Poster 10 Modelling integrated system for urban air quality in Bologna

Poster 10 Modelling integrated system for urban air quality in Bologna

Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177...

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Developments in Environmental Science, Volume 6 C. Borrego and E. Renner (Editors) Copyright r 2007 Elsevier Ltd. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(07)06810-6

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Poster 10 Modelling integrated system for urban air quality in Bologna Linda Passoni, Vanes Poluzzi, Marco Deserti, Enrico Minguzzi, Michele Stortini and Giovanni Bonafe` Abstract Bologna, as most of the urban areas located in the Po Valley, is often affected by high pollution mainly by PM10 and NO2. These pollutants are produced by large-scale chemical processes and by direct emissions inside the urban area. The urban pollution was simulated combining the background concentration calculated by a chemical transport model (CTM), with the roadside concentration calculated by an urban dispersion model.

1. Methodology

The advanced Gaussian ADMS-Urban model (CERC, 2003) has been used to simulate the pollutants in urban area, calculating the daily and hourly mean concentrations of PM10 and NO2 during a 1-year period (April 2003–March 2004). The model was run using two meteorological datasets. The first one is provided by the meteorological pre-processor CALMET (Deserti et al., 2001), which uses data from surface and upper air stations in northern Italy. The other dataset (LAMA) was provided by the non-hydrostatic meteorological model LAMI with a continuous assimilation of surface and upper air stations data. Traffic emissions on 213 road links were estimated on the basis of: traffic flows (source: Bologna Municipality), emissions factors (sources: Corinair2000 for gases, TNO for PM10), emissions time-varying profiles (source: Bologna Municipality Structural Plan). The urban background concentrations were provided by the CTM CHIMERE (50 km horizontal resolution; source: INERIS).

Modelling Integrated System for Urban Air Quality

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Table 1. Urban model results (mg m 3) for the annual period, at the monitoring station NO2 annual mean

NO2 18th highest hourly value

PM10 annual mean

PM10 35th highest daily value

68 52

195 123

46 42

68 73

Simulated Observed

Table 2. Urban model results (mg m 3) for the pollution episodes PM10

NO2 Episode

Summer Winter

Simulated (CALMET input)

Simulated (LAMA input)

Observed

Simulated (CALMET input)

Simulated (LAMA input)

Observed

102 63

136 71

35 65

50 55

69 66

32 87

The simulation domain covers a district (2 km  2 km) of the Bologna urban area. Output surface fields are calculated with a 50-m horizontal resolution. The resulting concentrations were used to calculate, by a deterministic exposure model, the exposure of a population of 333 children.

2. Results

The urban model, combined with the regional CTM, performs quite well to assess long-term averages of PM10 and NO2, less to simulate peak pollution (Table 1). During the summer episode (10–16 June 2003), the PM10 daily concentration is in good agreement with observations, while it is underestimated during the winter episode (12–19 February 2004). NO2 concentrations are in good agreement with observations during winter, when the diurnal cycle is well described, while the summer diurnal cycle is not well simulated (Table 2), probably because of the chemical reaction scheme of the urban model, which is not detailed enough. NO2 personal exposures range from 20 to 50 mg m 3, PM10 exposures from 10 to 25 mg m 3.

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Linda Passoni et al.

REFERENCES CERC, 2003. ADMS-Urban User Guide (Version 2.0). Cambridge. Deserti, M., Cacciamani, C., Golinelli, M., Kerschbaumer, A., Leoncini, G., Savoia, E., Selvini, A., Paccagnella, T., Tibaldi, S., 2001. Operational meteorological preprocessing at Emilia-Romagna ARPA Meteorological Service as a part of a decision support system for Air Quality Management. In: Coppalle A. (Ed.), Proceedings of the Sixth Workshop on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Int. J. Environ. Pollut. 16(1–6).