspond to stability class B or C conditions. The measured data are compared with results obtained using a convective boundary layer model and a steady-state Gaussian model.     

Influence of temporally variable groundwater flow conditions on point measurements and contaminant mass flux estimations

Monitoring of contaminant concentrations, e.g., for the estimation of mass discharge or contaminant degradation rates, often is based on point measurements at observation wells. In addition to the problem, that point measurements may not be spatially representative, a further complication may arise due to the temporal dynamics of groundwater flow, which may cause a concentration measurement to be not temporally representative. This paper presents results from a numerical modeling study focusing on temporal variations of the groundwater flow direction. “Measurements” are obtained from point information representing observation wells installed along control planes using different well frequencies and configurations. Results of the scenario simulations show that temporally variable flow conditions can lead to significant temporal fluctuations of the concentration and thus are a substantial source of uncertainty for point measurements. Temporal variation of point concentration measurements may be as high as the average concentration determined, especially near the plume fringe, even when assuming a homogeneous distribution of the hydraulic conductivity. If a heterogeneous hydraulic conductivity field is present, the concentration variability due to a fluctuating groundwater flow direction varies significantly within the control plane and between the different realizations. Determination of contaminant mass fluxes is also influenced by the temporal variability of the concentration measurement, especially for large spacings of the observation wells. Passive dosimeter sampling is found to be appropriate for evaluating the stationarity of contaminant plumes as well as for estimating average concentrations over time when the plume has fully developed. Representative sampling has to be performed over several periods of groundwater flow fluctuation. For the determination of mass fluxes at heterogeneous sites, however, local fluxes, which may vary considerably along a control plane, have to be accounted for. Here, dosimeter sampling in combination with time integrated local water flux measurements can improve mass flux estimates under dynamic flow conditions.     

A Review of Techniques Available for Estimating Short-Term NO2 concentrations

The Environmental Protection Agency is reviewing the need for a short-term NO₂ standard based on an averaging time of three hours or less. State Implementation plans and New Source Reviews will require air quality simulation techniques capable of estimating ambient NO₂ concentrations. There is a need for multi-source (urban) models and for point source models.

A review of currently available techniques for the estimation of NO₂ concentrations resulting from NO_x point sources is presented. The available methods include simple screening techniques and refined reactive plume models. The screening techniques first use a standard gaussian dispersion model to estimate the maximum 1 hr NO_x concentration caused by the source. The second step involves estimating the fraction of this NO* concentration occurring as NO₂.

Reactive plume models numerically simulate the simultaneous effects of dispersion and chemistry on NO₂ concentrations. Organic as well as inorganic reactions are incorporated. Reactive plume models should be used, where screening techniques indicate the potential for violation of the NO₂ standard.

Current generation reactive plume models neglect the effect of turbulent concentration fluctuation on NO₂ formation and use inappropriately large dispersion coefficients to estimate plume concentrations. Approaches being developed to resolve these problems are discussed.