Modelling dispersion of pollutants with a simple software - it is a practical approach K. Oduyemi University of Abertay Dundee, Bell Street, Dundee DD1 1HG, UK Abstract One of the most important assessment in EIA studies of incineration facilities is how a pollutant disperses downwind from a source. A range of solutions exist for this problem, but the question is whether for practical engineering purpose one needs an advanced model to predict accurately enough. A recently developed Gaussian dispersion model, with its known limitations, was used for long-term prediction of pollutant concentrations downwind from a process emission. This was performed by using building blocks of 1 hour wind magnitude and direction data, day solar insolation and night cloud cover data and process emission data. The predicted results have been compared with results from a SCREEN (a commercially available software) modelling exercise and with UK urban area air quality data. The comparisons were very reasonable and show that a simple modelling approach may be justified for some practical engineering problems. Introduction This paper gives details of the development, application and preliminary verification of plume rise and dispersion models of air-borne contaminants. In recent years there has been an increasing interest in numerically modelling the concentration of air-borne contaminants in the atmospheric environment, particularly in view of the increasing confidence and reliability of dispersion models and the scaling difficulties encountered in modelling contaminant concentration using traditional hydraulic models. Furthermore, there has been an increasing interest in the need to predict contaminant concentration in the atmosphere due to proposals like the siting of incinerators in a city; the growing environmental concerns regarding the concentration level of contaminants in the vicinity of hospital and energy-recycling incinerators
26 Computer Techniques in Environmental Studies have commonly been dealt with in Environmental Impact Assessment (EIA) studies (eg. Wimpey Environmental Ltd. (1993) ). A range of solutions exist for modelling the dispersion of contaminants in the atmosphere, from a simple modelling approach to an advanced approach. However, a major obstacle in the development of an advanced mathematical model for predicting concentration of contaminants in the vicinity of a process emission source has been the paucity of field data available for calibrating and verifying the model. Most of the advanced mathematical models for predicting concentration of contaminants in the atmosphere do not have universal acceptance and some of them are not easy to use. By far the greatest problem is that associated with not allowing comparison between the estimates by different modellers in varying situations. The question that is often asked by environmentalists is whether for practical engineering purpose one needs an advanced model to predict accurately enough. Often, in practice, there is time limitation for the development of a numerical model and hence the approach that is adopted will more than likely depend on a compromise between accuracy, project cost, ease of use of software, data availability for calibration and verification of software, and time available for software development. The trade-off will depend on the reality of the problem in hand. A recently developed Gaussian dispersion model was used for long-term prediction of contaminant concentrations downwind from a process emission in the study described in this report. Equations for Plume Rise and Contaminant Dispersal Models The Gaussian point-source dispersion equation relates average steady-state pollutant concentrations to the source strength, wind speed, effective stack height, and atmospheric conditions. Its form can be derived from basic considerations involving gaseous diffusion in threedimensional space. The following assumptions are incorporated into any analysis utilising the Gaussian point-source dispersion equation: a) The rate of emissions from the source is constant.
Computer Techniques in Environmental Studies 27 b) The wind speed is constant both in time and with elevation. c) The pollutant is conservative. d) The terrain is relatively flat, open country. A three-dimensional co-ordinate system with the process emission source (eg. Chimney) at the origin is usually established for the modelling, with distance directly downwind given by x, transverse distance from the centreline is given by y and elevation given by z. The environmental concern is mainly with the receptors (people and ecosystem) at ground level. Hence, the Gaussian plume equation used in this study were outputed at z=0. At z=0, the general Gaussian plume equation (Engineering and the Environment, 1984) reduces to: C(x,y) = (Q/(3.14*U*Py*Pz) y2/(2*p2))) (1) where C(x,y) = concentration at ground level at point (x,y) x = distance directly downwind y = distance in the transverse direction from the plume centreline Q = emission rate of pollutants H = effective stack height = h + DELH, where h = actual stack height, and DELH = plume rise U = average wind speed at the effective height of the stack Py = horizontal dispersion coefficient P% = vertical dispersion coefficient The two dispersion coefficients, P and P are just the standard deviations of the horizontal and vertical Gaussian distributions, respectively. Smaller values of a dispersion coefficient mean the Gaussian curve is narrower, with a higher peak, while larger values mean the opposite. The further downwind one is from the source, the larger these coefficients become. This causes the Gaussian curves to spread further and further. These coefficients not only depend on downwind distance, they also depend, in a complex manner, on atmospheric stability. The most common procedure for estimating the dispersion coefficients was introduced by Pasquill (1961), modified by Gifford (1961), and adopted by the U.S. Public Health
28 Computer Techniques in Environmental Studies Service (Turner, 1970). The parameters A to F that are used represent stability classifications based on qualitative descriptions of prevailing environmental conditions. The difference between the actual stack height h and the effective height H is called the plume rise DELH. Plume rise is caused by a combination of factors, the most important ones being the buoyancy and momentum of the exhaust gases, and the stability of the atmosphere itself. Buoyancy results when exhaust gases are hotter than the ambient air. Momentum is caused by the mass and velocity of the gases as they leave the stack. Unfortunately, a number of techniques have been proposed in the literature for dealing with plume rise, and they tend to give different results. The U.S. Environmental Protection Agency (EPA) recommends a model based on work by Briggs(1972) and it has been used in the development of the model put forward in this study. Model Development and Tests The long-term prediction of ground level concentrations was performed by using 'building blocks' of 1 hour wind magnitude and direction data, day solar insolation and night cloud cover data, process emission data and Equation 1. TABLE 1 PREDICTED LONG-TERM CONCENTRATION OF NITROGEN OXIDE EMITTED TO ATMOSPHERE BY AN INCINERATION FACILITY IN COMPARISON TO THE AMBIENT AIR QUALITY Pollutant Predicted UK Urban Area Maximum Ambient Air Concentration Quality mg/m mg/m Nitrogen Oxides 0.00077 0.020-0.090 Quality of Urban Air Review Group. 'Urban Air Quality in the UK'. DoE, London, 1993.
Computer Techniques in Environmental Studies 29 The predicted result for Nitrogen Oxides, shown in Table 1 above, has been compared with measured UK ambient air data. As expected, the predicted result is very small in comparison with the normal UK urban background level. The Gaussian short-term dispersion model, from which the long-term model was developed, was tested with a EIA problem that had already been solved using SCREEN, a commercially available software. The short-term (Ihr.) ground-level concentrations for the EIA problem, from which the results from a SCREEN modelling exercise were available (Wimpey Environmental Ltd., 1993), were modelled using the author's Gaussian point source dispersion model. The results and details of the problem are presented in Table 2. The results, shown in Table 2, are approximately the same for both models. This tests further confirms the accuracy of the author's Gaussian point source model. In view of the large amount of data used for the long-term modelling several tests were performed. Firstly, the ground-level concentrations of pollutants were predicted for a specific month by two modellers, the results of which were the same. Secondly, predictions of ground-level concentrations of pollutants were performed with two years of meteorological data, with the results for both years showing good agreement. Lastly meteorological data input by one modeller were randomly checked by a second modeller for their correctness. Model Application The model was used to predict the annual ground level concentration of air-borne contaminants for a proposed process emission source. The results, shown in Fig. 1, have allowed for an assessment of the impact of the airborne contaminants in the vicinity of the proposed process emission source. Concluding Remarks The Gaussian plume equation is a simple model. Predictions based on the model in this study have been shown to be, at least, correct for the application discussed in this report and have compared reasonably well with commercially
30 Computer Techniques in Environmental Studies available software. Furthermore, Gaussian model is universally acceptable and easy to use. The quality assurance for the data input was easy to undertake. The comparison with measured UK air quality data was reasonable and shows that a simple modelling approach may be justified for some practical engineering problems. References Briggs, GA, 1972, Discussion of chimney plumes in neutral and stable surroundings, Atmospheric Environment 6(1). Wanielista, MP, et al., 1984, Engineering environment, Wadsworth Inc., California. and the Gifford, FA, 1961, Uses of routine meteorological observations for estimating atmospheric dispersion, Nuclear Safety 2(4). Pasquill, F, 1961, The estimation of the dispersion of windbome material, Meteorological Magazine 90:1063. Turner, DB, 1970, Workbook of atmospheric dispersion estimates, U.S. Environmental Protection Agency, Washington, DC. Wimpey Environmental Ltd., 1993, Environmental statement in support of a tyre recycling facility, East Kilbride (Ref. No. EPP2148M), Scotland, UK.
Computer Techniques in Environmental Studies 31 2.0 1.6- I.2-0.8-0.4- -0.0-0.0 X AXIS *10 Y AXIS *10 i 0.4 \ Fig.l Ground Plot of Plume Dispersal Model Characteristics - Pollutant Concentration for 1 g/s Emission Rate X (X*10 and Y*10 in Km, Source at (lokm, lokm)) (Contour Height * 10'3 in microgramme/cubic metre) r 0.8 1.2 1.6 2.0 CONTOUR HEIGHT *10-3
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