Pollutant dispersion models require a careful preparation and validation of at- mospheric data sets since the accuracy of the model output strongly depend on ...
Estimate of boundary layer parameters and background concentrations for pollutant dispersion modeling in urban areas S. Biemmi1, R. Gaveglio1, P. Salizzoni2, M. Boffadossi3, S. Casadei4, M. Bedogni4, V. Garbero1,5, L. Soulhac2 1
Golder Associates S.r.l ,2 LMFA – Ecole Centrale de Lyon, France , 3DIASP – Politecnico di Milano ,4Agenzia Mobilità e Ambiente (A.M.A) del Comune di Milano, 5DIMAT – Politecnico di Torino
1. INTRODUCTION Pollutant dispersion models require a careful preparation and validation of atmospheric data sets since the accuracy of the model output strongly depend on the accuracy of the meteorological input. Therefore the characterization of the dynamical state of the atmospheric boundary layer is an important challenge in air quality studies. This is particularly important in urban area, where the sensors often give misleading information, due to non correct positioning or to disturbance induced by human activities. In this study we use the meteorological data and background pollutant concentration collected in different measurements stations in the city of Milan to model atmospheric dispersion of traffic related pollutant in the central part of it. A particular care was given in the estimation of the boundary layer depth, which was computed with different methods proposed in literature. The meteorological input was used to run SIRANE (Soulhac, 2002), an urban pollutant dispersion model adapted at the neighbourhood scale. The results provided by the model have been compared with in situ measurements of concentrations of NOx, O3 and CO. The performances of the model have been analysed by means of statistical parameters. A sensibility analysis was performed to identify the most critical parameters to estimate concentrations of contaminants in urban area.
2. METEOROLOGICAL DATA The meteorological parameters were acquired in three different stations. Two stations were placed in the city centre. The first, in Piazza Duomo, was equipped with a sodar. The other, located in via Juvara, about 2,5 km away, was equipped
2
with a hygrometer, a pluviometer, a net radiometer and a thermometer. In order to verify the reliability and the accuracy of the data collected in the stations located within the urban area the two data sets were compared with that obtained at the Linate Airport, which lies at about 5 Km from the city centre. A detailed intercomparison of the three data sets is presented in Biemmi et al. (2008). Once that the data sets have been validated, these have been used to compute the meteorological parameters that could not be measured directly, such as the Monin Obukhov height (L) and the boundary layer depth (h). These were estimated according to the Monin Obukhov similarity theory (Nieuwstadt, 1984) together with hourly mean wind direction and speed, mean temperature and turbulence intensities. A particular care was given in the estimate of the boundary layer depth in stable conditions, which was computed with two different methods proposed in the literature. The first estimate is (Arya, 1981): u* L (1) h=a f
where a = 0.74, u* is the friction velocity (m/s) and f is Coriolis parameter (1/s). The second estimate was obtained adopting the model proposed by Nieuwstadt (1984): (2) L ⎛⎜ u * ⎞⎟ h=
3.8 ⎜⎝
− 1 + 1 + 2.28
fL ⎟⎠
As an example we show in Figure 1 the results obtained with equation (1) and (2) for varying wind speed. Fig. 1. Estimate of boundary layer depth as a function of the wind speed
3. BACK-GROUND CONCENTRATION A correct estimation of the background value of pollution characterizing the investigated area is a tricky problem. These can be estimated by means of direct measurements or by means of numerical simulations at a larger scale. In this case
3
the hourly background concentrations were assumed to be those measured at different monitoring stations external to the domain. Measurements at different stations have been compared with an optimal background concentration, referred to as Copt. Copt was calculated as the difference between the concentration calculated by the local scale model (SIRANE) and that measured at the monitoring stations within the domain. The background concentration that showed the lower standard deviation and fractional bias compared to Copt has been assumed as the background value of the pollutant concentration.
4. RESULTS Pollutant maps over the central part of Milan were computed with SIRANE, with a time step of 1 hour over the whole year 2005. Emission data were obtained by applying the COPERT methodology and refer only to traffic emissions. All other potential sources were neglected and considered as background emissions. The results provided by the model have been compared by the in situ measurements of pollutant concentrations of CO, NOx, and O3. A comparison between measured and calculated concentrations of CO during two weeks is presented in Figure 2, whereas in Table 1 is given an overall evaluation of the performance of the model. Fig.2. Comparison between measured and modeled concentration of CO C [ug/m3] 2000
Model Sirane
1500
Senato receptor 1000 500
31 /0 8/ 20 05
29 /0 8/ 20 05
27 /0 8/ 20 05
25 /0 8/ 20 05
23 /0 8/ 20 05
21 /0 8/ 20 05
19 /0 8/ 20 05
17 /0 8/ 20 05
15 /0 8/ 20 05
0
Table 1. Comparison between modeled pollutant concentrations Pollutant C SIRANE (µg/m3) C measured (µg/m3)
FB
ER
R
CO
1016
986
-0.03
0.36
0.49
NOx
107
115
0.07
0.45
0.49
NO
28
38
0.3
0.70
0.47
NO2
40
56
0.33
0.46
0.51
O3
50
31
-0.46
0.47
0.73
4
Finally a sensibility analysis of the model was performed, in order to evaluate the influence of the estimate of background concentration and meteorological parameters on the ground level pollutant concentration. The most significant influence on the accuracy of the model results was related to the estimate of background concentrations, which affects the pollutant concentration results of about 70-90%. Different estimations of the boundary layer depth instead lead to a slighter variation in model results. Percentage difference in calculated concentrations, using as input both formulas (1) and (2), is equal to 9% (Figure 3). Compared to these two parameters, the differences in all other meteorological variables did not affect significantly the results. Fig. 3. Comparison between modeled values of CO concentration with different estimates of the boundary layer depth. 3
C [ug/m ] 1200 1000 800
Nieuwstadt Formula Arya Formula
600 400 200
17 :0 0 18 :0 0 19 :0 0 20 :0 0 21 :0 0 22 :0 0 23 :0 0 24 :0 0 01 :0 0 02 :0 0 03 :0 0 04 :0 0 05 :0 0 06 :0 0 07 :0 0 08 :0 0 09 :0 0
0
5. REFERENCES Arya S.P. (1981), Parameterizing the height of the stable atmospheric boundary Layer, J. Appl. Meteor., 20, 1192-1202 Batcharova E., Gryning S.E.(1994), An applied model for the height of the daytime mixed layer and entrainment zone, Bound. Layer Meteor., 71, 311-323. Biemmi, S., Gaveglio, R., Salizzoni, P., Boffadossi, M., Casadei, S. and Bedogni, M. (2008) Analisi dei dati meteorologici e parametrizzazione dello strato limite terrestre nell'area urbana milanese. Nimbus, 49-50, 6-16.. Nieustadt F.T.M. (1984), The turbulent structure of the stable, nocturnal boundary layer, J. Atmos. Sci., 41, 2202-2216. Soulhac L. (2002), Notice d’utilisation de SIRANE, École Centrale de Lyon.
