A deterministic air quality forecasting system for

Environmental Modelling & Software 23 (2008) 344e355 www.elsevier.com/locate/envsoft

A deterministic air quality forecasting system for Torino urban area, Italy S. Finardi a,*, R. De Maria b, A. D’Allura a, C. Cascone b, G. Calori a, F. Lollobrigida b b

a ARIANET, via Gilino 9, 20128 Milano, Italy ARPA Piemonte, corso Unione Sovietica 216, 10134 Torino (TO), Italy

Received 25 January 2007; received in revised form 1 April 2007; accepted 2 April 2007 Available online 31 May 2007

Abstract An urban air quality forecasting system for Torino city has been developed, within the EU funded project FUMAPEX, to support the prevention and management of urban air pollution episodes. The proposed forecasting system is designed to provide stakeholders with information useful to define mitigation actions, and to inform the population. The modelling system is based on prognostic downscaling of weather forecasts and on multi-scale chemical transport model simulation, in order to describe atmospheric circulation in a complex topographic environment, space/time variation of emissions and pollutant import from neighbouring regions. Nested domains are employed to reach the target resolution of 1 km, resolving the main structure of the urban area. The modelling system has been verified through its application to a summer photochemical episode and to a winter NO2 and PM10 episode. The verification has demonstrated the system capability to describe space and time variation of summer NO2 and O3 concentrations. Winter pollution levels during rush hours have also correctly reproduced, while underestimation of NO2 and PM10 has been found during the central part of the day. The positive results of FUMAPEX project induced Piemonte Region and Novara Province Administrations to implement operational air quality forecasting systems for the cities of Torino and Novara. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Urban air pollution; Air quality forecasting; Air quality management; Urban meteorology; Chemical transport models

Software availability

1. Introduction

Program title: FUTURE (FUmapex Torino Urban air quality foREcasting system). Developer and contact address: ARIANET, via Gilino 9, 20128 Milano, Italy. Hardware required: Personal computer PIV or equivalent, parallel computer with at least 4 nodes to run RAMS operationally. Operating System: Unix/Linux. Program language: Fortran90, bash, make and tcl/tk. Availability and cost: [email protected]

The European Union air quality standards, adopted by member states in 2005 and foreseen for implementation in 2010 (EC/99/30; EC/2000/69 and EC/2002/3), include shortterm concentration limits for the major pollutants affecting the urban areas (e.g. NO2, PM10 and O3) that can be exceeded very few times a year. This approach aims to protect the population from peak concentrations and fosters severe episode prevention also through the implementation of air quality forecasting systems. To respect short-term limit values given by legislation and diminish dangerous concentration levels, emissions abatement actions have to be planned at least one or two days in advance. Moreover, according to EU directives, information to the public on the air quality status and on the predictable trend for the following days have to be provided by local authorities. Presently, mitigation measures are often decided only on the

* Corresponding author. Tel.: þ39 2 2700 7255; fax: þ39 02 2570 8084. E-mail address: [email protected] (S. Finardi). 1364-8152/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2007.04.001

S. Finardi et al. / Environmental Modelling & Software 23 (2008) 344e355

different modelling tools through the connection of Numerical Weather Prediction, Urban Air Pollution and Population Exposure models. Torino, together with Helsinki, Oslo, Copenhagen, Bologna and Valencia, was one of the target cities chosen for UAQIFS development and demonstration. Torino metropolitan area, with around 1.3 millions inhabitants, is one of the largest urbanised areas in Northern Italy and location of relevant industrial facilities. The city, sited at the western edge of the Po Valley (Fig. 1), lays mainly on flat topography between the Western Alps and a range of hills on its east side. Torino is an important knot of the road and rail networks, on the route to the most important passes and tunnels of the Western Alps (e.g., Frejus and Mont Blanc tunnels). Local atmospheric circulation is strongly influenced by the shelter effect of the Alpine chain, and it is dominated by the superposition of mesoscale (e.g. Po Valley stagnation, mountain/valley breezes and fo¨hn) and urban flow features. Similar to other urban areas in the Po Valley (Finardi and Pellegrini, 2004; Kukkonen et al., 2005), the city core and the surrounding urbanised area are exposed to severe short-term air pollution episodes during both winter and summer periods, when EU air quality standards for NO2, PM10 (winter) and O3 (summer) concentrations are often exceeded, while SO2 and CO show rather low levels. The peculiar topography of the area, the emission features, and the geographic position of the city, within the heavily polluted Po Valley, rises the need of a forecasting system capable to describe the atmospheric flow at different scales influencing the area, the local emissions dispersion, the contributions from large scale and the pollutants chemical transformations. These requirements are quite common in areas where the description of local emissions effect is often insufficient to reconstruct and explain air pollutant concentrations, especially during summertime. This multi-scale approach and the aim to simultaneously describe atmospheric chemistry and air quality at regional and urban scale represent the major differences of the proposed modelling tool with respect to previously cited deterministic forecasting systems. 5300 Austria 5200

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basis of measured concentrations and weather forecasts when the air quality episode has already taken place, limiting their possible effectiveness. On the other side, the availability of a reliable urban air quality forecasting system could support decision makers to plan effective abatement measures on time, preventing air pollution episodes while avoiding useless restrictions of economic activities. Such system could also be employed for urban emergency management for accidental toxic releases, fires, or even chemical, radioactive, or biological substance releases due to terrorist actions. During the last decade several forecasting systems have been designed on the basis of different computational methods to provide air quality predictions. The first systems generally implemented statistical approaches based on empirical observations, such as artificial neural network, taking advantage of their limited computational resources demand. An evaluation and intercomparison of these models for NO2, PM10 and O3 concentrations forecast is provided by Kukkonen et al. (2003) and Schlink et al. (2003). A variety of approaches based on empirical methods are presently applied to forecast air pollutant concentrations in different urban location: multilayer perceptronbased neural models are used to forecast ozone peaks in the Orleáns region (Dutot et al., 2007), NO2 and O3 hourly average concentrations of in Bilbao (Agirre-Basurko et al., 2006) and PM10 concentrations in Thessaloniki (Slini et al., 2006); an Extended Kalman filter algorithm has been designed to forecast daily mean PM10 concentrations in Bordeaux (Zolghadri and Cazaurang, 2006). The performance of 15 different statistical techniques have been assessed in an inter-comparison study based on data sets from 10 European regions (Schlink et al., 2006). Later, the fast increase in velocity and memory capacity of new generation computers allowed the development and implementation of forecasting systems based on deterministic or semi-deterministic models (see e.g. Brandt et al., 2001; Frohn et al., 2001; Vaughan and Lamb, 2003). National and regional scale forecasts are presently provided by US-EPA and NOAA (http://www.airnow.gov) and by different European institutions as French Institut National de l’Environnement Industriel et des Risques (INERIS) (http://prevair. ineris.fr; Bessagnet et al., 2004) and University of Cologne (http://www.eurad.uni-koeln.de/index_e.html). Urban scale forecasting systems have been successively developed and are now in use in some European cities for both urban air quality forecast, as in Oslo (Slørdal et al., 2004; Baklanov et al., 2007), and emergency preparedness, as in Copenhagen (Hoe et al., 2002; Baklanov et al., 2006). Deterministic air quality modelling is applied even for industrial plants real-time air quality control systems (San Jose´ et al., 2007). A big impulse to improve urban air quality management and forecasting tools performances and availability has been provided by the European Commission’s Framework Research Programmes. An urban air quality information and forecasting system (UAQIFS) for Torino metropolitan area has been developed, within the EU funded 5th Framework Programme project FUMAPEX (http://www.fumapex.dmi.dk). The main scientific objectives of the project have been the improvement of meteorological forecasts for urban areas and the integration of

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The architecture of the modelling system designed to answer these needs is introduced and discussed in Section 2. The Torino UAQIFS built within FUMAPEX project is described in Section 3, while details of the different modules implemented are briefly described in specific subsections. The modelling system performances have been verified both on a summer photochemical episode and on a winter pollution one (Section 4). The studied episodes have been used to investigate the influence of space resolution on forecasted concentrations, to verify the system configuration and to optimise the use of computational resources. The specific features of the forecasting system derived from FUMAPEX UAQIFS and presently operational at the Environmental Protection Agency of Piemonte Region are briefly commented in Section 5. The major steps followed during the modelling system choice, development and verification are resumed and discussed in Section 6, referring to the modelling development process recently described and discussed by Jakeman et al. (2006) in the Position Paper published on this journal. 2. Forecasting system architecture The proposed forecasting system aims to match the scientific reliability of state-of-the-art atmospheric transport and dispersion modelling with the end users needs and goals, providing an upgradeable system architecture suitable to be exported to different cities, where different meteorological forecasts may be available. The structure of the computational system has been built as modular as possible, limiting models’ inter-dependence, to facilitate system improvements (e.g. modules upgrade or substitution) without modifying the general system structure. Results production and visualisation are user-oriented, to facilitate presentation, interpretation and comparison according to national and EU legislation. All the conceptual modules composing the forecasting modelling system are sketched in Fig. 2. The forecasting system needs to access emission inventories (to describe sources at different scales), geographic and physiographic data (to describe topography, surface cover and possibly urban cover details, for emission treatment, meteorological and air quality modelling), larger scale meteorological and air quality forecasts and observations (used to define initial and boundary conditions for the Geographic & Physiographic Data

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Fig. 2. General Urban Air Quality Information and Forecasting System architecture.

meteorological downscaling and air quality modelling). The main functions (bold font in Fig. 2) are briefly introduced hereafter, while a detailed description of the models actually implemented in FUMAPEX UAQIFS is provided in the following section. The meteorological downscaling module was used to fill the resolution gap between the available meteorological weather forecasts and the target resolution of the air quality modelling system, usually of the order of 1 km. Within the EU different numerical weather prediction models are run by national and regional weather services, research and private institutions. Among the others COSMO (http://www.cosmo-model.org/ public/default.htm), ALADIN (http://www.cnrm.meteo.fr/ aladin) and HIRLAM (http://hirlam.knmi.nl) consortia can be cited. Weather forecast data provided by each institution are characterised by their own grid system, coordinates, space and time resolutions. One of the basic ideas of the proposed UAQIFS is the capability to access any of these forecasts, without being strictly linked to a particular meteorological model, while the meteorological downscaling module has to be implemented following the specific needs of the site tied e.g. to its topographic features and to the available computer power. In the realised FUMAPEX prototype UAQIFS the meteorological downscaling through a prognostic model has been preferred. Otherwise, the direct use of the available weather forecast is always possible, through the interface module, using a diagnostic treatment based on fields interpolation and adjustment to local topography, similar to the one used in Calori et al. (2006). The emission processor has to provide gridded hourly emission rates for all the pollutants considered by the air quality model, through space disaggregation, time modulation and VOC and PM speciation of data from one or more emission inventories. The capability of integrating multiple inventories is a fundamental requirement to exploit high resolution data where available and near administrative borders, where different data sets may be available (e.g. in the case of Torino UAQIFS four different emission inventories have been considered). The interface module has to match the differences between the computational meshes used by meteorological and air quality models, cope with grid system differences (changes in geographic projections, horizontal and vertical mesh structure, resolution). Moreover, the change of grid system and topography makes necessary to re-compute the vertical wind component, to guarantee mass conservation, which is essential for dispersion calculations. The interface module also includes a micro-meteorological and turbulence processor to estimate quantities needed by air quality models but not directly provided by meteorological models, e.g. dispersion parameters, boundary layer height and deposition velocities of the considered chemical species. Finally, the interface module can also enhance the description of land surface features through the introduction of local physiographic data (e.g. high resolution land cover or urban texture) and elements of ‘‘model urbanisation’’ in the computation of micrometeorological and dispersion parameters. As for the air quality model, the suitable component in such a forecasting framework can only be an Eulerian chemical


transport model with multiple grids nesting capabilities. Only this type of model can in fact consistently treat dispersion, gas-phase chemistry and aerosol processes at the different space scales that have to be considered. 3. Fumapex prototype for Torino The forecasting modelling system configuration for Torino is based on three two-way nested domains (Fig. 3): a ‘‘background’’ domain (1088 1088 km2 with 16 km horizontal resolution), devoted to optimise the connection with the larger scale driving meteorological model and to describe long-range pollutants transport, a regional domain, including the whole Piemonte Region (208 272 km2 with 4 km resolution) and the target metropolitan domain, focused on the city of Torino (52 52 km2 with 1 km resolution). This multi-scale modelling approach, applied to emission processing, meteorological and air quality models, should allow to consistently take into account the effect of local as well as distant sources, and to describe processes (e.g. sub-synoptic flow features, photochemical smog) dominated by scales larger than the city scale. Fig. 4 summarizes the different modules of Torino UAQIFS and the related data flow. The main modules’ features and their use inside the forecasting system are shortly described in the following subsections. 3.1. Emission pre-processing system (EMMA) Emission input for air quality simulation is prepared starting from different inventories: the high-resolution inventories of Piemonte and Lombardia Regions (Caserini et al., 2004), the APAT (National Environmental Protection Agency) Italian inventory (Liburdi et al., 2004) and the European scale UNECE/EMEP emission database (http://www.emep.org). Inside each inventory, emission data are subdivided by activity (more than one hundred in the regional inventories) and organized using the SNAP (Selected Nomenclature for Sources of Air Pollution) classification scheme. The gridded emission rates are generated from inventories data via Emission

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Manager/EMMA (ARIANET, 2005a) module, having the capability to merge information from different database including line and area sources. Space and time disaggregation of inventory data are made on activity basis, according to thematic layers (CORINE Land Cover and high-resolution vector information on built-up areas from Piemonte Region cartography) and typical time modulation profiles (yearly, monthly and daily). The panels in Fig. 5 show NOX inventory data over Piemonte and model-ready NO input on Torino local domain. 3.2. The numerical weather prediction model RAMS The model used for the meteorological downscaling is RAMS (Regional Atmospheric Modeling System) Version 4.4 (Pielke et al., 1992; Cotton et al., 2003), a prognostic, non-hydrostatic model with a sigma terrain following coordinate system and rotated polar stereographic projections. The model implements a full set of non-hydrostatic, compressible Reynolds-averaged primitive equations, plus conservation equations for scalar quantities, supplemented with parameterisations for turbulent diffusion, solar and terrestrial radiation, moist processes, kinematic effects of terrain, cumulus convection, and sensible and latent heat exchange between the atmosphere and the surface, described by multiple soil layers, vegetation, snow cover, canopy air, and surface water. For the present application RAMS is initialised and driven by ECMWF forecasts with 0.5 space resolution and 6-h time resolution. Topography is described by the USGS GTOPO30 digital elevation model at 3000 resolution, while surface cover is defined using the European CORINE Land Cover data set at 250 m resolution. 3.3. Interface module (GAP/SURFPRO) Meteorological and air quality models are connected by the interface module GAP/SURFPRO (Calori et al., 2006; Finardi et al., 2005). GAP (Grid AdaPtor) is a grid interpolation tool interfacing FARM (Flexible Air quality Regional Model) chemical-transport model with any NWP and

Fig. 3. Torino City UAQIFS nested computational domains (left) and inner target area details (right), with major road network and urbanised areas.


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Fig. 4. Torino City Urban Air Quality Information and Forecasting System.

meso-meteorological models. GAP interpolates a sequence of 2D and 3D atmospheric fields from a source grid identified by mesh points, geographic coordinates and altitudes, to a target grid defined using UTM projections and terrain-following vertical coordinates. The set of 2D/3D variables to be interpolated is freely configurable, and different interpolation techniques for sparse data can be selected. Starting from topography and land-use data managed by the modelling system and gridded fields of meteorological variables (e.g. wind, temperature and humidity) provided by RAMS, SURFPRO (SURface-atmosphere interFace PROcessor) (ARIANET, 2005b; Finardi et al., 1997) can compute 2D gridded fields of turbulence scaling parameters (i.e. roughness length, sensible heat flux, friction velocity, Monin Obukhov length, mixing height and convective velocity scale) as well as 3D fields of horizontal and vertical diffusivities and

2D fields of deposition velocities for a given set of chemical species. The computational schemes implemented in SURFPRO are based on the Monin Obukhov similarity theory (Holtslag and van Ulden, 1983; Hanna et al., 1985; Paine, 1988; Scire et al., 1990; Fisher et al., 1999); when computing radiation and energy budgets, the processor can take into account water bodies, terrain slopes and related solar shading effects. In order to better describe the effect of urban canopy on surface energy budget and on the turbulent boundary layer development, an updated version of SURFPRO (Finardi et al., 2005) has been developed within the FUMAPEX project. The new version includes the objective hysteresis model of Grimmond and Oke (1999, 2002), to enhance the description of the urban surface energy budget, and mixing height computational schemes accounting for inhomogeneities and advection effects (Gryning and Batchvarova, 1996; Zilitinkevich and Baklanov, 2002).

3.4. Air quality model (FARM) FARM is a three-dimensional Eulerian model that accounts for transport, chemical conversion and deposition of atmospheric pollutants, originally derived from STEM (Sulfur Transport Eulerian Model) (Carmichael et al., 1998). FARM can be configured with different gas-phase chemical mechanisms; SAPRC-90 mechanism (Carter, 1990) is used for the present test cases. As for aerosols, FARM allows to choose between two modules: the CMAQ aero3 modal aerosol module (Binkowski, 1999) and a simplified bulk aerosol module (aero0), based on the approach adopted by the EMEP Eulerian Unified model (EMEP, 2003). Due to computational

Fig. 5. Diffuse NOX emissions at municipality level (tons year1) from 2001 Piemonte Region emission inventory (left) and NO diffuse emission rates (g km2 s1) reconstructed at 08:00 l.s.t. on Jan 14th 2003 (right). Contour lines on the right panel indicate topography elevations; x and y axis labels indicate UTM zone 32 coordinate projection in km.


constraints, the aero0 scheme has been adopted in the current prototype, leaving to a later stage the adoption of a PM forecast based on the more complex scheme. Initial conditions for modelled species are prepared using a successive corrections objective analysis method, employed to correct large-scale concentration fields with local observations. Boundary conditions to drive the master grid are directly extracted from largescale forecasted fields. To carry out system verification test cases larger scale pollutant concentration fields have been obtained from a climatology of Unified EMEP Model’s fields at 50 50 km2 horizontal resolution and 6-h time frequency, while local observations have been provided by Piemonte Region air quality monitoring network. For operational system application, the initial and boundary conditions will be obtained by continental scale air quality forecasts such as those provided by INERIS with Prev’air system (http:// prevair.ineris.fr). 3.5. UAQIFS scheduling procedure and software details All the modules described in the previous paragraphs are coded in FORTRAN90. EMMA makes use of additional bash shell and make scripts. The operational procedure that schedules the whole forecasting system, including access to external data, modules execution, post-processing and graphs production, is written in tcl/tk scripting language. The modelling system includes in fact also simple statistical tools to verify air quality standards attainment and graphical tools to permit a quick results analysis. Standard graphics are automatically generated through GrADS scripts, while interactive graphical analysis of results is provided by Arianet Visualizer (AVISU), a GUI-based program developed on PV-WAVEÒ platform for 3D atmospheric fields visual exploration. The forecasting system tasks requiring the largest part of computer power are: meteorological downscaling (Section 3.2) and air quality modelling (Section 3.4). RAMS is a parallel code realised using MPI techniques and it takes about 2.5 h on a 4 nodes AMD Opteron 275, 2200 MHz processor computer, for a 48 h meteorological forecast, with a speed-up factor around 3 with respect to scalar run. FARM provides a 48 h air quality forecast in about 3.5 h on a single node of the previously mentioned computer.

some detail, while results obtained on the second episode are briefly commented. 4.1. Summer episode: sensitivity to horizontal resolution The first verification period is a photochemical pollution episode occurred during July 19e21, 1999, when exceedances of the O3 hourly concentration limit of 180 mg m3 were recorded in some monitoring stations of the Torino Province network. The episode was controlled by persistent high pressure conditions, characterised by fair weather with some convective instability and local episodic precipitation over the Alpine chain. The forecasting system simulations have been extended for 72 h to emulate operational conditions. The reliability of the predictions has been verified through comparison with urban and sub-urban meteorological and air quality observations from the Torino Province monitoring network (Fig. 6). The sensitivity to horizontal resolution has been investigated by comparing results from two simulations employing respectively 3 and 2 nested grids, with final resolution of 1 and 4 km. The increase of spatial resolution strongly enhances the general features of meteorological and concentration fields, also in view of the better description of emission patterns. The wind fields show increasing flow channelling between the Alps and the hill in agreement with increased topography details. Significant wind direction changes are observed over Torino urban area, e.g. on July 20th at 09:00 l.s.t. when wind simulated in the two resolutions shows differences in direction of about 90 degrees (Fig. 7). The resulting forecasts of wind, temperature, humidity, NO, NO2 and O3 concentrations have been compared with measurements taken within Torino urban area. The meteorological results have been compared with measurements at the urban stations of Alenia and CSELT, located inside Torino, and with the rural stations of Druento and Bauducchi, located respectively North-West and South-East of the city. Air quality forecasts have been verified using the Torino urban stations of Consolata and Lingotto, and the suburban stations of

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4. Forecast verification on severe air pollution episodes Following the FUMAPEX project approach, the verification of the Torino forecasting system has been based on the reconstruction of severe urban episodes. The criteria that guided the selection of the episodes for different target cities and the analysis of their meteorological and air quality features are widely documented in Valkama and Kukkonen (2003). The selected air quality episodes have been used also to verify FUMAPEX UAQIFSs sensitivity to different aspects of modelling system configurations, such as computational mesh resolution and forecast length (Ødegaard et al., 2005). Torino system results on the first selected episode, employed for sensitivity analysis too, are described hereafter in

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Fig. 7. Effect of increased resolution on hourly mean wind field, from 4 km (left) to 1 km (right). Wind fields refer to 20/07/1999 at 09:00 l.s.t. Every second arrow is plotted for the 1 km resolution field to make the picture more readable.

Alpignano and Borgaro, located at Northern and North-Western suburbs of Torino (Fig. 6). Fig. 8 shows the direct comparison of computed and measured values of wind speed and direction at CSELT and Bauducchi stations. Wind results are rather satisfactory for the urban station, where time variations of speed are reasonably well reproduced at both 4 and 1 km resolution. Direction forecast is otherwise improved at higher resolution, especially during the night between July 19th and July 20th, thus confirming the correctness of the rotation previously pointed out. The model performance is worse at Bauducchi rural station, where the wind speed is overestimated and the wind direction shows larger errors. It has to be kept

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into account that the first RAMS computational level is located around 25 m above ground, while rural stations have wind measurements at 10 m, and CSELT urban station is on a rooftop, at about 30 m AGL. Due to this, in very low wind conditions the values at the urban station probably better reflect the mesoscale flow described by model simulation, while the very low wind speeds recorded at the rural station are probably more influenced by local scale features. The NO2 and O3 concentrations patterns show increased details with higher space resolution. Fig. 9 shows NO2 and O3 ground fields on July 20th respectively at 09:00 and 14:00 l.s.t., when maximum concentrations are recorded.

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Fig. 9. Hourly average concentration fields of NO2 on 20/07/1999 at 09:00 (above) and O3 on 20/07/1999 at 14:00 (below) at 1 km (right) and 4 km (left) resolution; grey levels inside circles represents measured values; x and y axis labels indicate UTM zone 32 coordinate projections in km.

The footprint of the emissions from the urban area and major roads are in fact better recognizable in the high resolution simulation and computed maximum and minimum concentration values show large differences from 4 to 1 km resolution (maxima are nearly doubled at higher resolution). Moreover, high resolution concentration fields show a closer agreement with the observed space distribution of concentrations depicted by local observations, confirming a better description of the gradients existing between urban and sub-urban locations. As expected, daytime ozone concentration fields show larger scale structure and a less evident influence of small-scale emission patterns. Nevertheless, the increase of space resolution causes a slight increase of O3 maximum concentrations and the appearance of exceedances of the hourly average threshold (180 mg m3) at the foothills on the western part of the domain (Fig. 9), downwind of the Torino urbanised area, a combined effect of topography and local chemical regime. The direct comparison of computed and measured values show a quite good reproduction of NO2 concentrations at both urban and sub-urban locations (Fig. 10). Higher space resolution improves the description of morning maximum values. The larger errors in the simulation of the NO2 peak value observed at the urban station of Lingotto during the first

hours of simulation can be explained by the spin-up time of the dispersion simulation. Sensitivity analysis showed, in fact, that initial condition’s effects last around 12 h for NOX over the target area (model grid 3), while longer influences can be detected on O3 concentrations under weak synoptic meteorological forcing. The effects of boundary conditions instead can be neglected, due to the use of the large-scale background computational domain. O3 concentrations are adequately described at Borgaro suburban station, with a decrease of the model performance on the third day of the forecast (Fig. 10), while larger errors are observed at Lingotto urban station. Differently from what usually expected, the observed concentrations show larger values at the urban station, while the modelling system forecasts slightly larger values at the suburban location (Lingotto station is located near the minimum values of the computed concentration fields e Fig. 9). Such peculiar behaviour in measured data can hardly be explained, and the possible influencing factors (e.g. local sources behaviour or circulation features) can not be easily described by the modelling system basing on the current sets of information. The higher resolution simulation produced slightly higher O3 maximum values and enhanced the modelling system’s performance during nighttime and


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Fig. 10. Comparison among NO2 (top) and O3 (bottom) observed (black squared line) and computed concentrations (mg m ) at 4 km (grey triangles line) and 1 km (black squares line) resolution, at Lingotto urban station (left column) and Borgaro suburban station (right column).

morning hours, when minimum observed values are better described. 4.2. Winter episode verification The second test episode occurred during winter high pressure conditions (13e15/01/2003), characterised by low temperatures, very low winds and fog in the Western Po Valley. Exceedances of PM10 concentrations were recorded in all the monitoring stations of Torino Province, with daily averages as high as 160 mg m3. Simultaneous high concentrations of NO2 were also recorded, with peak hourly values over 150 mg m3 and a few exceedances inside Torino urban area. Satisfactory results have been obtained for simulated NO2 and PM10 during rush hours and evening hours, as shown in Fig. 11. Difficulties arose in the description of measured concentration during the central part of the day, when computed concentrations diminish due to a moderate reduction of emission rate and the simultaneous growth of turbulent boundary layer, while measured concentrations of both NO2 and PM10 show persistence of elevated values (not shown). Further investigation is ongoing to understand the causes of this behaviour at ground level and to improve urban scale dispersion modelling during winter stable conditions. Another difficulty that emerged concerns the spatial structure of PM10 concentration fields. The surface concentration fields shows a strong gradient between the urbanised area and sub-urban and rural surroundings, where modelled concentration values are rather low. This feature can not be confirmed by the available measurements, that recorded daily average concentrations over 120 mg m3 within the city, around 90 mg m3 at sub-urban locations and 60 mg m3 at rural background ones. This

underestimation of background values may be due to an insufficient description of PM accumulation phenomena that characterises the Po Valley basin during wintertime. The definition of initial conditions (for a lack of observations currently based only on EMEP climatological concentration fields) must probably be enhanced. The simplified aerosols scheme used so far can also play a role, and therefore the adoption of the more complete scheme available in the model will be tested soon. 5. Torino and Novara operational uaqifs The promising results obtained by FUMAPEX project prototype induced Piemonte Region and Novara Province Administrations to implement operational air quality forecasting systems for the cities of Torino and Novara (the two largest urban areas in Piemonte). Due to limited computational power presently available, the operational UAQIFS implemented at ARPA (Agenzia Regionale per la Protezione dell’Ambiente) Piemonte is a simplified version of FUMAPEX prototype, where meteorological fields are not obtained by prognostic downscaling, but are provided by the non-hydrostatic prognostic model LAMI (Italian version of DWD Lokal Modell), running at ARPA Emilia-Romagna Meteorological Service and available on a daily basis at ARPA Piemonte as member of the COSMO consortium. LAMI provides forecasts over the whole Italian territory with horizontal resolution of about 7 km. In the operational UAQIFS meteorological fields are then directly interpolated and adjusted (forced to be non-divergent) by the GAP interface module to match the air quality model resolutions of 16, 4 and 1 km. The system runs on a daily basis in order to produce concentration maps that, after a verification period presently


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ongoing, will be used to design air quality forecast bulletins to be disseminated to the public administrators and to the citizens. The UAQIFS starts computation around 5 AM. It releases the first day of simulation (þ24 h forecast) at about 8:00 AM, and the second day (þ48 h forecast) around 11:00 AM. 6. Comments on model choice, development and evaluation practice A good practice for purposeful and reliable model development have been effectively outlined through the definition of ten basic steps by the position paper of Jakeman et al. (2006). The presented UAQIFS development process partially respected the proposed criteria. In particular the first five steps concerning model choice, development and implementation can be considered fulfilled. The modelling system has been designed and built with the clear scope to forecast air quality over Torino metropolitan area, describing space and time variation of concentration fields of all pollutants covered by EU legislation. The modelling system is finalised to support local

administrations to prevent and manage air pollution episodes. The selection of deterministic models is justified by the intention to support the analysis of conditions causing or favouring severe air pollution episodes occurrence, and it is based on the possibility to exploit larger scale forecasts and local observations operationally available at ARPA Piemonte. Moreover, the choice of the different modules has been conditioned by previous application experiences and by their routine use for air quality assessment activities at ARPA and Regione Piemonte. An end user oriented approach has been guaranteed by the common effort of ARIANET and ARPA Piemonte, who jointly participated to FUMAPEX research activities, model development and preliminary verification. The collaboration later continued through the implementation and management of the operational system described in Section 5. The last five steps described by Jakeman et al. (2006) have been only partially covered by the development and verification of the proposed UAQIFS. Sensitivity analysis has been performed to verify initial and boundary conditions effects and to support the definition of the computational grid system

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(Fig. 3) and further model configuration. Preliminary verification of model forecasting capabilities have been based on episodes reconstruction exercises. More exhaustive evaluation is programmed to be performed on results of the operational system first year activities. A precise quantification of model uncertainty is at the moment missing. A thorough uncertainty evaluation of a system including complex modules covering specific items (i.e. emissions, meteorology and air quality) and relevant possible side effects (e.g. initial and boundary condition definition and space resolution effects) is a quite complex and expensive process, that can hardly be based on consolidated and easily applicable procedures. This activity has been postponed to the demonstration and implementation of the UAQIFS and to the evaluation of its effectiveness through the comparison of model results with local air quality observations. 7. Conclusions The different activities performed within the EU funded project FUMAPEX allowed to build an urban air quality forecasting system for Torino city through the integration of state-of-the art modelling tools for emission treatment, meteorological and air quality modelling. The Torino city UAQIFS has been designed to manage complex topographic, meteorological and emission conditions, suitable for application in urban areas exposed to both local emissions and to regional contributions. System features selected to meet these requirements have been discussed, with special attention on Torino case. The Torino forecasting system was evaluated through the simulation of summer and winter episodes. High resolution meteorological and air quality modelling proved to be very important to obtain satisfactory results for summer photochemical episodes. In particular, simulations at 1 km horizontal resolution allowed to forecast maximum NO2 and O3 concentrations better than simulations at coarser resolution, which gives lower maxima. Good results were obtained for NO2 and O3 concentrations during the summer episode, with satisfactory reproduction of spatial distribution and temporal variation of concentrations. Some difficulties were found in the simulation of the winter episode. NO2 and PM10 hourly average concentrations were correctly reproduced during rush hours and during the evening, while some underestimation was detected during the central part of the day. PM10 background values at rural and sub-urban locations resulted to be underestimated, due to the possible difficulty to simulate particulate accumulation phenomena at the Po Valley scale during stagnation conditions. Further investigations are ongoing to identify the weak points, improve simulation results and provide a more comprehensive model validation. The presented modelling system verification is, in fact, limited by the small number of measuring stations used, e.g. only one urban station recording hourly values was available for PM10 during the selected episodes. More recently, Torino air quality network has been updated and improved, PM10 stations are now available in urban, sub-urban and rural-background locations, allowing a better model results verification. Moreover a comprehensive

verification of an UAQIFS has to be based on its operational results or on long lasting test cases to define its effectiveness during different meteorological and air quality conditions and to state its capability to forecast severe air pollution episodes. The positive results obtained by FUMAPEX UAQIFS induced Piemonte Region and Novara Province Administrations to implement air quality forecasting systems for the cities of Torino and Novara, that are presently operational in test phase. Acknowledgements FUMAPEX (http://fumapex.dmi.dk) is a research project funded by the European Commission (contract EVK4-200100281) Fifth Framework Programme, Energy, Environment and Sustainable Development within the sub-programme: Environment and Sustainable Development; Key Action 4: City of Tomorrow and Cultural Heritage. The authors are indebted with Massimo Muraro and Stefano Bande, from the Meteorological Service of ARPA Piemonte, for their help to access and manage meteorological data and for the analysis of air quality episodes. The authors would like to thank Regione Piemonte and in particular Giorgio Arduino and Carla Contardi for their precious support. References Agirre-Basurko, E., Ibarra-Berastegi, G., Madariaga, I., 2006. Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software 21, 430e446. ARIANET, 2005a. EMMA (EMGR/make) e User’s guide e Version 3.5. Arianet R2005.08, Milano, April 2005. ARIANET, 2005b. SURFPRO, SURface-atmosphere interFace PROcessor, Version 2.2, User’s Guide. Arianet R2005.11, Milano, April 2005. Baklanov, A., Ha¨nninen, O., Slørdal, L.H., Kukkonen, J., Bjergene, N., Fay, B., Finardi, S., Hoe, S.C., Jantunen, M., Karppinen, A., Rasmussen, A., Skouloudis, A., Sokhi, R.S., Sørensen, J.H., 2007. Integrated systems for forecasting urban meteorology, air pollution and population exposure. Atmospheric Chemistry and Physics 7, 855e874 (www.atmos-chem-phys.net/7/855/2007/). Baklanov, A., Sørensen, J.H., Hoe, S.C., Amstrup, B., 2006. Urban meteorological modelling for nuclear emergency preparedness. Journal of Environmental Radioactivity 85, 154e170. Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honore´, C., Liousse, C., Rouil, L., 2004. Aerosol modelling with CHIMERE: preliminary evaluation at the continental scale. Atmospheric Environment 38, 2803e2817. Binkowski, F.S., 1999. The aerosol portion of Models-3 CMAQ. In: Byun, D.W., Ching, J.K.S. (Eds.), Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Part II: Chapters 9e18. National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, pp. 10-1e1016. EPA-600/R-99/030. Brandt, J., Christensen, J.H., Frohn, L.M., Palmgren, F., Berkowicz, R., Zlatev, Z., 2001. Operational air pollution forecasts from European to local scale. Atmospheric Environment 35 (1), 91e98. Calori, G., Clemente, M., De Maria, R., Finardi, S., Lollobrigida, F., Tinarelli, G., 2006. Air quality integrated modelling in Turin urban area. Environmental Modelling and Software 21 (4), 468e476. Carmichael, G.R., Uno, I., Phadnis, M.J., Zhang, Y., Sunwoo, Y., 1998. Tropospheric ozone production and transport in the springtime in east Asia. Journal of Geophysical Research 103, 10649e10671.

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