Plume model pollution concentration

The initial direction of transport of pollutants from their source is determined by the wind direction at the source. Air pollutant concentrations from point sources are probably more sensitive to wind direction than any other parameter. If the wind is blowing directly toward a receptor (a location receiving transported pollutants), a shift in direction of as little as 5° (the approximate accuracy of a wind direction measurement) causes concentrations at the receptor to drop about 10% under unstable conditions, about 50% under neutral conditions, and about 90% under stable conditions. The direction of plume transport is very important in source impact assessment where there are sensitive receptors or two or more sources and in trying to assess the performance of a model through comparison of measured air quality with model estimates. 

Air Pollution Dispersion Application of air dispersion modeling principles and EPA tools to assessing environmental impacts from stack and area releases of pollutants Dispersion theory Gaussian plume model Ground-level concentrations Worst case scenarios Air quality impact assessments Stationary source emissions... 

The earliest and still widely used dispersion model to compute pollutant concentration profiles is the Gaussian plume model for single or multiple source pollution problems. Box-type model techniques, which can take into account nonlinear interactions among different species arising from chemical reactions, have been used in longer-range dispersion computations. 

These indicator covariance models allow differentiation of the spatial correlation of high-valued concentrations (cut-off high) and low to background-valued concentrations low). In the particular case study underlying the Figure 3, it was found that high value concentration data were more spatially correlated (due to the plume of pollution) than lower value data. 

One commonly used suite of models that is based on Gaussian plume modeling is the Industrial Source Complex (ISC) Dispersion Models (US EPA, 1995). This suite includes both a short-term model (ISCST), which calculates the hourly air pollutant concentrations in an area surrounding a source, as well as a long-term model (ISCLT), which calculates the average air pollutant concentrations over a year or longer. ISCLT uses meteorological data summarized by frequency for 16 radial sectors (22.5° each) this data format is referred to as a stability array (STAR). Within each sector of STAR, joint frequencies of wind direction, wind speed, and atmospheric stability class are provided. 

The General Structure of Multiple-Source Plume Models Multiple-source plume (and particularly Gaussian plume) models (Calder 1977) are commonly used for predicting concentrations of inert pollutants over urban areas. Although there are many special-puipose computational algorithms currently in use, the basic element that is common to most is the single-point-source release. The spatial concentration distribution from such a source is the underlying component and the multiple-source model is then developed by simple superposition of the individual plumes from each of the sources. 

It must not be forgotten that a prime aim of reduction of lead emissions is to limit the exposure of the general population by reducing ambient concentrations of lead around the works. According to the Gaussian plume model, the ground-level concentration of a pollutant emitted from a point source (x) is given by... 

Sources of pollutants are commonly divided into two categories point sources and nonpoint sources. Point sources of pollution refer to discrete, localized, and often readily measurable discharges of chemicals. Examples of point sources are industrial outfall pipes, treated sewage outfalls from wastewater treatment plants, and untreated storm water discharge pipes. A spill of chemicals, due fo an accident on or near a surface water body, can also be regarded as a point source because its initial areal extent is limited. Point sources are often modeled by "plume" or "cloud" models that explicitly consider where and when a chemical release occurs, and then mathematically model the concentration of the chemical as a function of time and location. 

The horizontal dispersion of a plume has been modeled by the use of expanding cells well mixed vertically, with the chemistry calculated for each cell (31). The resulting simulation of transformation of NO to NO2 in a power plant plume by infusion of atmospheric ozone is a peaked distribution of NO2 that resembles a plume of the primary pollutants, SO2 and NO. The ozone distribution shows depletion across the plume, with maximum depletion in the center at 20 min travel time from the source, but relatively uniform ozone concentrations back to initial levels at travel distances 1 h from the source. 

In Gaussian plume computations the change in wind velocity with height is a function both of the terrain and of the time of day. We model the air flow as turbulent flow, with turbulence represented by eddy motion. The effect of eddy motion is important in diluting concentrations of pollutants. If a parcel of air is displaced from one level to another, it can carry momentum and thermal energy with it. It also carries whatever has been placed in it from pollution sources. Eddies exist in different sizes in the atmosphere, and these turbulent eddies are most effective in dispersing the plume. 

One major item remains before we can apply the dispersion methodology to elevated emission sources, namely plume height elevation or rise. Once the plume rise has been determined, diffusion analyses based on the classical Gaussian diffusion model may be used to determine the ground-level concentration of the pollutant. Comparison with the applicable standards may then be made to demonstrate compliance with a legal discharge standard. 

Fumes and vapors discharged to the environment via a chimney form a plume, which is approximately cone shaped. Mathematical modeling of dispersal rates is possible. The Gaussian dispersion model is commonly used to calculate the concentration of pollutants at coordinate positions X, Y and Z. (The coordinates are measured from the plume centerline.) The equation used is ... 

Important issues in groundwater model validation are the estimation of the aquifer physical properties, the estimation of the pollutant diffusion and decay coefficient. The aquifer properties are obtained via flow model calibration (i.e., parameter estimation see Bear, 20), and by employing various mathematical techniques such as kriging. The other parameters are obtained by comparing model output (i.e., predicted concentrations) to field measurements a quite difficult task, because clear contaminant plume shapes do not always exist in real life. 

Gifford, F. A. (1960). Peak to average concentration ratios according to a fluctuating plume dispersion model. Int. J. Air Pollut. 3, 253-260. 

In another review, Hoffert discussed the social motivations for modeling air quality for predictive purposes and elucidated the components of a model. Meteorologic factors were summarized in terms of windfields and atmospheric stability as they are traditionally represented mathematically. The species-balance equation was discussed, and several solutions of the equation for constant-diffusion coefficient and concentrated sources were suggested. Gaussian plume and puff results were related to the problems of developing multiple-source urban-dispersion models. Numerical solutions and box models were then considered. The review concluded with a brief outline of the atmospheric chemical effects that influence the concentration of pollutants by transformation. 

Several types of models are commonly used to describe the dispersion of atmospheric contaminants. Among these are the box, plume, and puff models. None are suitable, however, for describing the coupled transport and reaction phenomena that characterize atmospheres in which chemical reaction processes are important. Simulation models that have been proposed for the prediction of concentrations of photochemically formed pollutants in an urban airshed are reviewed here. The development of a generalized kinetic mechanism for photochemical smog suitable for inclusion in an urban airshed model, the treatment of emissions from automobiles, aircraft, power plants, and distributed sources, and the treatment of temporal and spatial variations of primary meteorological parameters are also discussed. 

Portions of the material described here are derived from a comprehensive airshed modeling program in which the authors are participating (17). This chapter focuses on urban airshed models however novel models have been proposed for urban air pollution problems of a more restricted scale— particularly, the prediction of concentrations in the vicinity of major local sources, notably freeways, airports, power plants, and refineries. In discussing plume and puff models earlier we pointed out one such class of models. Other work is the model proposed by Eschenroeder (18) to predict concentrations of inert species in the vicinity of roadways and the modeling of chemically reacting plumes, based on the Lagrangian similarity hypothesis, as presented by Friedlander and Seinfeld (19). 

Dispersion Models Based on Inert Pollutants. Atmospheric spreading of inert gaseous contaminant that is not absorbed at the ground has been described by the various Gaussian plume formulas. Many of the equations for concentration estimates originated with the work of Sutton (3). Subsequent applications of the formulas for point and line sources state the Gaussian plume as an assumption, but it has been rigorously shown to be an approximate solution to the transport equation with a constant diffusion coefficient and with certain boundary conditions (4). These restrictive conditions occur only for certain special situations in the atmosphere thus, these approximate solutions must be applied carefully. 

The smallest spatial scale at which outdoor air pollution is of concern corresponds to the air volume affected by pollutant chemical emissions from a single point source, such as a smokestack (Fig. 4-24). Chemicals are carried downwind by advection, while turbulent transport (typically modeled as Fick-ian transport) causes the chemical concentrations to become more diluted. Typically, smokestacks produce continuous pollutant emissions, instead of single pulses of pollutants thus, steady-state analysis is often appropriate. At some distance downwind, the plume of chemical pollutants disperses sufficiently to reach the ground the point at which this occurs, and the concentrations of the chemicals at this point and elsewhere, can be estimated from solutions to the advection-dispersion-reaction equation (Section 1.5), given a knowledge of the air (wind) velocity and the magnitude of Fickian transport. 

When pollution is established and its plume is mapped dynamic dispersivity may be derived by way of selecting advection-dispersion equation of mass transfer (see below) and value of 6, which produce modeled concentration distribution for tracer, adequate to a natural one. One difficulty in such determination is that the initial concentration and amoimt of tracer are often not known. 

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