**1. Introduction**

Air pollution in a region depends mainly on the emission of pollutants and on local meteorological conditions. The probability of air pollution occurrences may be estimated by simple

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

atmospheric dispersion models with proper meteorological data and predefined typical air pollution sources [1, 2].

The novel method of data analysis applied to the study of air micro-pollution management, the rough set approach (RSA), considers objects described by a lot of both qualitative and/ or quantitative attributes and criteria (that is their 'profile'). In this context, inconsistencies between descriptions and risk classes assignments need not be removed prior to the analysis, therefore giving useful information about the quality of the inferred decision rules; moreover, the RSA also allows for highlighting the attributes which most contribute to air pollution among those taken into account for the assessment, giving too some useful information about

Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate…

http://dx.doi.org/10.5772/intechopen.75630

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Furthermore, this method is able to identify redundant attributes. This concerns the elimination of superfluous data from the data table, without deteriorating the quality of the results, that is, obtaining the same information of that inferred from the original table, therefore permitting enormous savings in data collection. Additionally, the rough set theory also shows a posteriori the relative importance of the considered attributes and criteria, without requiring a priori any elicitation or assessment of technical parameters (such as importance weights, trade-off, etc.), which are often very difficult to provide and never easily understandable by

The results hereby obtained are just an example of the RSA application, in order to under-

This chapter contains other five sections; Section 2 explains the basic principles of rough set theory and its main methodological features; Section 3 shows air micro-pollution analyzed data; Section 4 presents the main decision rules obtained; Section 5 discusses the interpretations of the results from the methodological and operational points of view; and lastly,

The rough set theory (RST), introduced by Pawlak [15–17], has proved to be an excellent tool for data analysis, even in the presence of inconsistencies and ambiguities. The main idea of the RSA is that every object in the universe U (data to be analyzed) is associated a certain amount of information (data, knowledge), expressed by means of some attributes used for their description (e.g. if the objects are air pollution observed by monitoring stations, attributes may be air temperature, the relative humidity index, direction and wind speed, quantities of some micro-pollutants, etc.). Objects having the same description [18] in terms of these attributes are called indiscernible (similar); the indiscernibility relation thus generated induces a partition of the universe U into blocks of indiscernible objects, called elementary sets or granules of knowledge, which therefore result in information granulation. If set U is divided in some classes, objects indiscernible should belong to the same class to be consistent

From the universe U, any subset X can be expressed either precisely (as a union of elementary sets) or approximately. In the latter case, the subset X may be characterized by two ordinary sets, called the lower and upper approximations. The lower approximation of X is composed

stand how and why it is possible to apply this approach to environmental problems.

the management of pollution.

Section 6 concludes this chapter.

**2. The rough set theory**

with the indiscernibility principle.

decision-makers.

A lot of studies, however, do not give enough information about the possible relationships between sampling and meteorological parameters, as well as their optimal correspondence formal tools in order to enable modeling and determination of patterns which are characteristic of the investigated area. Proposing conceptual models enables decision-makers at many levels to assess and manage air quality as a whole, rather than on a pollutant-by-pollutant concentration. By developing a holistic approach to air quality, it is possible to evaluate its extensive benefits of more effective developments in existing air-quality features, thereby avoiding the growth of air-quality ceilings, and to consider air quality within its wider meteorological context [3, 4]. By establishing why and when air pollution occasions may occur across a region, strategies should be designed and implemented so as to deal with such episodes. The possibility of forecasting pollutant concentration near the ground with high spatial detail offers the opportunity of constantly monitoring and managing the territory. Air-quality modeling procedures can forecast the behavior and the effects of the substances emitted from identified sources, particularly using data from meteorological instruments. These models can supply the distribution of pollutant concentrations on the ground, and are used for thermoelectric power plant management, being very useful in the case of exceptional events, such as when a highly dangerous pollutant escapes [5].

This study analyses the main relationships between air micro-pollution and meteorological conditions of the area surrounding Siracusa, a city located in Sicily. This was done by measuring air samples from a receiving station near a small town called Melilli, a Sicilian industrial area with a high environmental risk rate [6, 7].

This station has been chosen because it allows the production of a complete picture with respect to the amount of micro-pollution data and meteorological variables descriptions [8]. Then the most reliable parameters for the phenomena of the dispersion of micro-pollutants were identified and also the various critical scenarios were checked, so that all available air pollution sources were considered [9–11]. In particular, a specially designed model, with forecasting abilities of air pollution, has been developed, working independently from the knowledge of the local sources [12]. This monitoring model uses temperature and wind vertical profiles, measured by Radar Analysis Support System (RASS, a radar manufacturer-independent system for evaluating the different elements of a radar by connecting to signals) and SOnic Detection And Ranging (SODAR, a meteorological instrument used as a wind profiler to measure the scattering of sound waves by atmospheric turbulence) and concentration data from ground stations. The local values are correlated with the characteristics of the thermal profile and the direction and intensity of the wind at a selected altitude. On the basis of stored and statistically analyzed data, the model is able to forecast the pollution in the area surrounding the ground station [13] and to give useful information about the management of its main sources.

From the methodological point of view, the proposed approach is in the framework of multicriteria decision analysis, where a lot of different points of views, often conflicting one other, are explicitly considered together to support effective decisions. The utility or, better, the necessity of a multicriteria evaluation in public policies has been recently underlined by Munda [14].

The novel method of data analysis applied to the study of air micro-pollution management, the rough set approach (RSA), considers objects described by a lot of both qualitative and/ or quantitative attributes and criteria (that is their 'profile'). In this context, inconsistencies between descriptions and risk classes assignments need not be removed prior to the analysis, therefore giving useful information about the quality of the inferred decision rules; moreover, the RSA also allows for highlighting the attributes which most contribute to air pollution among those taken into account for the assessment, giving too some useful information about the management of pollution.

Furthermore, this method is able to identify redundant attributes. This concerns the elimination of superfluous data from the data table, without deteriorating the quality of the results, that is, obtaining the same information of that inferred from the original table, therefore permitting enormous savings in data collection. Additionally, the rough set theory also shows a posteriori the relative importance of the considered attributes and criteria, without requiring a priori any elicitation or assessment of technical parameters (such as importance weights, trade-off, etc.), which are often very difficult to provide and never easily understandable by decision-makers.

The results hereby obtained are just an example of the RSA application, in order to understand how and why it is possible to apply this approach to environmental problems.

This chapter contains other five sections; Section 2 explains the basic principles of rough set theory and its main methodological features; Section 3 shows air micro-pollution analyzed data; Section 4 presents the main decision rules obtained; Section 5 discusses the interpretations of the results from the methodological and operational points of view; and lastly, Section 6 concludes this chapter.
