br The industry I is a manufactory plant whose main
The industry I1 9(S)-HODE a manufactory plant whose main emission source is a boiler fueled with natural gas for both heating and production pur-poses. The industry I2 is a paper industry which presents a gas-turbine plant and a boiler, both fueled with natural gas. Finally, the industry I3 is a paper industry with a gas-turbine cogeneration plant fueled with natural gas. Yearly natural gas consumptions of I1, I2, and I3 were equal to 0.44 × 106 m3, 10.9 × 106 m3 and 16.5 × 106 m3, respectively. Further possible emissions of the plants were considered negligible and not included in the calculation. The emission factor data for PM10, CO, CO2, NOx, SO2, As, Cd, Ni, B(a)P, and dioxins emitted by the different sources were obtained from the database carried out by the European Environmental Agency (2013). In particular, the EF values (expressed as mg GJ−1 and then properly converted to mg m−3) for the source cat-egories referring to the electricity and heat production with boilers and gas turbines fueled with natural gas were adopted. Emission factors in terms of particle number (EFN) were obtained from scientific studies in-vestigating such natural gas fueled source (Timko et al., 2010).
The emission of the i-th pollutant emitted by the j-th street/road links in Cassino (Eij) was evaluated through the formula:
where EFij is the emission factor of the i-th pollutant emitted by the j-th street/road link, expressed as amount of pollutant emitted per street/ road link, Lj is the length of the j-th street/road link, Nj is the number of vehicles crossing the j-th street/road link on annual basis.
In the rural areas 5 main roads where involved in the study (Fig. 1): the “A1” highway (crossing the municipality for N8 km) and four main regional roads (SR 006, SR 509, SR 630, SR 149; here the northbound and southbound SR006 roads, SR 006-N and SR 006-S, respectively, were referred as a single SR 006 road). Minor rural roads were not in-cluded in the calculations. The number of vehicles crossing the main rural street/road links (Nj) were obtained from traffic databases pro-vided by Regional Road Agency (ASTRAL) and by Italian Highway Agency (AISCAT, www.aiscat.it, and ASTRAL, www.astralspa.it).
In the urban area 108 street/road links (see Fig. 1 and Fig. 2) with a significant traffic flow were selected, whereas secondary streets charac-terized by a limited traffic flow were neglected. The 108 street/road links have a length ranging from 0.10 km to 0.70 km for a total length of 19.12 km.
In order to evaluate traffic flow in main urban street a transport de-mand model was developed. Actually, within Transportation
Engineering issues in urban areas, the use of traffic forecasting models is very common, since it is practically unfeasible and time/cost consum-ing to carry out generalized and extended traffic surveys. In other terms, traffic forecasting models allow to evaluate relevant traffic flows on large urban areas by collecting a limited amount of traffic data. The traf-fic forecasting model (formerly known as “four-step travel demand model”) adopted in the study can be formally expressed by the follow-ing equation (Cascetta, 2009):
diodðs; h; m; kÞ ¼ dioðshÞ piðd=oshÞ piðm=oshdÞ piðk=oshdmÞ ð4Þ
• diod (s, h, m, k) is the average number of trips carried out by class user i (depending on the socio-economic role of the user) starting from or-
igin traffic zone o, and stopping in the destination traffic zone d, for a specific purpose s, within the time period h, using the transport mode m, and choosing the trip path k;
• dio (sh) is the average number of class user i who perform a trip from origin zone o, for purpose s, within the time period h (better known as the “traffic generation sub-model”);
• pi (d/osh) is the fraction of the abovementioned users who terminate the trip in the destination zone d (known as the “traffic distribution sub-model”);