The air pollution situation of many European urban areas doesn't present indications of substantial improvement, in spite of the adoption of technological interventions for emission limitations and processes for source reduction (1); actually, these actions, without other activities, like clear understanding of emissive and atmospheric phenomena influencing the result, are not able to lead the air quality back to desired standards.
The air quality situation is even more critical in areas like northern Italy, where the pollution levels (in particular [PM.sub.10] and [NO.sub.2]) are very high because of the low wind conditions of the Po Valley that don't help the dilution of the pollutants. In order to obtain some improvements for air quality, the regional decision makers are trying to define some intervention policies, such as the limitation of old vehicles, in particular diesel cars before EURO II and gasoline cars before EURO I. The present paper deals with PM emissions from traffic, considering exhaust and non-exhaust particles, from civil heating plants and industrial plants. The investigated area is the town of Cuneo, placed in the South of Piedmont, N-W Italy; the town has 50,000 inhabitants and the surrounding area is characterized by the presence of two cement factories, a glass manufacture and a tyre production plant.
In order to deal with the problem in the right way, many subsequent elements are necessary, as follows:
- it is necessary to individuate the principal emission fluxes, taking into account the sources spatial distribution and their capacity to generate fixed quantities of pollutants;
- the correlation between emitted fluxes and environmental concentrations must be evaluated by means of atmospheric models, and the results must be compared with experimental values;
- with reference to different emission scenarios, the different effect on air quality must be established, and the obtained concentrations must be evaluated and compared to the required standards; this way, it will be possible to establish criteria for real time limitation or structural interventions.
RESULTS AND DISCUSSION
Particles emissions from traffic
Data at disposal: In the analyzed town we have at disposal the meteorological data measured by the regional station placed on the roof of the Chamber of Commerce: wind direction, wind speed, solar radiation and ambient temperature. In the analysed area, the winds have a typical bimodal behaviour around 40-60 degrees clockwise from the N: during the night the wind comes from N-E and in the night it blows towards N-E. The mean wind speed in the area is quite low, around 1.4 m/s, and there is an high percentage of calm hours (
As for the traffic data, magnetic counter measurements provide traffic flows for all the main street of the town, as one can see in Fig. 1. As a matter of fact, in the main street of the town, more than 308,000 vehicles circulate every day.
[FIGURE 1 OMITTED]
Moreover, in order to determine the composition of the vehicle park, we used the Automobile Club Italia data referring to the registered vehicles of the town in 2004. Finally, in order to assess the emissions of the measured traffic flows, we used the emission factors provided by the European model Copert3 (2): according to this methodology, the vehicle park can be divided into 105 categories depending on the typology, the fuel and the legislation class. The emission factors are speeddependent functions and they can take into account the transient thermal engine operation (cold start) and the increase of the emissions due to the degradation of the catalytic converters with the mileage of the vehicles.
Particles emission factors
PM emissions from traffic can be divided into three main groups (3):
* non-exhaust emissions deriving from brakes wear (PM10-PM2.5);
* non-exhaust emissions from road abrasion, tyre wear and road dust re-suspension that are found partly in the fine fraction ([PM.sub.2.5]) and mostly in the coarse fraction ([PM.sub.10]-[PM.sub.2.5]).
First of all in the present paper, given the dimension of PM emitted by traffic, it will be considered as [PM.sub.10]. Secondly, PM emissions are strongly influenced by external factors as road condition (wetness, salting, sanding, road material) and use of studded tyres.
The emission factor for PM is a critical parameter for our work. Literature data reports several different model to define in particular non-exhaust emissions:
* The US EPA model (4) based on silt load and the weight of the vehicles,
* The "German method" based on the traffic situation (5),
* The Swedish Empirical Model (6),
* The Danish method (7),
* TNO-CEPMEIP database (8).
Table 1 and Table 2 report the results deriving from some of these methods.
Table 1: PM emission factors from different methods Method/Traffic average Share of exhaust Situation Speed constant emiss. [km/h] speed factor driving[%] (fleet-mix) [mg/km veh] German method: 60-130 motorways or Outside cities tunnel 60-100 city main 56 46 19 road(HVS1)** city main 44 52 20 road(HVS2)** city main 34 44 22 road(HVS3)** city main 28 37 26 road(HVS4)** city traffic 24 32 28 lights(LSA2)** city slow 17 23 32 traffic(IO-Kem)** Daniesh method for 45 66 JGTV swedish method for 40 37 HORG Method/ non-exhaust non exhaust Traffic emission factor emission situation (fleet-mix)[mg/km factor*[mg/km veh] veh] cars/vans trucks German method: motorways or outside cities 22 200 tunnel 10 200 city main road 29 22 200 (HVS1)** city main road (HVS2)** 41 30 300 city main road (HVS3)** 54 40 380 city main road (HVS4)** 66 50 450 city traffic lilghts (LSA2)** 82 60 600 city traffic lilghts (IO_Kern)** 118 90 800 Danish method for JGTV 57 50/70 230 Table 2: Non-exhaust PM emission from TNOCEPMEIP non-exhaust PM emissions (mg/km/vehicle) tyres...