This study presents a Rough Set Analysis (RSA) application, partially based on dominance in relation to air micro-pollution
management in an industrial place with a high environmental risk rate, such as the industrial area of Siracusa, located in the South
of Italy. This new data analysis instrument has been applied to different decisional problems in various fields with considerable
success. Therefore, it is believed that it could also be used for the environmental issue related to multi-attribute sorting, considering
both qualitative and quantitative attributes and criteria, such as sulphur oxides (SOx), nitrogen oxides (NOx), Methane (CH4), nonmethane
hydrocarbons (NMCH) and some meteorological variables, such as air temperature and the relative humidity index. After
outlining some basic concepts of the RSA theory, the most significant results obtained from the RSA specific application are
presented and discussed particularly examples of decisional rules, attribute relevance and some other methodological features are
offered to improve understanding and advantages of the approach.
The decisional rules obtained can also be usefully implemented in order to explain and manage the risk of air pollution.