AIR POLLUTION – MONITORING MODELLING AND HEALTH

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50 Air Pollution <strong>–</strong> Monitoring, Modelling and Health 12 Will-be-set-by-IN-TECH 1,0 0,8 0,6 z / h 0,4 0,2 0,0 0 50 100 150 200 250 K z instável K z (500,z) K z (1500,z) K z (2500,z) Fig. 1. The vertical eddy diffusivity K z (z, t) for three different travel times (t = 500s, 1500s, 2500s) using run 8 of the Copenhagen experiment. n = 0.1 is valid for a power low wind profile in unstable condition. Moreover, U.S.EPA suggests for rural terrain (as default values used in regulatory models) to use n = 0.15 for neutral condition (class D) and n = 0.1 for stability class C (moderately unstable condition). In Figure 1 we present a plot of K z (z, t) for three different travel times (t = 500s, 1500s, 2500s) using run 8 of the Copenhagen experiment. In order to exclude differences due to numerical uncertainties we define the numerical accuracy 10 −4 of our simulations determining the suitable number of terms of the solution series. As an eye-guide we report in table 2 on the numerical convergence of the results, considering successively one, two, three and four terms in the solution series. One observes that the desired accuracy, for the solved problem solved is attained including only four terms in the truncated series, which is valid for all distances considered. Once the number of terms in the series solution is determined numerical comparisons of the 3D-GILTT results against experimental data may be performed and are presented in table 3. Figure 2 shows the scatter plot of the centreline ground-level observed concentrations versus the simulated the 3D-GILTT model predictions, normalized by the emission rate and using two points in the time Gaussian Quadrature inversion (Moreira et al. (2006a), Stroud & Secrest (1966)). In the scatter diagram analysis, the closer the data are from the bisector line, the better are the results. The lateral lines indicate a factor of two (FA2), meaning if all the obtained data are between these lines FA2 equals to 1 (the maximum value for stochastically distributed data). From the scatter diagram (figure 2 one observes the fairly good simulation of dispersion data by the 3D-GILTT model, even for the two-point Gaussian quadrature scheme considered here.
Analytical Model for Air Pollution in the Atmospheric Boundary Layer Analytical Model for Air Pollution in the Atmospheric Boundary Layer 13 Run Recursion c(x, y, z, t) Run Recursion c(x, y, z, t) depth (10 −7 g ) m 3 0 8.68 4.49 0 3.65 2.10 1.31 1 10.45 3.66 1 7.28 2.23 1.47 1 2 10.21 3.65 6 2 7.78 2.20 1.46 3 10.21 3.65 3 7.78 2.20 1.46 4 10.21 3.65 4 7.78 2.20 1.46 5 10.21 3.65 5 7.78 2.20 1.46 0 8.62 2.01 0 5.77 3.07 2.27 1 6.60 2.30 1 6.73 3.33 1.65 2 2 7.09 2.28 7 2 6.61 3.32 1.65 3 7.09 2.28 3 6.60 3.32 1.65 4 7.09 2.28 4 6.60 3.32 1.65 5 7.09 2.28 5 6.60 3.32 1.65 0 5.58 6.38 3.72 0 6.38 3.94 2.76 1 10.76 7.97 4.51 1 6.40 4.91 1.91 3 2 9.84 7.88 4.50 8 2 5.99 4.87 1.91 3 9.79 7.88 4.50 3 5.97 4.87 1.91 4 9.79 7.88 4.50 4 5.97 4.87 1.91 5 9.80 7.88 4.50 5 5.97 4.87 1.91 0 10.73 0 5.01 2.83 2.08 1 15.32 1 5.73 3.05 2.20 4 2 15.23 9 2 5.65 3.04 2.19 3 15.24 3 5.64 3.04 2.19 4 15.24 4 5.64 3.04 2.19 5 15.24 5 5.64 3.04 2.19 0 7.14 4.11 2.47 1 9.11 4.77 3.78 5 2 6.96 4.46 3.73 3 6.65 4.47 3.73 4 6.65 4.48 3.73 5 6.68 4.48 3.73 Table 2. Pollutant concentrations for nine runs at various positions of the Copenhagen experiment and model prediction by the 3D-GILTT approach with time dependent eddy diffusivity. To perform statistical comparisons between GILTT results against Copenhagen experimental data we consider the set of statistical indices described by Hanna (1989) and defined in by • NMSE (normalized mean square error) = (C o−C p ) 2 C o C p , • COR (correlation coefficient) = (C o−C o )(C p −C p ) σ o σ p , • FA2 = fraction of data (%, normalized to 1) for 0, 5 ≤ C p C o ≤ 2, • FB (fractional bias) = C o−C p 0,5(C o +C p ) , • FS (fractional standard deviations) = σ 0 −σ p 0,5(σ 0 +σ p ) , 51
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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AIR POLLUTION – MONITORING MODELLING AND HEALTH

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