The Interference of Higher-Order Statistics of the Concentration Field Produced by Two Point Sources According to a Generalized Fluctuating Plume Model

The higher-order correlation functions for the concentrationfluctuations arising from a two-point-source configuration have beencalculated analytically within the context of the phenomenology of afluctuating plume model (viz., a meandering plume model that explicitlyincorporates internal fluctuations). Explicit expressions for thesecond-, third-, and fourth-order correlationfunctions between the concentrationfluctuations produced by two point sources are given in terms of the sourceseparation d and the five physically based parameters that define thegeneralized fluctuating plume model: namely, the absolute plume dispersion,_a, which determines the outer plume length scale; the relative plume dispersion, _r, which determines the inner plume length scale; the fluctuation intensity, i_r, in relative coordinates, which determines the internal concentration fluctuation level; the correlation coefficient, r,between the positions of the centroids of the two interfering plumes; and,the correlation coefficient, r^*, between the concentration fluctuationsof the two plumes in relative coordinates, which determines the degree ofinternal mixing of the two scalars. Furthermore, the form of the totalconcentration probability density function arising from the interferenceproduced by two point sources is presented. Predictions for the second-ordercorrelation function, , and for the total concentration probabilitydensity function have been compared with some new experimental data fora two-point-source configuration in grid turbulence generated in awater-channel simulation. These results are in good agreement with the dataand suggest that the analytical model for the second-order correlationfunction and the total concentration probability density function canreproduce many qualitative trends in the interaction of plumes from twosources.     

Statistical characteristics of concentration fluctuations in dispersing plumes in the atmospheric surface layer

Measurements have been made of concentration fluctuations in a dispersing plume from an elevated point source in the atmospheric surface layer using a recently developed fast-response photoionization detector. This detector, which has a frequency response (–6 dB point) of about 100 Hz, is shown to be capable of resolving the fluctuation variance contributed by the energetic subrange and most of the inertial-convective subrange, with a reduction in the fluctuation variance due to instrument smoothing of the finest scales present in the plume of at most 4%.Concentration time series have been analyzed to obtain the statistical characteristics of both the amplitude and temporal structure of the dispersing plume. We present alongwind and crosswind concentration fluctuation profiles of statistics of amplitude structure such as total and conditional fluctuation intensity, skewness and kurtosis, and of temporal structure such as intermittency factor, burst frequency, and mean burst persistence time. Comparisons of empirical concentration probability distributions with a number of model distributions show that our near-neutral data are best represented by the lognormal distribution at shorter ranges, where both plume meandering and fine-scale in-plume mixing are equally important (turbulent-convective regime), and by the gamma distribution at longer ranges, where internal structure or spottiness is becoming dominant (turbulent-diffusive regime). The gamma distribution provides the best model of the concentration pdf over all downwind fetches for data measured under stable stratification. A physical model is developed to explain the mechanism-induced probabilistic schemes in the alongwind development of a dispersing plume, that lead to the observed probability distributions of concentration. Probability distributions of concentration burst length and burst return period have been extracted and are shown to be modelled well with a powerlaw distribution. Power spectra of concentration fluctuations are presented. These spectra exhibit a significant inertial-convective subrange, with the frequency at the spectral peak decreasing with increasing downwind fetch. The Kolmogorov constant for the inertial-convective subrange has been determined from the measured spectra to be 0.17±0.03.     

Concentration fluctuation measurements in a plume dispersing in a stable surface layer

A set of tracer experiments studying concentration fluctuations in a pollutant plume dispersing near the surface in a stably stratified nocturnal boundary layer is described, and the results are compared with those obtained in near-neutral stability conditions by Mylne and Mason (1991). The results highlight the importance of slow meandering of the plume which is characteristic of stable conditions. This meandering makes it impossible to conduct experiments under near-stationary conditions, resulting in considerable statistical variability in the results, but is important in reducing time-averaged concentrations. Spectral characteristics of the plume and general fluctuation statistics are qualitatively similar to those in near-neutral stability, but there are significant quantitative differences. Fluctuation time scales are shown to be substantially longer under stable conditions. This difference cannot be fully explained by the reduced windspeed alone, indicating that the length scale of plume elements is also longer. Some of the differences observed in stable conditions, particularly the longer time scales, are shown to substantially increase the potential hazard due to fluctuations in practical applications. A conceptual model of plume dispersion is described, which explains the observed plume structure under different conditions by relating it to the turbulent velocity spectra.     

The effects of source size on concentration fluctuations in plumes

The effects of source size on plume behaviour have been examined in a 1.2 m wind tunnel boundary layer for isokinetic sources with diameters from 3 to 35 mm at source heights of 230 mm and at ground level. Experimental measurements of mean concentration and the variance, intermittency and probability density functions of the concentration fluctuations were obtained. In addition, a fluctuating Gaussian plume model is presented which reproduces many of the observed features of the elevated emission. The mean plume width becomes independent of source size much more rapidly than the instantaneous plume width. Since it is the meandering of the instantaneous plume which generates most of the concentration fluctuations near the source, these are also dependent on source size. The flux of variance in the plume reaches a maximum, whose value is greatest for the smallest source size, close to the source and thereafter is monotonically decreasing. The intermittency factor reaches a minimum, whose value is lowest for the smallest source, and increases back towards one. Concentration fluctuations for the ground-level source are much less dependent on source size due to the effects of the surface.     

Predicting plume meandering and averaging time effects on mean and fluctuating concentrations in atmospheric dispersion simulated in a water channel

Plume meandering and averaging time effects were measured directly using a high spatial resolution, high frequency, linescan laser-induced fluorescence (LIF) technique for measuring scalar concentrations in a plume dispersing in a water channel. Post-processing of the collected data removed time dependent background dye levels and corrected for attenuation across the laser beam to produce accurate measurements over long sample times in both a rough surface boundary-layer shear flow and shear free grid-generated turbulent flow. The data were used to verify the applicability of a meandering plume model for predicting the properties of mean and fluctuating concentrations. The centroid position of the crosswind concentration profile was found to have a Gaussian probability density function and the instantaneous plume spread about the centroid fluctuated log-normally. A modified travel-time power law model for averaging time adjustment was developed and compared to the widely used, but much less accurate, 0.2 power-law model.     

Effect of Vertical Wind Shear on Concentration Fluctuation Statistics in a Point Source Plume

Measurements of concentration fluctuation intensity, intermittency factor, and integral time scale were made in a water channel for a plume dispersing in a well-developed, rough surface, neutrally stable, boundary layer, and in grid-generated turbulence with no mean velocity shear. The water-channel simulations apply to full-scale atmospheric plumes with very short averaging times, on the order of 1–4 min, because plume meandering was suppressed by the water-channel side walls. High spatial and temporal resolution vertical and crosswind profiles of fluctuations in the plume were obtained using a linescan camera laser-induced dye tracer fluorescence technique. A semi-empirical algebraic mean velocity shear history model was developed to predict these concentration statistics. This shear history concentration fluctuation model requires only a minimal set of parameters to be known: atmospheric stability, surface roughness, vertical velocity profile, and vertical and crosswind plume spreads. The universal shear history parameter used was the mean velocity shear normalized by surface friction velocity, plume travel time, and local mean wind speed. The reference height at which this non-dimensional shear history was calculated was important, because both the source and the receptor positions influence the history of particles passing through the receptor position.     

Macro- and micro-scale mixing in chemical reactive plumes

The average dispersion of a plume in the atmospheric boundary layer is strongly influenced by atmospheric turbulence. Atmospheric turbulence determines also concentration fluctuations due to turbulent meandering by large scale turbulent eddies and in-plume fluctuations, due to smaller scale eddies. Conversion of NO to NO₂ in a plume is influenced by micro-scale mixing, due to the concentration fluctuation correlation % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaaca% qGobGaae4tamaaCaaaleqabaGaaeymaaaakiaab+eadaqhaaWcbaGa% ae4maaqaaiaabgdaaaaaaaaa!3AF4!\[\overline {{\rm{NO}}^{\rm{1}} {\rm{O}}_{\rm{3}}^{\rm{1}} } \] and macro-scale mixing, the mixing in of ambient air containing O₃ into the plume.The study of turbulent meandering, in-plume fluctuations, microscale and macro-scale mixing will contribute to a better understanding of concentration fluctuations in general.     

The exponential probability density function and concentration fluctuations in smoke plumes

Observations of 1-s average concentration fluctuations during two trials of a U.S. Army diffusion experiment are presented and compared with model predictions based on an exponential probability density function (pdf). The source is near the surface and concentration monitors are on lines about 30 to 100 m downwind of the source. The observed ratio of the standard deviation to the mean of the concentration fluctuations is about 1.3 on the mean plume axis and 4 to 5 on the mean plume edges. Plume intermittency (fraction of non-zero readings) is about 50%; on the mean plume axis and 10%; on the mean plume edges. A meandering plume model is combined with an exponential pdf assumption to produce predictions of the intermittency and the standard deviation of the concentration fluctuations that are within 20%; of the observations.     

Micromixing modelling of concentration fluctuations in inhomogeneous turbulence in the convective boundary layer

The micromixing technique, widely used in engineering calculations of mixing and chemical reaction, is extended to atmospheric boundary-layer flows. In particular, a model based on the interaction-by-exchange-with-the-conditional-mean (IECM) micromixing approach is formulated to calculate concentration fluctuation statistics for a line source and a point source in inhomogeneous and non-Gaussian turbulence in the convective boundary layer. The mixing time scale is parameterised as a linear function of time with the intercept value determined by the source size at small times. Good agreement with laboratory data for the intensity of concentration fluctuations is obtained with a value of 0.9 for the coefficient of the linear term in the time-scale parameterisation for a line source, and a value of 0.6 for a point source. Calculation of higher-order moments of the concentration field for a line source shows that non-Gaussian effects persist into the vertically well-mixed region. The cumulative distribution function predicted by the model for a point source agrees reasonably well with laboratory data, especially in the far field. In the limit of zero mixing time scale, the model reduces to a meandering plume model, thus enabling the concentration variance to be partitioned into meandering and relative components. The meandering component is shown to be more persistent for a point source than for a line source.     

Probability distributions of concentration fluctuations of a weakly diffusive passive plume in a turbulent boundary layer

Results are presented from an experimental investigation of turbulent dispersion of a saline plume of large Schmidt number (Sc=830) in a turbulent boundary-layer shear flow simulated in a laboratory water channel. The dispersion measurements are obtained in a neutrally buoyant plume from an elevated point source over a range of downstream distances, where both plume meandering and fine-structure variations in the instantaneous plume are important. High-resolution measurements of the scalar fluctuations in the plume are made with a rake of conductivity probes from which probability distributions of concentration at various points throught the plume are extracted from the time series.Seven candidate probability distributions were tested, namely, the exponential, lognormal, clipped normal, gamma, Weibull, conjugate beta, andK-distributions. Using the measured values of the conditional mean concentration, , and the conditional fluctuation intensity,i _p , the Weibull distribution provided the best match to the skewness and kurtosis over all downstream fetches. The skewness and kurtosis were always overpredicted by the lognormal probability density function (pdf), and underpredicted by the gamma pdf. The conjugate beta distribution for which the model parameters are determined using a method of moments based on the fluctuation intensity,i _p , and skewness,S _p , was capable of modeling the distribution of scalar concentration over a wide range of positions in the plume.     

An Analytical Model For Threshold Crossing Rates Of Concentration Fluctuations In Dispersing Plumes

A simple analytical model is developed for the meanupcrossing rate of plume concentration fluctuations assuming that thisprocess can be well approximated by a lognormal process. The resultingexpression requires only the specification of the in-plume fluctuationintensity and in-plume Taylor micro-time scale and, hence, does notexplicitly involve the joint probability density function of theconcentration and its derivative. The analytical model provides agood fit to some field measurements of the mean upcrossing rate ina dispersing plume.     

Incorporation of internal fluctuations in a meandering plume model of concentration fluctuations

A meandering plume model that explicitly incorporates the effects of small-scale structure in the instantaneous plume has been formulated. The model requires the specification of two physically based input parameters; namely, the meander ratio,M, which is dependent on the ratio of the meandering plume dispersion to the instantaneous relative plume dispersion and, a relative in-plume fluctuation measure,k, that is related inversely to the fluctuation intensity in relative coordinates. Simple analytical expressions for crosswind profiles of the higher moments (including the important shape parameters such as fluctuation intensity, skewness, and kurtosis) and for the concentration pdf have been derived from the model. The model has been tested against some field data sets, indicating that it can reproduce many key aspects of the observed behavior of concentration fluctuations, particularly with respect to modeling the change in shape of the concentration pdf in the crosswind direction.List of Symbols C Mean concentration in absolute coordinates - C _r Mean concentration in relative coordinates - C₀ Centerline mean concentration in absolute coordinates - C _r,0 Centerline mean concentration in relative coordinates - f Probability density function of concentration in absolute coordinates - f _c Probability density function of plume centroid position - f _r Probability density function of concentration in relative coordinates - i Absolute concentration fluctuation intensity (standard deviation to mean ratio) - i _r Relative concentration fluctuation intensity (standard deviation to mean ratio) - k Relative in-plume fluctuation measure:k=1/i _r ² - K Concentration fluctuation kurtosis - M Meander ratio of meandering plume variance to relative plume variance - S Concentration fluctuation skewness - x Downwind distance from source - y Crosswind distance from mean-plume centerline - z Vertical distance above ground - Instantaneous (random) concentration - Crosswind dispersion ofnth concentration moment about zero - _ny Mean-plume crosswind (absolute) dispersion - _y Plume centroid (meandering) dispersion in crosswind direction - _y,c Instantaneous plume crosswind (relative) dispersion - Normalized mean concentration in absolute coordinates:C/C ₀ - Particular value taken on by instantaneous concentration,      

The vertical profile of concentration fluctuations in near-surface plumes

Field experiments on concentration fluctuations have frequently measured horizontal cross-sections of fluctuation statistics through plumes at fixed heights near the surface, but have not considered the effect of height above the ground in any detail. A set of tracer experiments designed to measure vertical profiles of concentration fluctuations in plumes, with a range of source heights, is described, and profiles of statistics are presented. Considerable variation of the statistics with both source and detector height is found. Near the surface, fluctuation intensity is a minimum and the time and length scales of the fluctuations are greatly increased. Profiles are consistent with the idea that concentration fluctuations near the surface are like those higher up at a greater distance from the source. Lowering the source height reduces the fluctuation intensity at all heights, and also alters the form of the concentration PDF. Results may be explained by the reduced length scale of sheargenerated turbulence near the surface causing enhanced small-scale mixing, which rapidly smooths out much of the fine structure with the plume.     

A Stochastic Time Series Model for Threshold Crossing Statistics of Concentration Fluctuations in non-Intermittent Plumes

A numerical stochastic model is developed for the upcrossing rate across a specified threshold concentration. The model assumes that the concentration time series at a given spatial point within a dispersing plume can be approximated as a first-order Markovian process designed to be consistent with a given time-invariant concentration probability density function (pdf). The model requires only the specification of a concentration pdf with a given mean and variance and a concentration fluctuation integral time scale. Predicted upcrossing rates are compared with atmospheric plume concentration data obtained from a point source near the ground. For this data set, a log-normal pdf is found to give better estimates of the threshold crossing rate than a gamma pdf.     

A wind tunnel study of mean and fluctuating concentrations in a plume dispersing over a two-dimensional hill

This paper describes an experimental investigation of the behaviour of the statistics of concentration fluctuations in a passive plume dispersing over a two-dimensional hill of moderate steepness. Recently developed high frequency response Flame Ionization Detector (FID) technology with a frequency response in excess of 200 Hz was utilized to obtain an extensive set of measurements of the mean and fluctuating plume concentrations. Plumes dispersing over flat terrain and over a hill with a maximum slope of 0.3 were studied. For both cases, extensive turbulent flow measurements were also carried out.The measured mean plume concentration profiles were of a generally Gaussian form and showed the expected effects of surface reflection for the flat terrain and hill. Plume intermittency and concentration fluctuation intensity were calculated at all measurement locations. Conditional and unconditional plume concentration statistics were calculated. The conditional (in-plume) concentrations and intensities were more uniform with height than for the unconditional ones.     

Simulating Concentration Fluctuation Time Series with Intermittent Zero Periods and Level Dependent Derivatives

Intermittent concentration fluctuation time series were produced with a stochastic numerical model derived from the assumption that the concentration fluctuations at a fixed receptor in a point-source plume can be modelled as a first order Markov process. The time derivative of concentration was assumed to be level-dependent and constrained by a stationary lognormal probability density function. The input parameters required to reconstruct the intermittent time series are the intermittency factor , the conditional fluctuation intensity i _p ² , and the time scale T _c . A clipped lognormal probability distribution was used to describe the fluctuation time series. Good agreement between the stochastic simulation and experimental water-channel data was demonstrated by comparing the time derivative of concentration and the upcrossing rates over a range of intermittency factors = 0.7 to 0.01 and fluctuation intensities i _w ² = 2.2 to 7.5.     

Concentration fluctuations according to fluctuating plume models in one and two dimensions

Concentration probability density functions (pdfs) calculated according to fluctuating plume models in one- and two-dimensions, representing the limiting cases of one-dimensional dispersion from a line source or a point source in strongly anisotropic turbulence and of axisymmetric dispersion from a point source in isotropic turbulence, are discussed and analyzed in terms of the location of the sampling point within the mean plume and of the ratio, s/m, of the standard deviations for relative dispersion and meandering.In both cases, the pdfs cover the finite concentration range from zero to C ₀, the centreline concentration of the instantaneous plume. The main difference between them is that whereas the 2-D pdf is always unimodal, the 1-D pdf has a singularity at C ₀ which under some circumstances results in a bimodal form. However, the probability associated with this singularity is not always significant. Differences of practical importance in the shape of the pdfs occur mainly for centreline or near-centreline sampling locations when meandering is not too much larger than relative dispersion (1 < m ²/s² < 10) and for sampling locations a distance of order s from the centreline when relative dispersion is not too much larger than meandering (1 < s ²/m² < 5).Comparison against wind tunnel measurements not too far downstream of a line source in grid turbulence shows that the 1-D model reproduces the essential features and trends of the measurements. Under appropriate circumstances the measurements show the bimodal pdf predicted by the 1-D model (but not by the 2-D model) confirming that the effect of the anisotropy in the source distribution is observable.Present address: School of Mechanical Engineering, Aristotle University, Thessaloniki, 54006 Thessaloniki, Greece.     

Scaling of the Vertical Spreading of a Plume of a Passive Tracer in a Rough-Wall Neutral Boundary Layer

The influence of surface roughness on the dispersion of a passive scalar in a rough wall turbulent boundary layer has been studied using wind-tunnel experiments. The surface roughness was varied using different sizes of roughness elements, and different spacings between the elements. Vertical profiles of average concentration were measured at different distances downwind of the source, and the vertical spread of the plume was computed by fitting a double Gaussian profile to the data. An estimate of the integral length scale is derived from the turbulence characteristics of the boundary layer and is then used to scale the measured values of plume spread. This scaling reduces the variability in the data, confirming the validity of the model for the Lagrangian integral time scale, but does not remove it entirely. The scaled plume spreading shows significant differences from predictions of theoretical models both in the near and in the far field. In the region immediately downwind of the source this is due to the influence of the wake of the injector for which we have developed a simple model. In the far field we explain that the differences are mainly due to the absence of large-scale motions. Finally, further downwind of the source the scaled values of plume spread fall into two distinct groups. It is suggested that the difference between the two groups may be related to the lack of dynamical similarity between the boundary-layer flows for varying surface roughness or to biased estimates of the plume spread.