This paper considers the similarity between two measures of air pollution/quality control, on the one hand, and widely used indicators of life quality and welfare, on the other. We have developed a multi-layer perceptron neural network system which is trained to predict the measurements of air quality (emissions of sulphur and nitrogen oxides), using Eurostat data for 34 countries. We used life expectancy, healthy life years, infant mortality, Gross Domestic Product (GDP) and GDP growth rate as a set of inputs. Results were dominated by GDP growth rate and GDP. Obtaining accurate estimates of air quality measures can help in deciding on distinct dimensions to be considered in multidimensional studies of welfare and quality of life.