All activities aimed at studying the primary causes and effects of air pollution cannot disregard the fact that it is necessary to have an optimal air quality monitoring network for assessing population exposure to air pollution and predicting the magnitude of the health risks. In the framework of a cooperation between the ARPA Sicilia Organization and the Department of Engineering, University of Palermo, research was performed to develop an innovative methodology useful for defining environmental similarity maps aimed at supporting the design of air quality monitoring networks at the regional scale. This approach is based on a new index called the fuzzy environmental analogy index (FEAI) based on fuzzy theory. FEAI is deduced by combining two indexes: meteorological pressure indicator (MPI) and anthropic pressure indicator (API). MPI allows us to investigate, for the examined territory, analogies relevant to meteorological conditions, and API emphasizes the importance of impacts related to anthropogenic or natural sources at the regional scale. Finally, FEAI applications in a case study related to the Sicily region in Italy are also described. The obtained results confirm the capability of the FEAI to investigate similarities between neighboring areas in terms of environmental pressures due to anthropic and natural sources and to identify gaps in the monitoring network used to define existing air quality conditions.
|Numero di pagine||20|
|Rivista||Stochastic Environmental Research and Risk Assessment|
|Stato di pubblicazione||Published - 2019|
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Safety, Risk, Reliability and Quality
- Water Science and Technology
- Environmental Science(all)
Giardina, M., Buffa, P., Madonia, G., Abita, A. M., Madonia, G., Madonia, G., & Madonia, G. (2019). Fuzzy environmental analogy index to develop environmental similarity maps for designing air quality monitoring networks on a large-scale. Stochastic Environmental Research and Risk Assessment.