The quality of atmospheric air in large cities is a matter of great importance because of its impact on the environment and on the health of the population. Recently, measures restricting access of private vehicles to the centre of large cities and other measures to prevent atmospheric air pollution are currently topical (Hervada-Sala et al., 2018). The knowledge of air quality acquires special relevance to be able to evaluate the impact of those great social and economic measures. There are many indices to express air quality. In fact, quite every country has its own, depending on the main pollutants, they have as Plaia and Ruggeri (2011) pointed out. In general, all indices ignore the compositional nature of the concentrations of air pollutants and do not apply methods of Compositional Data Analysis; those indices also have some other weak points such as leak of standardized scale. A first approach applying Compositional Data Analysis methods has been developed in Jarauta-Bragulat et al., 2016. In the present work, we try to go some step further to improve the understanding and manageability of air quality. The air quality index proposed here takes into account the compositional nature of the data, it has an adequate correlation between input (concentrations) and output (air quality index), it distinguishes between air pollution and air quality and it has a 0-100 reference scale which makes easier interpretation and management of air quality expression. To illustrate the proposed method, an application is made to a series of air pollution data (Barcelona, 2001-2015)

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