The level of air quality in urban centres is affected by emission of several pollutants, mainly coming from the vehicles flowing in their road networks. This is a well known phenomenon that influences the quality of life of people. Despite the deep concern of researchers and technicians, we are far from a total understanding of this phenomenon. On the contrary, the availability of reliable forecasting models would constitute an important tool for administrators in order of assessing suitable actions concerning the transportation policies, public as well private. As matter of fact, the definition of a physical model requires the knowledge of many parameters, involving the running fleet, the microclimatic conditions and the behaviour of driving people: the resulting complexity of such kind of informations (that also depend on standard and rules) makes almost impossible the assessing and the solution of these models. In turn, in the recent years, a new approach is achieving, increasing popularity, which is essentially based on a phenomenological approach. In other words, the physics of the phenomenon concerning the air pollution is neglected, while the attention is focused on the functional relationships between sources of pollution and local concentrations. Such kind of approaches is made possible by means of the application of analytical models utilising the fuzzy logic, generally supported by genetic and/or neuronal algorithms. Referring to the situation of the running fleet and the measured pollutant concentrations concerning the Italian town of Palermo, a data-deduced traffic model is here derived, its truthfulness being justified by a fuzzification of the phenomenon. A first validation of the model is supplied by utilising the emissions characteristics and the pollutant concentrations referring to a two years period of time. This work could represent a first attempt in defining a new approach to the problem of the pollution of the urban contexts, in order of providing administrators with a reliable and easier tool.
|Numero di pagine||10|
|Stato di pubblicazione||Published - 2005|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Mechanical Engineering
- Automotive Engineering