Gaussian Plume Model Research Articles

Estimating the size of a methane emission point source at different scales: from local to landscape

Abstract. High methane (CH4) mixing ratios (up to 4 ppm) have occurred sporadically at our measurement site in Haddenham, Cambridgeshire, since July 2012. Isotopic measurements and back trajectories show that the source is the Waterbeach Waste Management Park 7 km SE of Haddenham. To investigate this further, measurements were made on 30 June and 1 July 2015 at other locations nearer to the source. Landfill emissions have been estimated using three different approaches at different scales; near source using the WindTrax inversion dispersion model, middle distance using a Gaussian plume (GP) model and at the landscape scale using the Numerical Atmospheric Modelling Environment (NAME) Inversion Technique for Emission Modelling (InTEM) inversion. The emission estimates derived using the WindTrax and Gaussian plume (GP) approaches agree well for the period of intense observations. Applying the Gaussian plume approach to all periods of elevated measurements seen at Haddenham produces year-round and monthly landfill emission estimates with an estimated annual emission of 11.6 Gg CH4 yr−1. The monthly emission estimates are highest in winter (2160 kg h−1 in February) and lowest in summer (620 kg h−1 in July). These data identify the effects of environmental conditions on landfill CH4 production and highlight the importance of year-round measurements to capture seasonal variability in CH4 emission.

How old is the Tasmanian cultural landscape? A test of landscape openness using quantitative land‐cover reconstructions

AbstractAimTo test competing hypotheses about the timing and extent of Holocene landscape opening using pollen‐based quantitative land‐cover estimates.LocationDove Lake, Tasmanian Wilderness World Heritage Area, Australia.MethodsFossil pollen data were incorporated into pollen dispersal models and corrected for differences in pollen productivity among key plant taxa. Mechanistic models (REVEALS—Regional Estimates of VEgetation Abundance from Large Sites) employing different models for pollen dispersal (Gaussian plume and Lagrangian stochastic models) were evaluated and applied in the Southern Hemisphere for the first time.ResultsValidation of the REVEALS model with vegetation cover data suggests an overall better performance of the Lagrangian stochastic model. Regional land‐cover estimates for forest and non‐forest plant taxa show persistent landscape openness throughout the Holocene (average landscape openness ~50%). Gymnoschoenus sphaerocephalus, an indicator of moorland vegetation, shows higher values during the early Holocene (11.7–9 ka) and declines slightly through the mid‐Holocene (9–4.5 ka) during a phase of partial landscape afforestation. Rain forest cover reduced (from ~40% to ~20%) during the period between 4.2–3.5 ka.Main conclusionsPollen percentages severely under‐represent landscape openness in western Tasmania and this bias has fostered an over‐estimation of Holocene forest cover from pollen data. Treeless vegetation dominated Holocene landscapes of the Dove Lake area, allowing us to reject models of landscape evolution that invoke late‐Holocene replacement of a rain forest‐dominated landscape by moorland. Instead, we confirm a model of Late Pleistocene inheritance of open vegetation. Rapid forest decline occurred after c. 4 ka, likely in response to regional moisture decline.

High-resolution modelling of air pollution and deposition over the Netherlands with plume, grid and hybrid modelling

We present high-resolution model results of air pollution and deposition over the Netherlands with three models, the Eulerian grid model LOTOS-EUROS, the Gaussian plume model OPS and the hybrid model LEO. The latter combines results from LOTOS-EUROS and OPS using source apportionment techniques. The hybrid modelling combines the efficiency of calculating at high-resolution around sources with the plume model, and the accuracy of taking into account long-range transport and chemistry with a Eulerian grid model. We compare calculations from all three models with measurements for the period 2009–2011 for ammonia, NOx, secondary inorganic aerosols, particulate matter (PM10) and wet deposition of acidifying and eutrophying components (ammonium, nitrate and sulfate). It is found that concentrations of ammonia, NOx and the wet deposition components are best represented by the Gaussian plume model OPS. Secondary inorganic aerosols are best modelled with the LOTOS-EUROS model, and PM10 is best described with the LEO model. Subsequently for the year 2011, PM10 concentration and reduced nitrogen dry deposition maps are presented with respectively the OPS and LEO model. Using the LEO calculations for the production of the PM10 map, yields an overall better result than using the OPS calculations for this application. This is mainly due to the fact that the spatial distribution of the secondary inorganic aerosols is better described in the LEO model than in OPS, and because more (natural induced) PM10 sources are included in LEO, i.e. the contribution to PM10 of sea-salt and wind-blown dust as calculated by the LOTOS-EUROS model. Finally, dry deposition maps of reduced nitrogen over the Netherlands are compared as calculated by respectively the OPS and LEO model. The differences between both models are overall small (±100 mol/ha) with respect to the peak values observed in the maps (>2000 mol/ha). This is due to the fact that the contribution of dry deposition of reduced nitrogen caused by emissions outside of the Netherlands is small, so effectively most of the calculated deposition in the LEO model comes from the model results from OPS.

Evaluation of Gaussian Plume Model against CFD Simulations through the Estimation of CO and NO Concentrations in an Urban Area

Dispersion of air pollutants, Carbon monoxide (CO) and Nitrogen oxide (NO), due to three point sources and a high-density traffic road in an urban area located near the center of Tokyo, Japan was studied using two different approaches. The first is an analytical approach using Gaussian Plume Model (GPM) while the second is a numerical approach using Computational Fluid Dynamics (CFD) simulations. In the first approach, the study area was divided into a fine grid and the traffic road was treated as a number of continuous adjacent stacks. The GPM was applied upon each stack and the concentration at each receptor was calculated using the principle of superposition. CFD simulations were carried out where steady state analysis was adopted and the standard k-e turbulence model was used. The concentrations calculated with the two approaches were compared together. The study results show that the agreement between the concentrations estimated by the two approaches was good in low-density built-up locations within the study area while significant deviation was obtained for high-density locations. Such result reflects the fact that the GPM is poor in predicting the actual concentrations in cases of densely urban areas.

Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures

Source term estimation for atmospheric dispersion deals with estimation of the emission strength and location of an emitting source using all available information, including site description, meteorological data, concentration observations and prior information. In this paper, Bayesian methods for source term estimation are evaluated using Prairie Grass field observations. The methods include those that require the specification of the likelihood function and those which are likelihood free, also known as approximate Bayesian computation (ABC) methods. The performances of five different likelihood functions in the former and six different distance measures in the latter case are compared for each component of the source parameter vector based on Nemenyi test over all the 68 data sets available in the Prairie Grass field experiment. Several likelihood functions and distance measures are introduced to source term estimation for the first time. Also, ABC method is improved in many aspects. Results show that discrepancy measures which refer to likelihood functions and distance measures collectively have significant influence on source estimation. There is no single winning algorithm, but these methods can be used collectively to provide more robust estimates.

Validation of Gaussian plume model Aeropol against Cabauw field experiment

The Gaussian dispersion model AEROPOL is validated against the Cabauw (1977-1978) dataset, applying the parameters and rules from the Model Validation Kit. The purpose to revisit this experiment is preparation for fast response to buoyant accidental releases. In the AEROPOL model, the classical Pasquill-Gifford stability and a scheme based on Lagrangian time scales (the Gryning scheme) are used as alternatives. Validation is based on correlation, fractional bias, fractional sigma, NMSE and fraction in factor 2, applying these statistics to maximal arc-wise, near-centreline and cross-wind integrated concentrations. Both parameterisations are found to be fairly adequate. The Gryning scheme results in moderately underestimated concentrations, which are well correlated with the experimental ones. The Pasquill parameterisation, in contrast, represents the concentrations well on average, but with larger scatter. The average wind direction and speed between the lowest measurement level and release level are found to be good approximations to represent the position of the Gaussian plume.

Validation of Gaussian plume model Aeropol against Cabauw field experiment

The Gaussian dispersion model AEROPOL is validated against the Cabauw (1977-1978) dataset, applying the parameters and rules from the Model Validation Kit. The purpose to revisit this experiment is preparation for fast response to buoyant accidental releases. In the AEROPOL model, the classical Pasquill-Gifford stability and a scheme based on Lagrangian time scales (the Gryning scheme) are used as alternatives. Validation is based on correlation, fractional bias, fractional sigma, NMSE and fraction in factor 2, applying these statistics to maximal arc-wise, near-centreline and cross-wind integrated concentrations. Both parameterisations are found to be fairly adequate. The Gryning scheme results in moderately underestimated concentrations, which are well correlated with the experimental ones. The Pasquill parameterisation, in contrast, represents the concentrations well on average, but with larger scatter. The average wind direction and speed between the lowest measurement level and release level are found to be good approximations to represent the position of the Gaussian plume.

Evaluating impacts of two-wheeler emissions on roadside air quality in the vicinity of a busy traffic intersection in Douala, Cameroon

Air quality is deteriorated due to heavy traffic in the urban centers of Cameroon in Central Africa. The Douala city has witnessed unprecedented growth of vehicles, particularly motorbikes (two-wheelers), which are the primary cause of roadside pollutants. However, the menace of pollution, due to the use of old cars over 12 years, continues. The main objective of this study has been to evaluate the impacts of two-wheelers and old cars on the pollutants originated at the Ndokoti intersection in Douala using a combine field and air quality modeling and assess the effect on urban population. The study uses a semi-empirical model to compute vehicular emission rate, the Gaussian plume model for pollutants dispersion, and the Robust Uniform World Model for assessing impacts of pollutant levels on urban population. The results show that during traffic jam, the concentrations of CO and NO2 increase with the age of cars and found to be higher with the increase in number of two-wheelers. Using models, scenarios such as a car plying together with 2, 4, 6, and 8 two-wheelers have been developed to determine the influence of the two-wheelers on the pollutant concentration. The estimated pollutant concentrations match well with the measured concentrations. The average roadside CO concentration increases from 60 (scenario 1) to 180 mg m−3 (scenario 5), indicating the prominent impact of two-wheelers on air quality. The comparison of the roadside concentrations in Douala and the guidelines of the WHO show that a large exceedance of up to about 300%, depending on the scenario.

Testing quantitative pollen dispersal models in animal-pollinated vegetation mosaics: An example from temperate Tasmania, Australia

Reconstructing past vegetation abundance and land-cover changes through time has important implications in land management and climate modelling. To date palaeovegetation reconstructions in Australia have been limited to qualitative or semi-quantitative inferences from pollen data. Testing pollen dispersal models constitutes a crucial step in developing quantitative past vegetation and land cover reconstructions. Thus far, the application of quantitative pollen dispersal models has been restricted to regions dominated by wind-pollinated plants (e.g. Europe) and their performance in a landscape dominated by animal-pollinated plant taxa is still unexplored. Here we test, for the first time in Australia, two well-known pollen dispersal models to assess their performance in the wind- and animal-pollinated vegetation mosaics of western Tasmania. We focus on a mix of wind- (6 taxa) and animal- (7 taxa) pollinated species that comprise the most common pollen types and key representatives of the dominant vegetation formations. Pollen Productivity Estimates and Relevant Source Area of Pollen obtained using Lagrangian Stochastic turbulent simulations appear to be more realistic when compared to the results from the widely used Gaussian Plume Model.

Theoretical and Experimental Study of Gaussian Plume Model in Small Scale System

Atmospheric dispersion pollution modelling is of great and actual concern in the scientific international community. Many dispersion models have been developed and used to estimate the downwind ambient concentration of air pollutants from sources such as industrial plants, vehicular traffic or accidental chemical release. Among them, Gaussian model is perhaps the most commonly used model type. It is often used to predict the dispersion of air pollution plumes originated from ground-level or elevated sources. In this research an experimental campaign was carried out in the wind tunnel of the Industrial Engineering Department of University of Catania. It was tested an emission plume of particulate matters and the concentrations of PM10 were evaluated in several points downwind beyond the emitter. Both the wind velocity and PM10 mass flow were varied in order to test the differences in terms of PM10 concentrations in the sampling points. A Gaussian plume mathematical model was developed according the boundaries conditions of the experimental campaign. The results of the model were compared with experimental ones in order to identify the limits and the advantages of this model in such a small scale system.

State of the art atmospheric dispersion modelling: should the Gaussian plume model still be used?

Abstract For regulatory purposes with respect to licensing and supervision of airborne releases of nuclear installations, the Gaussian plume model is still in use in Germany. However, for complex situations the Gaussian plume model is to be replaced by a Lagrangian particle model. Now the new EU basic safety standards for protection against the dangers arising from exposure to ionising radiation (EU BSS) [1] asks for a realistic assessment of doses to the members of the public from authorised practices. This call for a realistic assessment raises the question whether dispersion modelling with the Gaussian plume model is an adequate approach anymore or whether the use of more complex models is mandatory.

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated “artificial” observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling.

A Comparison Between Measured Concentration of 3H in Kalpakkam Environment with Predicted Atmospheric Dispersion Model.

The field measurements of 3H in the form of HTO present in air moisture carried out around Madras Atomic Power Station were compared with predicted values using atmospheric dispersion modeling. Air 3H samples were collected from different sectors at the site boundary of the operating reactors for the period of 2 y and compared with Gaussian Plume model. The predictions were comparable with the measured value. The slight variation observed between the two methods is attributed to the uncertainty involved in the measurement of air 3H concentration and in the measurement of site-specific meteorological parameters. The radiation dose imparted to members of public due to the levels observed is well within station technical specification limit for 3H.

A Methodology for Estimating the Uncertainty in Model Parameters Applying the Robust Bayesian Inferences

Background: Any real application of Bayesian inference must acknowledge that both prior distribution and likelihood function have only been specified as more or less convenient approximations to whatever the analyzer’s true belief might be. If the inferences from the Bayesian analysis are to be trusted, it is important to determine that they are robust to such variations of prior and likelihood as might also be consistent with the analyzer’s stated beliefs. Materials and Methods: The robust Bayesian inference was applied to atmospheric dispersion assessment using Gaussian plume model. The scopes of contaminations were specified as the uncertainties of distribution type and parametric variability. The probabilistic distribution of model parameters was assumed to be contaminated as the symmetric unimodal and unimodal distributions. The distribution of the sector-averaged relative concentrations was then calculated by applying the contaminated priors to the model parameters. Results and Discussion: The sector-averaged concentrations for stability class were compared by applying the symmetric unimodal and unimodal priors, respectively, as the contaminated one based on the class of e-contamination. Though e was assumed as 10%, the medians reflecting the symmetric unimodal priors were nearly approximated within 10% compared with ones reflecting the plausible ones. However, the medians reflecting the unimodal priors were approximated within 20% for a few downwind distances compared with ones reflecting the plausible ones. Conclusion: The robustness has been answered by estimating how the results of the Bayesian inferences are robust to reasonable variations of the plausible priors. From these robust inferences, it is reasonable to apply the symmetric unimodal priors for analyzing the robustness of the Bayesian inferences.

Bayesian estimation of airborne fugitive emissions using a Gaussian plume model

A new method is proposed for estimating the rate of fugitive emissions of particulate matter from multiple time-dependent sources via measurements of deposition and concentration. We cast this source inversion problem within the Bayesian framework, and use a forward model based on a Gaussian plume solution. We present three alternate models for constructing the prior distribution on the emission rates as functions of time. Next, we present an industrial case study in which our framework is applied to estimate the rate of fugitive emissions of lead particulates from a smelter in Trail, British Columbia, Canada. The Bayesian framework not only provides an approximate solution to the inverse problem, but also quantifies the uncertainty in the solution. Using this information we perform an uncertainty propagation study in order to assess the impact of the estimated sources on the area surrounding the industrial site.

Modeling air concentration of fly ash in Belgrade, emitted from thermal power plants TNTA and TNTB

Abstract The aim of the study was to estimate if dust/fly ash emitted into the atmosphere from chimneys and the ash disposal sites of the thermal power plants Nikola Tesla A and B in Obrenovac could increase the concentration of PM 10 in New Belgrade. TNTA and TNTB are close to Belgrade (population of 2.5 million) and it is important to estimate the amount of the pollution emitted into the atmosphere from these sources. The pollution from chimneys is estimated from yearly amounts of discharge, while the lifting of ash/coal dust was parameterized by the model. The used model is the straight-line Gaussian plume model written in the Fortran programming language. The first estimation was done using mathematical modeling for the idealized situation with prescribed winds and stability. The second estimation was done using the observed meteorological data for the whole year of 2009. With strong winds (over 40 km/h), dust will reach Belgrade in dozens of minutes, while during moderate winds (∼10–30 km/h), it would take about one hour to reach it. In these cases atmosphere is close to the neutral stability class. In case of weaker winds and stable atmosphere, the increase of air dust concentration in Belgrade would start after a few hours (6–10). Regarding the other two sources of pollution, coal handling piles and ash deposit sites, during strong winds (>40 km/h) and neutral stability, fly ash would reach Belgrade in several dozen of minutes.

Radiological Assessment of Atmospheric Release from Tehran Research Reactor during Normal Operation by Using Pc-Cream Code

In this work, radiological assessment of atmospheric release from Tehran’s Research Reactor (TRR) stack and assessment of public exposures under normal operation has been studied. To perform tasks mentioned above, Pc-Cream computer code which simulates Gaussian Dispersion air transport plume model as well as laboratory analysis of the soil and leaves samples and TLD (Thermo Luminescent Dosimeter) monitoring around the TRR site was used. Results of the Pc-Cream code showed that the annual committed and external dose received by the individual in the vicinity of the reactor is below the regulatory limit. Also, the results of laboratory analysis of available radionuclides in the soil and leaves samples showed that the concentrations are close to the background (K40=635, Th232=28, Cs137=0.29 up to 28.82, Ra226=25 (Bq[1]/Kg) in soil and K40=457, Be7≈70 (Bq/Kg) in leaves) and confirm the code results. The monitored dose values of the TLD detectors were positioned around the reactor within 500 m radius shows that the background dose in vicinity of TRR (113 μSv up to 150 μSv) is consistent with the background dose in Tehran province (125 μSv).

Radiological Assessment of Atmospheric Release from Tehran Research Reactor during Normal Operation by Using Pc-Cream Code

In this work, radiological assessment of atmospheric release from Tehran’s Research Reactor (TRR) stack and assessment of public exposures under normal operation has been studied. To perform tasks mentioned above, Pc-Cream computer code which simulates Gaussian Dispersion air transport plume model as well as laboratory analysis of the soil and leaves samples and TLD (Thermo Luminescent Dosimeter) monitoring around the TRR site was used. Results of the Pc-Cream code showed that the annual committed and external dose received by the individual in the vicinity of the reactor is below the regulatory limit. Also, the results of laboratory analysis of available radionuclides in the soil and leaves samples showed that the concentrations are close to the background (K40=635, Th232=28, Cs137=0.29 up to 28.82, Ra226=25 (Bq[1]/Kg) in soil and K40=457, Be7≈70 (Bq/Kg) in leaves) and confirm the code results. The monitored dose values of the TLD detectors were positioned around the reactor within 500 m radius shows that the background dose in vicinity of TRR (113 μSv up to 150 μSv) is consistent with the background dose in Tehran province (125 μSv).

Using Atmospheric Dispersion Theory to Inform the Design of a Short-lived Radioactive Particle Release Experiment.

Atmospheric dispersion theory can be used to predict ground deposition of particulates downwind of a radionuclide release. This paper uses standard formulations found in Gaussian plume models to inform the design of an experimental release of short-lived radioactive particles into the atmosphere. Specifically, a source depletion algorithm is used to determine the optimum particle size and release height that maximizes the near-field deposition while minimizing both the required source activity and the fraction of activity lost to long-distance transport. The purpose of the release is to provide a realistic deposition pattern that might be observed downwind of a small-scale vent from an underground nuclear explosion. The deposition field will be used, in part, to study several techniques of gamma radiation survey and spectrometry that could be used by an On-Site Inspection team investigating such an event.

Modeling and Evaluation of Dispersion Parameters For Odors From Agricultural Sources

This article presents an evaluation of four models that are widely used in developing Gaussian plume models, based on the atmospheric stability class and the Pasquill-Gifford curves. The four statistics used to evaluate the models are the sum of residuals, the minimized sum of the squared residuals, the correlation coefficient, and the estimated error standard deviation. Evaluation shows that all of the four models have problems in fitting the Pasquill-Gifford data when the downwind distance increases from the source. A new model has been developed which fits the Pasquill-Gifford data more effectively than the previously developed models studied. INTRODUCTION The Gaussian plume model is the most common air pollution model. It has been used extensively in the atmospheric sciences to predict atmospheric dif­ fusion [1-7] as well as in agricultural engineering for odor emission problems [8-11]. However, a correct calculation of the dispersion parameters ay and σζ in the Gaussian plume model is necessary. The quantities oy and σζ represent the crosswind and vertical standard deviations of the dispersing plume respec­ tively, and are functions of the atmospheric stability class and the downwind 27 © 1998, Baywood Publishing Co., Inc. doi: 10.2190/6CKR-LJRB-UMAD-J7UC http://baywood.com 28 / CHEN, HOFF AND BUNDY distance of the receptor from a source. Many models of the dispersion parameters have been developed [12-15]. These models are widely used in developing Gaussian plume models. The following questions arise for research in agricultural odor dispersion: Which model is the best to use and how should the prediction models be compared if the models for dispersion parameters are different? The objectives of this research were 1) to evaluate four models using the dispersion parameters ay and σζ based on the atmospheric stability class and the Pasquill-Gifford curves, and 2) to develop a new model, which is simple in form and fits the Pasquill-Gifford curves more effectively. ATMOSPHERIC STABILITY AND PASQUILL-GIFFORD CURVES One of the major meteorological factors that affect odor pollution phenomena is atmospheric stability. Atmospheric stability categorizes the turbulent status of the atmosphere and affects the dilution rate of odor. Pasquill characterized atmo­ spheric stability using six classes based on the incoming solar radiation, the cloud amount at night, and the wind speed: A, very unstable; B, unstable; C, slightly unstable; D, neutral; E, slightly stable; F, stable (Table 1) [16]. The Pasquill-Gifford curves are families of curves of ay and σζ as functions of the atmospheric stability class and the downwind distance (see Figures 1 and 2). The curves are used as the base reference to evaluate four models of the disper­ sion parameters ay and σζ because they are the most used formulation for U.S. EPA regulatory modeling applications [5]. Table 1. Stability Categories [3]